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RSI foundation for outer-loop improvement of adaptation ability; distinguishes a learned initial condition from a self-modified learning algorithm.", "claim_ids": [ "role:b570_06_role_wf_c3cbe075f0eac379c3" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_06_src_wf_c3cbe075f0eac379c3", "s18ctx_role_src_36dcd0fd301066b1" ], "evidence_ids": [ "b570_06_ev_wf_c3cbe075f0eac379c3", "s18ctx_role_ev_wf_c3cbe075f0eac379c3" ], "evidence_lines": [ 461, 824 ], "review_depths": [ "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_06_ev_wf_c3cbe075f0eac379c3", "kind": "claim_packets", "url": "https://arxiv.org/pdf/1803.02999v1", "version": "arXiv:1803.02999v1", "locator": "PDF p2; derived text lines 20-39 (one-based local extraction, not web-tool lines)", "line_count": 20 }, { "evidence_id": "b570_06_ev_wf_c3cbe075f0eac379c3", "kind": "claim_packets", "url": "https://arxiv.org/pdf/1803.02999v1", "version": "arXiv:1803.02999v1", "locator": "PDF p6; derived text lines 46-55 (one-based local extraction, not web-tool lines)", "line_count": 10 }, { "evidence_id": "b570_06_ev_wf_c3cbe075f0eac379c3", "kind": "limit_packets", "url": "https://arxiv.org/pdf/1803.02999v1", "version": "arXiv:1803.02999v1", "locator": "PDF p10; derived text lines 10-22 (one-based local extraction, not web-tool lines)", "line_count": 13 }, { "evidence_id": "b570_06_ev_wf_c3cbe075f0eac379c3", "kind": "conflict_packets", "url": "https://arxiv.org/pdf/1803.02999v1", "version": "arXiv:1803.02999v1", "locator": "PDF p6; derived text lines 33-45 (one-based local extraction, not web-tool lines)", "line_count": 13 } ], "content_characters": 11251, "evaluation_details_available": true }, { "work_family_id": "wf_d2df1aa1dd44cb7791", "title": "Quantum Random Self-Modifiable Computation", "role": "R02_CONCEPT", "role_label": "\u6982\u5ff5/\u7406\u8bba/\u7acb\u573a", "year": 2018, "branch_id": "M0_definitions", "path": "papers/r02_concept/wf_d2df1aa1dd44cb7791.md", "summary": "An ex-machine meta instruction executes an ordinary step and then adds or replaces an instruction while maintaining a unique state/scanned-symbol rule; the model can also add states. Optional concept background for explicitly distinguishing growth of a machine instruction set under random/meta instructions from objective-driven capability improvement.", "claim_ids": [ "role:b570_01_role_1807_01369" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_01_src_1807_01369", "s18ctx_role_src_4a72693a250c423c" ], "evidence_ids": [ "b570_01_ev_1807_01369", "s18ctx_role_ev_wf_d2df1aa1dd44cb7791" ], "evidence_lines": [ 462, 727 ], "review_depths": [ "original_abstract", "selected_primary_theorem_assumptions_and_limits" ], "primary_passages": [], "content_characters": 3865, "evaluation_details_available": false }, { "work_family_id": "wf_9f2516ddeb83a94d17", "title": "Self-Modifying Morphology Experiments with DyRET: Dynamic Robot for Embodied Testing", "role": "R03_METHOD", "role_label": "\u65b9\u6cd5\u8bba\u6587", "year": 2018, "branch_id": "M9_embodied", "path": "papers/r03_method/wf_9f2516ddeb83a94d17.md", "summary": "DyRET exposes interchangeable ROS modules and adjustable legs. Experiments compare two morphologies with manually designed controllers under voltage/environment changes, using physical walking speed as outcome. Embodied RSI background separating substrate/morphology changes from learner/improvement-policy changes; supports morphology-control attribution.", "claim_ids": [ "role:b570_06_role_wf_9f2516ddeb83a94d17" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_06_src_wf_9f2516ddeb83a94d17", "s18_role_src_4a72693a250c423c" ], "evidence_ids": [ "b570_06_ev_wf_9f2516ddeb83a94d17", "s18_role_ev_wf_9f2516ddeb83a94d17" ], "evidence_lines": [ 397, 823 ], "review_depths": [ "original_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_06_ev_wf_9f2516ddeb83a94d17", "kind": "claim_packets", "url": "https://arxiv.org/pdf/1803.05629v1", "version": "arXiv:1803.05629v1", "locator": "PDF p4; derived text lines 69-98 (one-based local extraction, not web-tool lines)", "line_count": 30 }, { "evidence_id": "b570_06_ev_wf_9f2516ddeb83a94d17", "kind": "claim_packets", "url": "https://arxiv.org/pdf/1803.05629v1", "version": "arXiv:1803.05629v1", "locator": "PDF p5; 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Current survey selection decision: exclude this Seq2Seq training paper from technical synthesis for insufficient demonstrated RSI thematic connection; preserve its OT objective and training feedback. Fixed optimizers and ordinary supervised training can provide bounded feedback; optimizer rewriting is not required for feedback.", "claim_ids": [], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_01_src_1901_06283", "r3_abstract_src_src_7a001528abaea2f3" ], "evidence_ids": [ "b570_01_ev_1901_06283", "r3_abstract_ev_1901_06283" ], "evidence_lines": [ 286, 728 ], "review_depths": [ "selected_primary_scope_exclusion_supported_by_original_body" ], "primary_passages": [], "content_characters": 4177, "evaluation_details_available": false }, { "work_family_id": "wf_1976f81dac4156ac7b", "title": "J-MoDL: Joint Model-Based Deep Learning for Optimized Sampling and Reconstruction", "role": "R11_BACKGROUND", "role_label": "\u80cc\u666f/\u76f8\u90bb\u57fa\u7840", "year": 2019, "branch_id": "M9_multimodal", "path": "papers/r11_background/wf_1976f81dac4156ac7b.md", "summary": "J-MoDL jointly optimizes MRI sampling locations and CNN reconstruction parameters against a training-data reconstruction error objective. Current survey selection decision: exclude this MRI acquisition/reconstruction paper from technical synthesis for insufficient demonstrated RSI thematic connection; preserve its joint objective and bounded feedback. Optimizer rewriting is not a necessary condition for feedback.", "claim_ids": [], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_01_src_1911_02945", "r3_abstract_src_src_7a001528abaea2f3" ], "evidence_ids": [ "b570_01_ev_1911_02945", "r3_abstract_ev_1911_02945" ], "evidence_lines": [ 287, 729 ], "review_depths": [ "selected_primary_scope_exclusion_supported_by_original_body" ], "primary_passages": [], "content_characters": 3865, "evaluation_details_available": false }, { "work_family_id": "wf_d87231e3448eebf771", "title": "Nicolas de Condorcet and the First Intelligence Explosion Hypothesis", "role": "R12_IMPACT", "role_label": "\u6cbb\u7406/\u7ecf\u6d4e/\u793e\u4f1a\u5f71\u54cd", "year": 2019, "branch_id": "M8_impact", "path": "papers/r12_impact/wf_d87231e3448eebf771.md", "summary": "The article supplies historical and conceptual context for intelligence-explosion ideas through Condorcet's jury theorem and accelerating-progress worldview.", "claim_ids": [ "role:b570_26_role_wf_d87231e3448eebf771" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_26_src_wf_d87231e3448eebf771" ], "evidence_ids": [ "b570_26_ev_wf_d87231e3448eebf771" ], "evidence_lines": [ 1208 ], "review_depths": [ "original_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_26_ev_wf_d87231e3448eebf771", "kind": "claim_packets", "url": "https://onlinelibrary.wiley.com/doi/full/10.1609/aimag.v40i1.2855", "version": "AI Magazine 40(1), first published 2019-03-01", "locator": "Official publisher HTML via web-tool extraction, lines 65-69", "line_count": 3 }, { "evidence_id": "b570_26_ev_wf_d87231e3448eebf771", "kind": "claim_packets", "url": "https://onlinelibrary.wiley.com/doi/full/10.1609/aimag.v40i1.2855", "version": "AI Magazine 40(1), first published 2019-03-01", "locator": "Official publisher HTML via web-tool extraction, lines 75-78", "line_count": 3 }, { "evidence_id": "b570_26_ev_wf_d87231e3448eebf771", "kind": "claim_packets", "url": "https://onlinelibrary.wiley.com/doi/full/10.1609/aimag.v40i1.2855", "version": "AI Magazine 40(1), first published 2019-03-01", "locator": "Official publisher HTML via web-tool extraction, lines 83-84", "line_count": 2 }, { "evidence_id": "b570_26_ev_wf_d87231e3448eebf771", "kind": "limit_packets", "url": "https://onlinelibrary.wiley.com/doi/full/10.1609/aimag.v40i1.2855", "version": "AI Magazine 40(1), first published 2019-03-01", "locator": "Official publisher HTML via web-tool extraction, lines 101-101", "line_count": 1 }, { "evidence_id": "b570_26_ev_wf_d87231e3448eebf771", "kind": "limit_packets", "url": "https://onlinelibrary.wiley.com/doi/full/10.1609/aimag.v40i1.2855", "version": "AI Magazine 40(1), first published 2019-03-01", "locator": "Official publisher HTML via web-tool extraction, lines 109-111", "line_count": 2 } ], "content_characters": 6518, "evaluation_details_available": false }, { "work_family_id": "wf_0b9899d3cf1cc03ea0", "title": "Reachability Analysis of Self Modifying Code", "role": "R03_METHOD", "role_label": "\u65b9\u6cd5\u8bba\u6587", "year": 2019, "branch_id": "M8_safety", "path": "papers/r03_method/wf_0b9899d3cf1cc03ea0.md", "summary": "Defines SM-PDS configurations as control point, stack and active transition-rule phase, including rule-replacement transitions. The malware study compares malicious-entry reachability with and without self-modifying semantics on 13 modified examples. 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Its research object is formal proof/justification syntax. Current survey selection decision: exclude this justification-logic subject from technical synthesis for insufficient demonstrated RSI thematic connection, while retaining the author warning and definitions. This decision establishes no general rule against bounded feedback and does not require optimizer rewriting.", "claim_ids": [], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_01_src_1902_01106", "r3_abstract_src_src_4a72693a250c423c" ], "evidence_ids": [ "b570_01_ev_1902_01106", "r3_abstract_ev_1902_01106" ], "evidence_lines": [ 296, 730 ], "review_depths": [ "original_abstract", "selected_primary_scope_exclusion_supported_by_original_body" ], "primary_passages": [], "content_characters": 4132, "evaluation_details_available": false }, { "work_family_id": "wf_6dbd3c7d95ca4ffbe2", "title": "Adaptive Reinforcement Learning through Evolving Self-Modifying Neural Networks", "role": "R03_METHOD", "role_label": "\u65b9\u6cd5\u8bba\u6587", "year": 2020, "branch_id": "M4_weights", "path": "papers/r03_method/wf_6dbd3c7d95ca4ffbe2.md", "summary": 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"selected_primary_method_and_experiment_settings_20261016" ], "primary_passages": [], "content_characters": 5205, "evaluation_details_available": true }, { "work_family_id": "wf_75ecf36c956baffb0e", "title": "Deep Collective Learning: Learning Optimal Inputs and Weights Jointly in Deep Neural Networks", "role": "R11_BACKGROUND", "role_label": "\u80cc\u666f/\u76f8\u90bb\u57fa\u7840", "year": 2020, "branch_id": "M4_weights", "path": "papers/r11_background/wf_75ecf36c956baffb0e.md", "summary": "Lookup-VNets jointly train DNN weights W and color lookup-table parameters T through gradient descent on a task loss, with parameters combined as Theta=[W,T]. Current survey selection decision: exclude this Lookup-VNet training paper from technical synthesis for insufficient demonstrated RSI thematic connection; preserve its joint update facts and bounded feedback. Ordinary supervised training with a fixed optimizer can provide feedback; rewriting the optimizer is not required.", "claim_ids": [], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_01_src_2009_07988", "r3_abstract_src_src_7a001528abaea2f3" ], "evidence_ids": [ "b570_01_ev_2009_07988", "r3_abstract_ev_2009_07988" ], "evidence_lines": [ 285, 731 ], "review_depths": [ "selected_primary_scope_exclusion_supported_by_original_body" ], "primary_passages": [], "content_characters": 5126, "evaluation_details_available": false }, { "work_family_id": "wf_cb81866cbc782352e1", "title": "Formation and Management of Industry 5.0 by Systems with Artificial Intelligence and Technological Singularity", "role": "R12_IMPACT", "role_label": "\u6cbb\u7406/\u7ecf\u6d4e/\u793e\u4f1a\u5f71\u54cd", "year": 2020, "branch_id": "M8_impact", "path": "papers/r12_impact/wf_cb81866cbc782352e1.md", "summary": "\u73b0\u6709\u8bc1\u636e\u4e0d\u8db3\u4ee5\u5f62\u6210\u5177\u4f53\u5185\u5bb9\u89e3\u6790\uff1b\u4fdd\u7559\u8eab\u4efd\u548c\u5f85\u6838\u4f4d\u7f6e\u3002", "claim_ids": [], "configuration_ids": [], "relation_ids": [], "source_ids": [], "evidence_ids": [], "evidence_lines": [], "review_depths": [ "registry_abstract" ], "primary_passages": [], "content_characters": 1024, "evaluation_details_available": false }, { "work_family_id": "wf_522645adcb3ae434f5", "title": "Learning to Plan and Realize Separately for Open-Ended Dialogue Systems", "role": "R11_BACKGROUND", "role_label": "\u80cc\u666f/\u76f8\u90bb\u57fa\u7840", "year": 2020, "branch_id": "M2_reasoning", "path": "papers/r11_background/wf_522645adcb3ae434f5.md", "summary": "The dialogue system separately trains a response planner and a surface realizer; symbolic ask/framing labels provide silver supervision and GPT-2-based models generate plans and responses. Current survey selection decision: exclude this dialogue-generation paper from technical synthesis for insufficient demonstrated RSI thematic connection; retain its method facts and bounded training feedback. Separate supervised training and fixed optimizers can provide feedback without optimizer rewriting.", "claim_ids": [], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_01_src_2009_12506", "r3_abstract_src_src_d45253343eca569f" ], "evidence_ids": [ "b570_01_ev_2009_12506", "r3_abstract_ev_2009_12506" ], "evidence_lines": [ 288, 732 ], "review_depths": [ "selected_primary_scope_exclusion_supported_by_original_body" ], "primary_passages": [], "content_characters": 4152, "evaluation_details_available": false }, { "work_family_id": "wf_19faa92a90f72bbe59", "title": "NEED OF ARTIFICIAL INTELLIGENCE IN PRESENT ERA", "role": "R11_BACKGROUND", "role_label": "\u80cc\u666f/\u76f8\u90bb\u57fa\u7840", "year": 2020, "branch_id": "M0_definitions", "path": "papers/r11_background/wf_19faa92a90f72bbe59.md", "summary": "The article surveys generic benefits, applications, and social concerns of artificial intelligence.", "claim_ids": [], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_17_src_wf_19faa92a90f72bbe59" ], "evidence_ids": [ "b570_17_ev_wf_19faa92a90f72bbe59" ], "evidence_lines": [ 1042 ], "review_depths": [ "original_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_17_ev_wf_19faa92a90f72bbe59", "kind": "claim_packets", "url": "https://www.jetir.org/papers/JETIR2004483.pdf", "version": "captured source; no latest-version assertion", "locator": "PDF physical page 3, derived page-text lines 27-32 (one-based local extraction, not web-tool lines)", "line_count": 6 }, { "evidence_id": "b570_17_ev_wf_19faa92a90f72bbe59", "kind": "claim_packets", "url": "https://www.jetir.org/papers/JETIR2004483.pdf", "version": "captured source; no latest-version assertion", "locator": "PDF physical page 5, derived page-text lines 8-13 (one-based local extraction, not web-tool lines)", "line_count": 6 }, { "evidence_id": "b570_17_ev_wf_19faa92a90f72bbe59", "kind": "limit_packets", "url": "https://www.jetir.org/papers/JETIR2004483.pdf", "version": "captured source; no latest-version assertion", "locator": "PDF physical page 4, derived page-text lines 27-32 (one-based local extraction, not web-tool lines)", "line_count": 6 }, { "evidence_id": "b570_17_ev_wf_19faa92a90f72bbe59", "kind": "limit_packets", "url": "https://www.jetir.org/papers/JETIR2004483.pdf", "version": "captured source; no latest-version assertion", "locator": "PDF physical page 4, derived page-text lines 33-38 (one-based local extraction, not web-tool lines)", "line_count": 6 } ], "content_characters": 6039, "evaluation_details_available": false }, { "work_family_id": "wf_4e38f0690ed9b4d9f3", "title": "Performance of Bounded-Rational Agents With the Ability to Self-Modify", "role": "R02_CONCEPT", "role_label": "\u6982\u5ff5/\u7406\u8bba/\u7acb\u573a", "year": 2020, "branch_id": "M8_safety", "path": "papers/r02_concept/wf_4e38f0690ed9b4d9f3.md", "summary": "With modification-independent belief/utility and epsilon-optimality at the empty history, Theorem 7 bounds expected future loss by min(epsilon/gamma^(t\u22121),1/(1\u2212gamma)); the bound is tight up to a factor gamma. Direct theoretical limitation context for how bounded optimization and discounting can undermine goal preservation during self-modification.", "claim_ids": [ "role:b570_01_role_2011_06275" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_01_src_2011_06275", "s18_role_src_4a72693a250c423c" ], "evidence_ids": [ "b570_01_ev_2011_06275", "s18_role_ev_wf_4e38f0690ed9b4d9f3" ], "evidence_lines": [ 399, 733 ], "review_depths": [ "original_abstract", "selected_primary_theorem_assumptions_and_limits" ], "primary_passages": [], "content_characters": 3853, "evaluation_details_available": false }, { "work_family_id": "wf_4e25b11088e125402d", "title": "Reflexive Reinforcement Learning: Methods for Self-Referential Autonomous Learning", "role": "R03_METHOD", "role_label": "\u65b9\u6cd5\u8bba\u6587", "year": 2020, "branch_id": "M0_historical", "path": "papers/r03_method/wf_4e25b11088e125402d.md", "summary": "RRL defines reflexive rewards from the learner's state/value/environment representations when meaningful external task reward is absent. Its illustrative agent uses fixed entropy, empowerment or prediction-error-related maxims; the algorithm switches between reflexive and task-related policies and updates their parameters from observed interaction. RSI-component bridge: internal learned representations feed future learning rewards and policies. This is a self-referential learning-feedback mechanism with fixed maxims, not evidence of open-ended recursive optimizer improvement.", "claim_ids": [ "role:b570_08_role_wf_4e25b11088e125402d" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_08_src_wf_4e25b11088e125402d" ], "evidence_ids": [ "b570_08_ev_wf_4e25b11088e125402d" ], "evidence_lines": [ 877 ], "review_depths": [ "original_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_08_ev_wf_4e25b11088e125402d", "kind": "claim_packets", "url": "https://www.scitepress.org/Papers/2020/99975/99975.pdf", "version": "IJCCI 2020 publisher PDF", "locator": "PDF physical page 3, derived page-text lines 27-42 (one-based local extraction, not web-tool lines)", "line_count": 16 }, { "evidence_id": "b570_08_ev_wf_4e25b11088e125402d", "kind": "claim_packets", "url": "https://www.scitepress.org/Papers/2020/99975/99975.pdf", "version": "IJCCI 2020 publisher 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"selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_15_ev_wf_77c330660409e4b955", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2008.11535v1", "version": "2008.11535v1", "locator": "PDF physical page 5, derived page-text lines 9-14 (one-based local extraction, not web-tool lines)", "line_count": 6 }, { "evidence_id": "b570_15_ev_wf_77c330660409e4b955", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2008.11535v1", "version": "2008.11535v1", "locator": "PDF physical page 21, derived page-text lines 28-33 (one-based local extraction, not web-tool lines)", "line_count": 6 }, { "evidence_id": "b570_15_ev_wf_77c330660409e4b955", "kind": "limit_packets", "url": "https://arxiv.org/pdf/2008.11535v1", "version": "2008.11535v1", "locator": "PDF physical page 1, derived page-text lines 17-22 (one-based local extraction, not web-tool lines)", "line_count": 6 } ], "content_characters": 6036, "evaluation_details_available": false }, { "work_family_id": "wf_c7bed965fa64fb5fc2", "title": "Survivable Hyper-Redundant Robotic Arm with Bayesian Policy Morphing", "role": "R03_METHOD", "role_label": "\u65b9\u6cd5\u8bba\u6587", "year": 2020, "branch_id": "M9_embodied", "path": "papers/r03_method/wf_c7bed965fa64fb5fc2.md", "summary": "BPM combines a fixed critic, behavior ensemble and Bayesian bias proposal/filtering to update a successor motion policy after mechanical faults; evaluates pose-reaching and learning convergence of an 8-DOF arm. Embodied RSI background for feedback-based repair and acceptance of policy updates after damage, distinct from recursively modifying the adaptation algorithm.", "claim_ids": [ "role:b570_06_role_wf_c7bed965fa64fb5fc2" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_06_src_wf_c7bed965fa64fb5fc2", "s18ctx_role_src_3efaf054ef1c94af" ], "evidence_ids": [ "b570_06_ev_wf_c7bed965fa64fb5fc2", "s18ctx_role_ev_wf_c7bed965fa64fb5fc2" ], "evidence_lines": [ 464, 826 ], "review_depths": [ "original_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_06_ev_wf_c7bed965fa64fb5fc2", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2010.09964v1", "version": "arXiv:2010.09964v1", "locator": "PDF p3; derived text lines 19-66 (one-based local extraction, not web-tool lines)", "line_count": 48 }, { "evidence_id": "b570_06_ev_wf_c7bed965fa64fb5fc2", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2010.09964v1", "version": "arXiv:2010.09964v1", "locator": "PDF p5; derived text lines 13-32 (one-based local extraction, not web-tool lines)", "line_count": 20 }, { "evidence_id": "b570_06_ev_wf_c7bed965fa64fb5fc2", "kind": "limit_packets", "url": "https://arxiv.org/pdf/2010.09964v1", "version": "arXiv:2010.09964v1", "locator": "PDF p5; derived text lines 74-82 (one-based local extraction, not web-tool lines)", "line_count": 9 }, { "evidence_id": "b570_06_ev_wf_c7bed965fa64fb5fc2", "kind": "conflict_packets", "url": "https://arxiv.org/pdf/2010.09964v1", "version": "arXiv:2010.09964v1", "locator": "PDF p2; derived text lines 40-48 (one-based local extraction, not web-tool lines)", "line_count": 9 }, { "evidence_id": "b570_06_ev_wf_c7bed965fa64fb5fc2", "kind": "conflict_packets", "url": "https://arxiv.org/pdf/2010.09964v1", "version": "arXiv:2010.09964v1", "locator": "PDF p5; derived text lines 83-90 (one-based local extraction, not web-tool lines)", "line_count": 8 } ], "content_characters": 12667, "evaluation_details_available": true }, { "work_family_id": "wf_cefa5df0ccf9f3455f", "title": "The computational inevitability of life: self-replication under resource-bounded nested algorithmic probability", "role": "R02_CONCEPT", "role_label": "\u6982\u5ff5/\u7406\u8bba/\u7acb\u573a", "year": 2020, "branch_id": "M0_definitions", "path": "papers/r02_concept/wf_cefa5df0ccf9f3455f.md", "summary": "The reviewed v1 nests program-output distributions across levels of the same fixed-length Turing-machine model, feeding the output-derived probability distribution back as program weighting. 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RSI background for learned-feedback bounded adaptation and evaluator blind spots; fixed segmentor/discriminator components do not negate real adaptor updates.", "claim_ids": [ "role:b570_06_role_wf_e836f60dc8f8943c46" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_06_src_wf_e836f60dc8f8943c46", "s18ctx_role_src_60233c3ce967e6d7" ], "evidence_ids": [ "b570_06_ev_wf_e836f60dc8f8943c46", "s18ctx_role_ev_wf_e836f60dc8f8943c46" ], "evidence_lines": [ 465, 827 ], "review_depths": [ "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_06_ev_wf_e836f60dc8f8943c46", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2108.12280v1", "version": "arXiv:2108.12280v1", "locator": "PDF p3; derived text lines 43-52 (one-based local extraction, not web-tool lines)", "line_count": 10 }, { "evidence_id": "b570_06_ev_wf_e836f60dc8f8943c46", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2108.12280v1", "version": "arXiv:2108.12280v1", "locator": "PDF p7; 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"relation_ids": [], "source_ids": [ "h6_src_w_acl_srwm", "s18_role_src_f856fccb78b754f3" ], "evidence_ids": [ "h6_ev_cfg_acl_srwm_meta_test_continual", "s18_role_ev_wf_5d7361f404c0455d9f" ], "evidence_lines": [ 427, 521 ], "review_depths": [ "original_abstract", "targeted_primary_method_state_feedback_limits_20261018" ], "primary_passages": [], "content_characters": 4991, "evaluation_details_available": true }, { "work_family_id": "wf_title_5c2491c7f6c82e5c", "title": "Practical Computational Power of Linear Transformers and Their Recurrent and Self-Referential Extensions", "role": "R02_CONCEPT", "role_label": "\u6982\u5ff5/\u7406\u8bba/\u7acb\u573a", "year": 2023, "branch_id": "M0_historical", "path": "papers/r02_concept/wf_title_5c2491c7f6c82e5c.md", "summary": "The paper defines a self-referential weight matrix that updates its own active weight matrix at each step and evaluates recurrent and self-referential linear-transformer extensions on formal-language recognition tasks.", 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(one-based local extraction, not web-tool lines)", "line_count": 22 }, { "evidence_id": "b570_4_ev_wf_title_5c2491c7f6c82e5c", "kind": "limit_packets", "url": "https://aclanthology.org/2023.emnlp-main.588.pdf", "version": "captured source; no latest-version assertion", "locator": "PDF physical page 1, derived page-text lines 38-43 (one-based local extraction, not web-tool lines)", "line_count": 6 }, { "evidence_id": "b570_4_ev_wf_title_5c2491c7f6c82e5c", "kind": "limit_packets", "url": "https://aclanthology.org/2023.emnlp-main.588.pdf", "version": "captured source; no latest-version assertion", "locator": "PDF physical page 3, derived page-text lines 65-91 (one-based local extraction, not web-tool lines)", "line_count": 27 } ], "content_characters": 8632, "evaluation_details_available": false }, { "work_family_id": "wf_title_85954f3817cf3610", "title": "Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution", "role": "R03_METHOD", "role_label": 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Admit only bibliographic scope disposition, not technical claims for the RSI survey.", "claim_ids": [], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_08_src_wf_title_42db5bd11f5ad214" ], "evidence_ids": [ "b570_08_ev_wf_title_42db5bd11f5ad214" ], "evidence_lines": [ 879 ], "review_depths": [ "original_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_08_ev_wf_title_42db5bd11f5ad214", "kind": "claim_packets", "url": "https://aclanthology.org/2024.acl-long.528.pdf", "version": "ACL 2024 long paper 528 publisher PDF", "locator": "PDF physical page 3, derived page-text lines 57-75 (one-based local extraction, not web-tool lines)", "line_count": 19 }, { "evidence_id": "b570_08_ev_wf_title_42db5bd11f5ad214", "kind": "claim_packets", "url": "https://aclanthology.org/2024.acl-long.528.pdf", "version": "ACL 2024 long paper 528 publisher PDF", "locator": "PDF physical page 4, derived page-text lines 45-58 (one-based local extraction, not web-tool lines)", "line_count": 14 }, { "evidence_id": "b570_08_ev_wf_title_42db5bd11f5ad214", "kind": "limit_packets", "url": "https://aclanthology.org/2024.acl-long.528.pdf", "version": "ACL 2024 long paper 528 publisher PDF", "locator": "PDF physical page 9, derived page-text lines 28-40 (one-based local extraction, not web-tool lines)", "line_count": 13 } ], "content_characters": 7912, "evaluation_details_available": true }, { "work_family_id": "wf_52c244d3fdd75d0080", "title": "Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models", "role": "R03_METHOD", "role_label": "\u65b9\u6cd5\u8bba\u6587", "year": 2024, "branch_id": "M4_alignment", "path": "papers/r03_method/wf_52c244d3fdd75d0080.md", "summary": "\u5728\u5f53\u524d\u5df2\u6838\u914d\u7f6e\u4e2d\uff0c\u7cfb\u7edf\u8fb9\u754c\u4e3a\uff1a\u65b0\u65e7\u8f6e\u6b21\u7b56\u7565\u53ca\u81ea\u751f\u6210\u5bf9\u624b\u54cd\u5e94\uff1b\u5177\u4f53\u66f4\u65b0\u8fc7\u7a0b\u4e3a\uff1a\u4e0a\u8f6eLLM\u4ea7\u751f\u5bf9\u624b\u54cd\u5e94\uff0c\u5f53\u524d\u8f6e\u8bad\u7ec3\u540e\u590d\u5236\u53c2\u6570\u6210\u4e3a\u4e0b\u4e00\u8f6e\u5bf9\u624b\uff1b\u66f4\u65b0\u72b6\u6001\u7684\u4fdd\u7559\u4e0e\u540e\u7ee7\u4f7f\u7528\u4e3a\uff1a\u8de8\u81ea\u535a\u5f08\u5fae\u8c03\u8f6e\u6b21\u3002", "claim_ids": [ "configuration:cfg_spin" ], "configuration_ids": [ "cfg_spin" ], "relation_ids": [], "source_ids": [ "expand_src_w_self_rewarding", "expand_src_w_spin", "stage1_src_w_rise" ], "evidence_ids": [ "e2_w_spin", "e3_training_p2b1_rise_spin", "e3_training_p2c_spin_selfrewarding_boundary" ], "evidence_lines": [ 125, 207, 208 ], "review_depths": [ "original_abstract", "selected_method_passages_with_abstract_baseline" ], "primary_passages": [], "content_characters": 3046, "evaluation_details_available": false }, { "work_family_id": "wf_35a65d9aba7dd5b532", "title": "Self-Rewarding Language Models", "role": "R03_METHOD", "role_label": "\u65b9\u6cd5\u8bba\u6587", "year": 2024, "branch_id": "M1_reward", "path": "papers/r03_method/wf_35a65d9aba7dd5b532.md", "summary": "\u5728\u5f53\u524d\u5df2\u6838\u914d\u7f6e\u4e2d\uff0c\u7cfb\u7edf\u8fb9\u754c\u4e3a\uff1a\u751f\u6210\u56de\u7b54\u548c\u81ea\u8bc4\u7684\u540c\u4e00\u6a21\u578b\uff0b\u504f\u597d\u8bad\u7ec3\u6570\u636e\uff1b\u5177\u4f53\u66f4\u65b0\u8fc7\u7a0b\u4e3a\uff1aM2\u7531M1\u81ea\u8bc4\u5206\u504f\u597d\u8bad\u7ec3\uff0cM3\u7531M2\u751f\u6210\u504f\u597d\u7ee7\u7eed\u8bad\u7ec3\uff1b\u66f4\u65b0\u6a21\u578b\u7ee7\u7eed\u627f\u62c5\u751f\u6210\u548c\u8bc4\u4ef7\uff1b\u66f4\u65b0\u72b6\u6001\u7684\u4fdd\u7559\u4e0e\u540e\u7ee7\u4f7f\u7528\u4e3a\uff1a\u8de8DPO\u8bad\u7ec3\u8f6e\u6b21\u3002", "claim_ids": [ "configuration:cfg_self_rewarding" ], "configuration_ids": [ "cfg_self_rewarding" ], "relation_ids": [ "r2_meta_rewarding_self_rewarding_1" ], "source_ids": [ "expand_src_w_meta_rewarding", "expand_src_w_self_rewarding", "expand_src_w_spin" ], "evidence_ids": [ "e2_w_self_rewarding", "e3_training_p2c_spin_selfrewarding_boundary", "e3_training_r2_meta_rewarding_self_rewarding_1" ], "evidence_lines": [ 113, 204, 208 ], "review_depths": [ "original_abstract", "selected_method_passages_with_abstract_baseline" ], "primary_passages": [], "content_characters": 4262, "evaluation_details_available": false }, { "work_family_id": "wf_fc02e232f119a6f60b", "title": "Smaller Large Language Models Can Do Moral Self-Correction", "role": "R07_EMPIRICAL", "role_label": "\u5b9e\u8bc1/\u5ba1\u8ba1/\u590d\u73b0", "year": 2024, "branch_id": "M4_selftraining", "path": "papers/r07_empirical/wf_fc02e232f119a6f60b.md", "summary": "Moral self-correction is evaluated by prompted output changes on Winogender and ambiguous BBQ bias tasks. Winogender fairness counts they/their/them prefixes; BBQ requires the unknown/cannot-be-determined answer. Instruction specificity and negation compare behavior, and the most specific instruction contains the desired answer. Optional feedback/prompt-evaluation background; local moral response correction requires an explicit bridge before use in RSI capability claims.", "claim_ids": [ "role:b570_02_role_wf_fc02e232f119a6f60b" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_02_src_wf_fc02e232f119a6f60b" ], "evidence_ids": [ "b570_02_ev_wf_fc02e232f119a6f60b" ], "evidence_lines": [ 751 ], "review_depths": [ "original_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_02_ev_wf_fc02e232f119a6f60b", "kind": "claim_packets", "url": "https://arxiv.org/html/2410.23496v1", "version": "2410.23496v1", "locator": "tool L53\u201378", "line_count": 23 }, { "evidence_id": "b570_02_ev_wf_fc02e232f119a6f60b", "kind": "claim_packets", "url": "https://arxiv.org/html/2410.23496v1", "version": "2410.23496v1", "locator": "tool L93\u201395", "line_count": 3 }, { "evidence_id": "b570_02_ev_wf_fc02e232f119a6f60b", "kind": "limit_packets", 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Its Python experiments compare direct analysis with translation to DPNs, and its application is malware detection. Runtime code mutation is the research object; the reviewed work supplies no capability-improving agent succession protocol. Background bridge: the SM-DPN abstraction and single-indexed LTL protocol provide a formal-verification reference for code that an RSI system may rewrite. Use only with this explicit bridge and the stated abstraction boundaries; this is not empirical evidence of agent capability improvement.", "claim_ids": [ "role:b570_04_role_wf_e89fb495d022538fd1" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_04_src_wf_e89fb495d022538fd1" ], "evidence_ids": [ "b570_04_ev_wf_e89fb495d022538fd1" ], "evidence_lines": [ 783 ], "review_depths": [ "original_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_04_ev_wf_e89fb495d022538fd1", "kind": "claim_packets", "url": "https://www.irif.fr/~zhangeldinov/papers/ltl_mc_smdpn.pdf", "version": "author-hosted unversioned manuscript, retrieved 2026-10-19", "locator": "PDF p1; derived text lines 9-18 (one-based local extraction, not web-tool lines)", "line_count": 10 }, { "evidence_id": "b570_04_ev_wf_e89fb495d022538fd1", "kind": "claim_packets", "url": "https://www.irif.fr/~zhangeldinov/papers/ltl_mc_smdpn.pdf", "version": "author-hosted unversioned manuscript, retrieved 2026-10-19", "locator": "PDF p17; derived text lines 3-15 (one-based local extraction, not web-tool lines)", "line_count": 13 }, { "evidence_id": "b570_04_ev_wf_e89fb495d022538fd1", "kind": "limit_packets", "url": "https://www.irif.fr/~zhangeldinov/papers/ltl_mc_smdpn.pdf", "version": "author-hosted unversioned manuscript, retrieved 2026-10-19", "locator": "PDF p19; derived text lines 18-29 (one-based local extraction, not web-tool lines)", "line_count": 12 } ], "content_characters": 8241, "evaluation_details_available": false }, { "work_family_id": "wf_b237b657c311c85eb1", "title": "Large Language Models Report Subjective Experience Under Self-Referential Processing", "role": "R07_EMPIRICAL", "role_label": "\u5b9e\u8bc1/\u5ba1\u8ba1/\u590d\u73b0", "year": 2025, "branch_id": "M8_failure", "path": "papers/r07_empirical/wf_b237b657c311c85eb1.md", "summary": "Experiment1 tests first-person experience reports after self-referential prompting versus history, conceptual and zero-shot controls. Each of7 models has50 trials per condition; an LLM judge labels only the final query-response pair, not the whole dialogue. Original-body topical exclusion; self-report under a feedback prompt does not by itself furnish the survey's RSI improvement evidence.", "claim_ids": [], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_02_src_wf_b237b657c311c85eb1", "r4_role_src_4a72693a250c423c" ], "evidence_ids": [ "b570_02_ev_wf_b237b657c311c85eb1", "r4_role_ev_wf_b237b657c311c85eb1" ], "evidence_lines": [ 375, 753 ], "review_depths": [ "original_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_02_ev_wf_b237b657c311c85eb1", "kind": "claim_packets", "url": "https://arxiv.org/html/2510.24797v2", "version": "2510.24797v2", "locator": "tool L100\u2013113", "line_count": 14 }, { "evidence_id": "b570_02_ev_wf_b237b657c311c85eb1", "kind": "limit_packets", "url": "https://arxiv.org/html/2510.24797v2", "version": "2510.24797v2", "locator": "tool L212\u2013222", "line_count": 11 } ], "content_characters": 11039, 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Main evaluation measures visual QA/reasoning benchmark accuracy. Bounded self-reward feedback relevant to RSI signal design; frozen tools and optional validation labels are explicit. No sustained multi-generation acceleration follows.", "claim_ids": [ "role:b570_06_role_wf_1105bf914fb53437b7" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_06_src_wf_1105bf914fb53437b7" ], "evidence_ids": [ "b570_06_ev_wf_1105bf914fb53437b7" ], "evidence_lines": [ 832 ], "review_depths": [ "original_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_06_ev_wf_1105bf914fb53437b7", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2512.22545v1", "version": "arXiv:2512.22545v1", "locator": "PDF p3; derived text lines 60-75 (one-based local extraction, not web-tool lines)", "line_count": 16 }, { "evidence_id": "b570_06_ev_wf_1105bf914fb53437b7", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2512.22545v1", "version": "arXiv:2512.22545v1", "locator": "PDF p5; derived text lines 43-67 (one-based local extraction, not web-tool lines)", "line_count": 25 }, { "evidence_id": "b570_06_ev_wf_1105bf914fb53437b7", "kind": "limit_packets", "url": "https://arxiv.org/pdf/2512.22545v1", "version": "arXiv:2512.22545v1", "locator": "PDF p4; derived text lines 59-65 (one-based local extraction, not web-tool lines)", "line_count": 7 }, { "evidence_id": "b570_06_ev_wf_1105bf914fb53437b7", "kind": "conflict_packets", "url": "https://arxiv.org/pdf/2512.22545v1", "version": "arXiv:2512.22545v1", "locator": "PDF p4; derived text lines 15-38 (one-based local extraction, not web-tool lines)", "line_count": 24 }, { "evidence_id": "b570_06_ev_wf_1105bf914fb53437b7", "kind": "conflict_packets", "url": "https://arxiv.org/pdf/2512.22545v1", "version": "arXiv:2512.22545v1", "locator": "PDF p7; derived text lines 55-65 (one-based local extraction, not web-tool lines)", "line_count": 11 } ], "content_characters": 10886, "evaluation_details_available": true }, { "work_family_id": "wf_b338af2fbaa49494ca", "title": "Self-Transcendence: Achieving AGI via Chaotic Dynamics and Thermodynamic Attractors", "role": "R03_METHOD", "role_label": "\u65b9\u6cd5\u8bba\u6587", "year": 2025, "branch_id": "M0_historical", "path": "papers/r03_method/wf_b338af2fbaa49494ca.md", "summary": "Self-Transcendence is a concrete but proposed architecture-level RSI mechanism centered on internal self-modeling, bounded criticality feedback, and falsifiable dynamical predictions.", "claim_ids": [ "role:b570_31_role_wf_b338af2fbaa49494ca" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_31_src_wf_b338af2fbaa49494ca" ], "evidence_ids": [ "b570_31_ev_wf_b338af2fbaa49494ca" ], "evidence_lines": [ 1230 ], "review_depths": [ "registry_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_31_ev_wf_b338af2fbaa49494ca", "kind": "claim_packets", "url": "https://d197for5662m48.cloudfront.net/documents/publicationstatus/290812/preprint_pdf/e22c30342e55df6f7b26dc89ab9fe41a.pdf", "version": "TechRxiv preprint v1, posted 2025-11-14, DOI 10.36227/techrxiv.176316092.26752975/v1", "locator": "Official publisher HTML via web-tool extraction, lines 1-4", "line_count": 4 }, { "evidence_id": "b570_31_ev_wf_b338af2fbaa49494ca", "kind": "limit_packets", "url": "https://d197for5662m48.cloudfront.net/documents/publicationstatus/290812/preprint_pdf/e22c30342e55df6f7b26dc89ab9fe41a.pdf", "version": "TechRxiv preprint v1, posted 2025-11-14, DOI 10.36227/techrxiv.176316092.26752975/v1", "locator": "Official publisher HTML via web-tool extraction, lines 5-6", "line_count": 2 } ], "content_characters": 5392, "evaluation_details_available": false }, { "work_family_id": "wf_baac8a4808a75ce83f", "title": "Self-evolving cognitive substrates through metabolic data processing and recursive self-representation with autonomous memory prioritization mechanisms", "role": "R02_CONCEPT", "role_label": "\u6982\u5ff5/\u7406\u8bba/\u7acb\u573a", "year": 2025, "branch_id": "M2_memory", "path": "papers/r02_concept/wf_baac8a4808a75ce83f.md", "summary": "The cognitive-substrate proposal combines local metabolic processing, recursive global self-representation, structural constraints and autonomous memory prioritization. Its described hybrid control passes performance information upward and architectural constraints downward, aiming to coordinate local structural adaptation without a separate training/inference phase. Explicit RSI-concept bridge: internal self-representation and performance feedback are proposed to govern later architectural and memory changes. 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This qualitative interview study invited 182 researchers and interviewed 25 via literature, workshop and network/snowball recruitment in August\u2013September 2025 (40\u201360 minutes). First-author inductive and separate AI-assisted categorical coding characterize automated-R&D beliefs, deployment, constraints and governance. 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separates target, self-reference, ownership, grounding and improvement evidence. Reliable horizon is a protocol-conditioned region over non-interchangeable task-pressure axes requiring repeated observed trials. 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Its guarded loop compares updated checkpoints on several heuristic quality metrics and rolls back unacceptable changes. Direct bounded model-update bridge: outputs and evaluation feedback control later weight/checkpoint state. Fixed metrics/training procedure constrain rather than erase the self-improvement loop.", "claim_ids": [ "role:b570_09_role_wf_e2da89c6509cd44c47" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_09_src_wf_e2da89c6509cd44c47" ], "evidence_ids": [ "b570_09_ev_wf_e2da89c6509cd44c47" ], "evidence_lines": [ 900 ], "review_depths": [ "registry_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_09_ev_wf_e2da89c6509cd44c47", "kind": "claim_packets", "url": "https://zenodo.org/api/records/18321445/files/ARC_Technical_Report_Zenodo-2.pdf/content", "version": "Zenodo 18321445", "locator": "PDF physical page 7, derived page-text lines 11-19 (one-based local extraction, not web-tool lines)", "line_count": 9 }, { "evidence_id": "b570_09_ev_wf_e2da89c6509cd44c47", "kind": "claim_packets", "url": "https://zenodo.org/api/records/18321445/files/ARC_Technical_Report_Zenodo-2.pdf/content", "version": "Zenodo 18321445", "locator": "PDF physical page 8, derived page-text lines 38-43 (one-based local extraction, not web-tool lines)", "line_count": 6 }, { "evidence_id": "b570_09_ev_wf_e2da89c6509cd44c47", "kind": "limit_packets", "url": "https://zenodo.org/api/records/18321445/files/ARC_Technical_Report_Zenodo-2.pdf/content", "version": "Zenodo 18321445", "locator": "PDF physical page 12, derived page-text lines 15-30 (one-based local extraction, not web-tool lines)", "line_count": 16 } ], "content_characters": 8344, "evaluation_details_available": true }, { "work_family_id": "wf_09087dd03b3d3e2d6b", "title": "ASC-EAM-001: Entangled Agent Mesh \u2014 Self-Improving Multi-Agent \u2014 E8 Intelligence Research", "role": "R02_CONCEPT", "role_label": "\u6982\u5ff5/\u7406\u8bba/\u7acb\u573a", "year": 2026, "branch_id": "M3_harness", "path": "papers/r02_concept/wf_09087dd03b3d3e2d6b.md", "summary": "The short E8 Intelligence note describes five agents sharing a knowledge mesh, a three-role hunt/remember/execute-and-validate arrangement, a timestamped evolution ledger and periodic self-healing. It separately outlines an ingest/geometric-conversion/compression/extraction pipeline. Explicit conceptual RSI bridge: learned/shared knowledge, repair/validation and a change ledger are intended to inform later agent versions. 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Standing assumptions include visible finite routes, receipt-bindable action interfaces, bounded recovery and a separate self-edit route. Direct formal assurance architecture for RSI proposals and promotion; no executed evaluation, unconditional safety guarantee or deployed-system certification.", "claim_ids": [ "role:b570_02_role_wf_0beae57e1f0d966105" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_02_src_wf_0beae57e1f0d966105" ], "evidence_ids": [ "b570_02_ev_wf_0beae57e1f0d966105" ], "evidence_lines": [ 738 ], "review_depths": [ "registry_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_02_ev_wf_0beae57e1f0d966105", "kind": "claim_packets", "url": "https://zenodo.org/api/records/20122776/files/Assured_Intelligence_Systems.pdf/content", "version": "Zenodo version DOI 10.5281/zenodo.20122776; metadata version 2", "locator": "derived L1377\u20131407", "line_count": 31 }, { "evidence_id": "b570_02_ev_wf_0beae57e1f0d966105", "kind": "claim_packets", "url": "https://zenodo.org/api/records/20122776/files/Assured_Intelligence_Systems.pdf/content", "version": "Zenodo version DOI 10.5281/zenodo.20122776; metadata version 2", "locator": "derived L2071\u20132085", "line_count": 15 }, { "evidence_id": "b570_02_ev_wf_0beae57e1f0d966105", "kind": "limit_packets", "url": "https://zenodo.org/api/records/20122776/files/Assured_Intelligence_Systems.pdf/content", "version": "Zenodo version DOI 10.5281/zenodo.20122776; metadata version 2", "locator": "derived L5305\u20135342", "line_count": 38 } ], "content_characters": 12165, "evaluation_details_available": false }, { "work_family_id": "wf_c1155d6197cc62bf78", "title": "Audit the Scaffold, Not the Checkpoint: A Stationarity Dichotomy for Recursive Self-Improvement in Agentic Coding", "role": "R08_LIMITATION", "role_label": "\u5931\u8d25/\u9650\u5236/\u98ce\u9669", "year": 2026, "branch_id": "M8_failure", "path": "papers/r08_limitation/wf_c1155d6197cc62bf78.md", "summary": "\u4ee5\u6709\u754c\u8d28\u91cf\u4e0e\u6301\u7eed\u6539\u8fdb\u5047\u8bbe\u5206\u6790\u9971\u548c\uff0c\u51bb\u7ed3\u6743\u91cd\u4e0a\u754c\u8fdb\u4e00\u6b65\u4f9d\u8d56M1/M2\uff1bscaffold\u53ef\u53d8\u4e0d\u7b49\u4e8e\u5df2\u8bc1\u660e\u65e0\u754c\u589e\u76ca\u3002Agentic Boosting\u4fdd\u7559/\u91cd\u52a0\u6743\u534f\u8bae\u660e\u786e\u5c1a\u672a\u6784\u5efa\u3002", "claim_ids": [ "role:r4safety_role_audit_scaffold" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "r4safety_src_audit_scaffold", "r4safety_src_audit_unbuilt" ], "evidence_ids": [ "r4safety_ev_audit_scaffold" ], "evidence_lines": [ 391 ], "review_depths": [ "original_abstract" ], "primary_passages": [], "content_characters": 2126, "evaluation_details_available": false }, { "work_family_id": "wf_5b401b695ec51bf1f3", "title": "Auditable Self-Improvement on One Commodity Host", "role": "R06_EVALUATION", "role_label": "\u8bc4\u6d4b\u65b9\u6cd5/\u6d4b\u91cf\u79d1\u5b66", "year": 2026, "branch_id": "M7_measurement", "path": "papers/r06_evaluation/wf_5b401b695ec51bf1f3.md", "summary": "The single-host report describes a frozen recall evaluator rejecting three proposed retrieval upgrades. Its first self-authored brief misdiagnoses stale telemetry; architect/human review catches the error before merge. The corrected change is reported shipped, while production merge remains human-gated. Direct narrow self-improvement governance case with valuable negative results; do not upgrade propose-only/human-corrected operation into fully autonomous RSI.", "claim_ids": [ "role:b570_02_role_wf_5b401b695ec51bf1f3" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_02_src_wf_5b401b695ec51bf1f3" ], "evidence_ids": [ "b570_02_ev_wf_5b401b695ec51bf1f3" ], "evidence_lines": [ 743 ], "review_depths": [ "registry_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_02_ev_wf_5b401b695ec51bf1f3", "kind": "claim_packets", "url": "https://zenodo.org/api/records/20749065/files/Auditable-Self-Improvement-on-One-Commodity-Host.pdf/content", "version": "Zenodo version DOI 10.5281/zenodo.20749065; metadata version 1.0", "locator": "derived L586\u2013630", "line_count": 45 }, { "evidence_id": "b570_02_ev_wf_5b401b695ec51bf1f3", "kind": "claim_packets", "url": "https://zenodo.org/api/records/20749065/files/Auditable-Self-Improvement-on-One-Commodity-Host.pdf/content", "version": "Zenodo version DOI 10.5281/zenodo.20749065; metadata version 1.0", "locator": "derived L661\u2013685", "line_count": 25 }, { "evidence_id": "b570_02_ev_wf_5b401b695ec51bf1f3", "kind": "limit_packets", "url": "https://zenodo.org/api/records/20749065/files/Auditable-Self-Improvement-on-One-Commodity-Host.pdf/content", "version": "Zenodo version DOI 10.5281/zenodo.20749065; metadata version 1.0", "locator": "derived L687\u2013719", "line_count": 33 } ], "content_characters": 14042, "evaluation_details_available": false }, { "work_family_id": "wf_507cd20b891f2c6f48", "title": "Auditing Coding-Agent Rule Updates under Selective Feedback", "role": "R06_EVALUATION", "role_label": "\u8bc4\u6d4b\u65b9\u6cd5/\u6d4b\u91cf\u79d1\u5b66", "year": 2026, "branch_id": "M7_measurement", "path": "papers/r06_evaluation/wf_507cd20b891f2c6f48.md", "summary": "The audit prototype computes sharp feasible FPR/recall bounds over all binary completions of unknown labels in a declared fixed pool, returning PASS/REJECT/DEFER. Candidate hashes, policy epochs and scope bind activation receipts. Its two-epoch micro pilot freezes model proposals before calibration and separates policy-derived oracle labels from candidate firing. Direct evidence/admission governance for coding-agent rule updates; only the selected finite-pool and micro-pilot claims are admitted.", "claim_ids": [ "role:b570_02_role_wf_507cd20b891f2c6f48" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_02_src_wf_507cd20b891f2c6f48" ], "evidence_ids": [ "b570_02_ev_wf_507cd20b891f2c6f48" ], "evidence_lines": [ 740 ], "review_depths": [ "registry_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_02_ev_wf_507cd20b891f2c6f48", "kind": "claim_packets", "url": "https://zenodo.org/api/records/23234126/files/policy_repository_working_paper_v08.pdf/content", "version": "Zenodo version DOI 10.5281/zenodo.23234126; metadata version v0.8", "locator": "derived L194\u2013232", "line_count": 39 }, { "evidence_id": "b570_02_ev_wf_507cd20b891f2c6f48", "kind": "claim_packets", "url": 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"Authority Before Autonomy: Independent Enforcement Boundaries for Self-Improving and Agentic AI", "role": "R11_BACKGROUND", "role_label": "\u80cc\u666f/\u76f8\u90bb\u57fa\u7840", "year": 2026, "branch_id": "M3_harness", "path": "papers/r11_background/wf_984e7beef015b9024c.md", "summary": "IrisKey separates system authority from agent intelligence and adjusts permitted consequence envelopes using independently supportable authority under changing distributed state.", "claim_ids": [ "role:b570_20_role_wf_984e7beef015b9024c" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_20_src_wf_984e7beef015b9024c" ], "evidence_ids": [ "b570_20_ev_wf_984e7beef015b9024c" ], "evidence_lines": [ 1069 ], "review_depths": [ "registry_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_20_ev_wf_984e7beef015b9024c", "kind": "claim_packets", "url": 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(one-based local extraction, not web-tool lines)", "line_count": 6 }, { "evidence_id": "b570_20_ev_wf_984e7beef015b9024c", "kind": "limit_packets", "url": "https://zenodo.org/api/records/23091939/files/Authority_Before_Autonomy_IrisKey_Preprint_v1.pdf/content", "version": "captured source; no latest-version assertion", "locator": "PDF physical page 3, derived page-text lines 25-30 (one-based local extraction, not web-tool lines)", "line_count": 6 } ], "content_characters": 6898, "evaluation_details_available": false }, { "work_family_id": "wf_a84b529a4123526f5c", "title": "Authorization for Self-Modifying AI Agent Populations: Conserving Authority across Replacement, Forking, and Rollback", "role": "R03_METHOD", "role_label": "\u65b9\u6cd5\u8bba\u6587", "year": 2026, "branch_id": "M8_safety", "path": "papers/r03_method/wf_a84b529a4123526f5c.md", "summary": 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"selected_original_method_and_scope_limits" ], "primary_passages": [], "content_characters": 4526, "evaluation_details_available": true }, { "work_family_id": "wf_129b00de4b5ac6f628", "title": "AutoDataBench: Can Agents Write the Data That Feeds the Self-Improvement Loop?", "role": "R05_BENCHMARK", "role_label": "Benchmark/\u6570\u636e\u96c6/\u4efb\u52a1\u5957\u4ef6", "year": 2026, "branch_id": "M7_benchmarks", "path": "papers/r05_benchmark/wf_129b00de4b5ac6f628.md", "summary": "\u5df2\u6838\u5b98\u65b9\u5168\u6587\u8bc4\u6d4b\u5bf9\u8c61\u3001\u4efb\u52a1\u534f\u8bae\u548c\u9650\u5236\uff1b\u6d4b\u91cf\u534f\u8bae\u4e0e\u88ab\u6d4b\u7814\u7a76\u65b9\u6cd5\u5206\u5f00\u3002", "claim_ids": [ "configuration:cfg_autodatabench_per_artifact_acceptance_v1" ], "configuration_ids": [ "cfg_autodatabench_per_artifact_acceptance_v1" ], "relation_ids": [], "source_ids": [ "db4_src_autodatabench" ], "evidence_ids": [ "db4_ev_cfg_autodatabench_per_artifact_acceptance_v1" ], "evidence_lines": [ 386 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Each round screens4 proposals on64 tasks and admits a candidate using4,096 fresh paired tasks; separate audits do not feed selection. It compares recursive mutation, frozen mutation and random search across3 seeds/100 rounds. 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A compiler library stores checked rules/artifacts while protected admission prevents candidates from redefining evidence or success criteria. RSI-infrastructure bridge: witness/provenance memory and compiled decisions may be reused to reduce later improvement work under protected contracts. 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evolution\u3002\u57fa\u56e0\u662f\u7528\u6237\u7684model/attitude\u9009\u62e9\uff0c\u590d\u5236/\u53d8\u5f02/\u4ece\u56fa\u5b9a\u6c60\u91cd\u91c7\u7b56\u7565\u5e76\u4e0d\u662f\u5bf9LLM\u6743\u91cd\u8bad\u7ec3\u6216\u751f\u6210\u65b0\u6539\u8fdb\u7b97\u5b50\u3002", "claim_ids": [ "role:r14_role_collective" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "r14_src_collective" ], "evidence_ids": [ "r14_ev_collective" ], "evidence_lines": [ 687 ], "review_depths": [ "selected_primary_benchmark_or_evaluation_protocol_and_limits" ], "primary_passages": [], "content_characters": 3089, "evaluation_details_available": true }, { "work_family_id": "wf_title_04bd7ce6e6ba1d87", "title": "Evaluator Transport and Finite-State Descendant Reconstruction for Self-Modifying Agents", "role": "R02_CONCEPT", "role_label": "\u6982\u5ff5/\u7406\u8bba/\u7acb\u573a", "year": 2026, "branch_id": "M3_harness", "path": "papers/r02_concept/wf_title_04bd7ce6e6ba1d87.md", "summary": "Separates evaluator comparability from sufficient state for reconstructing descendant value. Gives a conditional finite-state reconstruction theorem with independently supplied or charged calibration and quotient, and reports finite hand-constructed theorem-search checks; learned Lean-agent experiments remain proposed. Direct formal and finite-test investigation of evaluating future descendants of self-modifying agents.", "claim_ids": [ "role:b570_05_role_wf_title_04bd7ce6e6ba1d87" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_05_src_wf_title_04bd7ce6e6ba1d87" ], "evidence_ids": [ "b570_05_ev_wf_title_04bd7ce6e6ba1d87" ], "evidence_lines": [ 815 ], "review_depths": [ "registry_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_05_ev_wf_title_04bd7ce6e6ba1d87", "kind": "claim_packets", "url": "https://zenodo.org/api/records/22144509/files/recursive_improvement_dossier.pdf/content", "version": "10.5281/zenodo.22144509", "locator": "PDF p10; derived text lines 3-28 (one-based local extraction, not web-tool lines)", "line_count": 26 }, { "evidence_id": "b570_05_ev_wf_title_04bd7ce6e6ba1d87", "kind": "claim_packets", "url": "https://zenodo.org/api/records/22144509/files/recursive_improvement_dossier.pdf/content", "version": "10.5281/zenodo.22144509", "locator": "PDF p25; derived text lines 10-30 (one-based local extraction, not web-tool lines)", "line_count": 21 }, { "evidence_id": "b570_05_ev_wf_title_04bd7ce6e6ba1d87", "kind": "limit_packets", "url": "https://zenodo.org/api/records/22144509/files/recursive_improvement_dossier.pdf/content", "version": "10.5281/zenodo.22144509", "locator": "PDF p16; derived text lines 4-21 (one-based local extraction, not web-tool lines)", "line_count": 18 } ], "content_characters": 10855, "evaluation_details_available": false }, { "work_family_id": "wf_efed52b019f207a7bb", "title": "Evidence for Bounded Level-3 Recursive Self-Improvement and Level-4 Autonomous Operation in a Finite Repair Domain", "role": "R07_EMPIRICAL", "role_label": "\u5b9e\u8bc1/\u5ba1\u8ba1/\u590d\u73b0", "year": 2026, "branch_id": "M7_measurement", "path": "papers/r07_empirical/wf_efed52b019f207a7bb.md", "summary": "\u73b0\u6709\u8bc1\u636e\u4e0d\u8db3\u4ee5\u5f62\u6210\u5177\u4f53\u5185\u5bb9\u89e3\u6790\uff1b\u4fdd\u7559\u8eab\u4efd\u548c\u5f85\u6838\u4f4d\u7f6e\u3002", "claim_ids": [], "configuration_ids": [], "relation_ids": [], "source_ids": [], "evidence_ids": [], "evidence_lines": [], "review_depths": [ "registry_abstract" ], "primary_passages": [], "content_characters": 1024, "evaluation_details_available": false }, { "work_family_id": "wf_29d4dce5c471f48a76", "title": "EvoAudio: Recursive Self-Improvement for Audio Understanding", "role": "R03_METHOD", "role_label": "\u65b9\u6cd5\u8bba\u6587", "year": 2026, "branch_id": "M4_selftraining", "path": "papers/r03_method/wf_29d4dce5c471f48a76.md", "summary": "\u5728\u5f53\u524d\u5df2\u6838\u914d\u7f6e\u4e2d\uff0c\u7cfb\u7edf\u8fb9\u754c\u4e3a\uff1a\u5f53\u524d\u97f3\u9891policy\u300147\u6280\u80fd\u753b\u50cf\u3001\u5408\u6210/\u9a8c\u8bc1\u6570\u636e\u53cacheckpoint\u9009\u62e9\u7ec4\u6210\u8bad\u7ec3\u7cfb\u7edf\uff1b\u5177\u4f53\u66f4\u65b0\u8fc7\u7a0b\u4e3a\uff1a\u590d\u7528\u56fa\u5b9avalidation\u753b\u50cf\u9009\u62e9\u6280\u80fd\uff0c\u5916\u90e8\u56fa\u5b9a\u6a21\u578b\u7ec4\u7ec7\u5408\u6210\uff1b\u5f53\u524dpolicy\u5c1d\u8bd5\u8fc7\u6ee4\u540eGRPO\u66f4\u65b0\uff0c\u5019\u9009\u4e0eparent\u6bd4\u8f83\u4fdd\u7559/\u56de\u9000\uff0c\u518d\u8ba1\u7b97\u4e0b\u4e00\u8f6e\u753b\u50cf\uff1b\u66f4\u65b0\u72b6\u6001\u7684\u4fdd\u7559\u4e0e\u540e\u7ee7\u4f7f\u7528\u4e3a\uff1a\u8de813\u8f6e\u4fdd\u7559\u88ab\u63a5\u53d7policy\uff1boptimizer\u6bcf\u8f6e\u91cd\u5efa\u3002", "claim_ids": [ "configuration:cfg_r8b_evoaudio_grpo" ], "configuration_ids": [ "cfg_r8b_evoaudio_grpo" ], "relation_ids": [], "source_ids": [ "r8b_src_audio" ], "evidence_ids": [ "r8b_ev_evoaudio_grpo" ], "evidence_lines": [ 623 ], "review_depths": [ "original_abstract", "selected_primary_method_protocol_and_limits" ], "primary_passages": [], "content_characters": 3173, "evaluation_details_available": true }, { "work_family_id": "wf_450f52bda5c32d4b5d", "title": "EvoMind: A Governed Cognitive Architecture for Persistent, Verifiable, Experience-Driven Agentic Autonomy", "role": "R04_SYSTEM", "role_label": "\u7efc\u5408\u7cfb\u7edf/\u667a\u80fd\u4f53", "year": 2026, "branch_id": "M3_tools", "path": "papers/r04_system/wf_450f52bda5c32d4b5d.md", "summary": "EvoMind describes a durable governed skill lifecycle with separated discovery/sandbox, causal/regression evidence and a single promotion authority. Only Stack C activation creates canonical runtime registration; evidence hashes, scope and predecessor state accompany transactional transitions, revocation and fail-closed states. Direct bounded RSI-system bridge: experience-derived skills persist through evidence/approval lifecycle into future runtime capability, with canonical registration and revocation. This is governed capability compounding, not established AGI.", "claim_ids": [ "role:b570_10_role_wf_450f52bda5c32d4b5d" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_10_src_wf_450f52bda5c32d4b5d" ], "evidence_ids": [ "b570_10_ev_wf_450f52bda5c32d4b5d" ], "evidence_lines": [ 917 ], "review_depths": [ "registry_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_10_ev_wf_450f52bda5c32d4b5d", "kind": "claim_packets", "url": "https://zenodo.org/api/records/20580112/files/EvoMind_Governed_Cognitive_Architecture_Whitepaper_v1.0.pdf/content", "version": "Zenodo 20580112 / 1.0", "locator": "PDF physical page 8, derived page-text lines 3-17 (one-based local extraction, not web-tool lines)", "line_count": 15 }, { "evidence_id": "b570_10_ev_wf_450f52bda5c32d4b5d", "kind": "claim_packets", "url": "https://zenodo.org/api/records/20580112/files/EvoMind_Governed_Cognitive_Architecture_Whitepaper_v1.0.pdf/content", "version": "Zenodo 20580112 / 1.0", "locator": "PDF physical page 8, derived page-text lines 18-26 (one-based local extraction, not web-tool lines)", "line_count": 9 }, { "evidence_id": "b570_10_ev_wf_450f52bda5c32d4b5d", "kind": "limit_packets", "url": "https://zenodo.org/api/records/20580112/files/EvoMind_Governed_Cognitive_Architecture_Whitepaper_v1.0.pdf/content", "version": "Zenodo 20580112 / 1.0", "locator": "PDF physical page 19, derived page-text lines 11-26 (one-based local extraction, not web-tool lines)", "line_count": 16 } ], "content_characters": 8648, "evaluation_details_available": true }, { "work_family_id": "wf_bd0ee80f504b218920", "title": "EvoUndo: Recoverability-Constrained Self-Evolution for LLM Agent Harnesses", "role": "R07_EMPIRICAL", "role_label": "\u5b9e\u8bc1/\u5ba1\u8ba1/\u590d\u73b0", "year": 2026, "branch_id": "M3_harness", "path": "papers/r07_empirical/wf_bd0ee80f504b218920.md", "summary": "EvoUndo keeps capability-positive forward mutations immutable while repairing witness/recovery programs and effect contracts. Typed observational equivalence, tracked effects, four-attempt synthesis and Wilson-LCB admission govern round trips. The 197 natural-failure factorial uses 10 development/40 hidden counterfactuals with aggregate hidden feedback; a separate 300-task holdout is feedback-isolated. Direct recoverability governance for bounded persistent harness mutation; distinguish fixed-mutation recovery repair from optimizing forward changes or proving universal rollback.", "claim_ids": [ "role:b570_03_role_wf_bd0ee80f504b218920" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_03_src_wf_bd0ee80f504b218920" ], "evidence_ids": [ "b570_03_ev_wf_bd0ee80f504b218920" ], "evidence_lines": [ 770 ], "review_depths": [ "original_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_03_ev_wf_bd0ee80f504b218920", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2608.28363v1", "version": "2608.28363v1", "locator": "PDF p3; derived page text lines 4\u201321 (one-based local extraction, not web-tool lines)", "line_count": 18 }, { "evidence_id": "b570_03_ev_wf_bd0ee80f504b218920", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2608.28363v1", "version": "2608.28363v1", "locator": "PDF p4; derived page text lines 2\u201348 (one-based local extraction, not web-tool lines)", "line_count": 47 }, { "evidence_id": "b570_03_ev_wf_bd0ee80f504b218920", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2608.28363v1", "version": "2608.28363v1", "locator": "PDF p5; derived page text lines 30\u201363 (one-based local extraction, not web-tool lines)", "line_count": 34 }, { "evidence_id": "b570_03_ev_wf_bd0ee80f504b218920", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2608.28363v1", "version": "2608.28363v1", "locator": "PDF p8; derived page text lines 18\u201328 (one-based local extraction, not web-tool lines)", "line_count": 11 }, { "evidence_id": "b570_03_ev_wf_bd0ee80f504b218920", "kind": "limit_packets", "url": "https://arxiv.org/pdf/2608.28363v1", "version": "2608.28363v1", "locator": "PDF p6; derived page text lines 1\u201311 (one-based local extraction, not web-tool lines)", "line_count": 11 }, { "evidence_id": "b570_03_ev_wf_bd0ee80f504b218920", "kind": "limit_packets", "url": "https://arxiv.org/pdf/2608.28363v1", "version": "2608.28363v1", "locator": "PDF p9; derived page text lines 10\u201343 (one-based local extraction, not web-tool lines)", "line_count": 34 } ], "content_characters": 20604, "evaluation_details_available": false }, { "work_family_id": "wf_dc235cdf84c021b246", "title": "EvoX: Meta-Evolution for Automated Discovery", "role": "R03_METHOD", "role_label": "\u65b9\u6cd5\u8bba\u6587", "year": 2026, "branch_id": "M5_program", "path": "papers/r03_method/wf_dc235cdf84c021b246.md", "summary": 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Three reported generations do not establish unlimited improvement or acceleration.", "claim_ids": [ "role:b570_09_role_wf_bc8df2b238a9f8e8de" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_09_src_wf_bc8df2b238a9f8e8de" ], "evidence_ids": [ "b570_09_ev_wf_bc8df2b238a9f8e8de" ], "evidence_lines": [ 896 ], "review_depths": [ "registry_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_09_ev_wf_bc8df2b238a9f8e8de", "kind": "claim_packets", "url": "https://zenodo.org/api/records/20156848/files/A%20Self-Evolving%20Organism%20Demonstrating%20Reproducible%20Recursive%20Self-Improvement%20(FELIX).pdf/content", "version": "Zenodo 20156848", "locator": "PDF physical page 4, derived page-text lines 6-15 (one-based local extraction, not web-tool lines)", "line_count": 10 }, { "evidence_id": "b570_09_ev_wf_bc8df2b238a9f8e8de", "kind": "claim_packets", "url": "https://zenodo.org/api/records/20156848/files/A%20Self-Evolving%20Organism%20Demonstrating%20Reproducible%20Recursive%20Self-Improvement%20(FELIX).pdf/content", "version": "Zenodo 20156848", "locator": "PDF physical page 4, derived page-text lines 16-27 (one-based local extraction, not web-tool lines)", "line_count": 12 }, { "evidence_id": "b570_09_ev_wf_bc8df2b238a9f8e8de", "kind": "limit_packets", "url": "https://zenodo.org/api/records/20156848/files/A%20Self-Evolving%20Organism%20Demonstrating%20Reproducible%20Recursive%20Self-Improvement%20(FELIX).pdf/content", "version": "Zenodo 20156848", "locator": "PDF physical page 10, derived page-text lines 23-34 (one-based local extraction, not web-tool lines)", "line_count": 12 } ], "content_characters": 8891, "evaluation_details_available": true }, { "work_family_id": "wf_5f55259a53b7a18a6a", "title": "FNE-AXIOMESH: Capability-Dependent Memory and Regenerative Representation Growth for Recursive Self-Improvement (RSI)", "role": "R03_METHOD", "role_label": "\u65b9\u6cd5\u8bba\u6587", "year": 2026, "branch_id": "M2_memory", "path": "papers/r03_method/wf_5f55259a53b7a18a6a.md", "summary": "FNE-AXIOMESH defines executable cognitive state and behavioral equivalence under protected observations and all allowed operator words. 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Training/evaluation logs and scores guide later iterations. Explicit meta-improver self-modification is allowed while projects and execution records persist. Evidence supports bounded recursive design search.", "claim_ids": [ "role:b570_07_role_wf_62b4bbaa99be14d657" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_07_src_wf_62b4bbaa99be14d657" ], "evidence_ids": [ "b570_07_ev_wf_62b4bbaa99be14d657" ], "evidence_lines": [ 851 ], "review_depths": [ "original_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_07_ev_wf_62b4bbaa99be14d657", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2609.35328v1", "version": "2609.35328v1", "locator": "PDF physical page 4, derived page-text lines 10-41 (one-based local extraction, not web-tool lines)", "line_count": 32 }, { "evidence_id": "b570_07_ev_wf_62b4bbaa99be14d657", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2609.35328v1", "version": "2609.35328v1", "locator": "PDF physical page 5, derived page-text lines 32-44 (one-based local extraction, not web-tool lines)", "line_count": 13 }, { "evidence_id": "b570_07_ev_wf_62b4bbaa99be14d657", "kind": "measurement_packets", "url": "https://arxiv.org/pdf/2609.35328v1", "version": "2609.35328v1", "locator": "PDF physical page 6, derived page-text lines 23-34 (one-based local extraction, not web-tool lines)", "line_count": 12 }, { "evidence_id": "b570_07_ev_wf_62b4bbaa99be14d657", "kind": "limit_packets", "url": "https://arxiv.org/pdf/2609.35328v1", "version": "2609.35328v1", "locator": "PDF physical page 6, derived page-text lines 17-35 (one-based local extraction, not web-tool lines)", "line_count": 19 }, { "evidence_id": "b570_07_ev_wf_62b4bbaa99be14d657", "kind": "limit_packets", "url": "https://arxiv.org/pdf/2609.35328v1", "version": "2609.35328v1", "locator": "PDF physical page 9, derived page-text lines 34-41 (one-based local extraction, not web-tool lines)", "line_count": 8 } ], "content_characters": 14680, "evaluation_details_available": true }, { "work_family_id": "wf_17eec8a15debc212b5", "title": "Hyperagents", "role": "R03_METHOD", "role_label": "\u65b9\u6cd5\u8bba\u6587", "year": 2026, "branch_id": "M3_harness", "path": "papers/r03_method/wf_17eec8a15debc212b5.md", "summary": "\u5728\u5f53\u524d\u5df2\u6838\u914d\u7f6e\u4e2d\uff0c\u7cfb\u7edf\u8fb9\u754c\u4e3a\uff1a\u4efb\u52a1\u4e0emeta\u4ee3\u7801\u7ec4\u5408\u7684hyperagent\u5f52\u6863\uff1b\u5177\u4f53\u66f4\u65b0\u8fc7\u7a0b\u4e3a\uff1a\u53ef\u7f16\u8f91meta\u7a0b\u5e8f\u5f71\u54cd\u540e\u7eed\u4fee\u6539\uff1b\u66f4\u65b0\u72b6\u6001\u7684\u4fdd\u7559\u4e0e\u540e\u7ee7\u4f7f\u7528\u4e3a\uff1a\u540e\u7ee7hyperagent\u53ef\u518d\u6b21\u88ab\u9009\u4e2d\u751f\u6210\u4fee\u6539\u3002", "claim_ids": [ "configuration:cfg_hyperagents", "configuration:cfg_hyperagents_fixed_meta_probe", "configuration:cfg_hyperagents_parent_selection" ], "configuration_ids": [ "cfg_hyperagents", "cfg_hyperagents_parent_selection", "cfg_hyperagents_fixed_meta_probe" ], "relation_ids": [ "r_hyper_dgm", "r_meta_n_hyper", "r3_red_queen_adapts_hyperagents_workspace" ], "source_ids": [ "recheck_src_w_hyperagents" ], "evidence_ids": [ "e_hyper", "e_hyper_fixed" ], "evidence_lines": [ 98, 99 ], "review_depths": [ "selected_method_passages_with_abstract_baseline" ], "primary_passages": [], "content_characters": 5345, "evaluation_details_available": false }, { "work_family_id": "wf_7b03eace0882ad44f4", "title": "Hyperagents: Metacognitive Self-Referential Frameworks for Recursive Self-Improvement in Open-Ended AI Systems -A Comprehensive Review (Vol-I)", "role": "R01_SURVEY", "role_label": "\u7efc\u8ff0/\u7cfb\u7edf\u7efc\u8ff0/\u8c31\u7cfb", "year": 2026, "branch_id": "M0_surveys", "path": "papers/r01_survey/wf_7b03eace0882ad44f4.md", "summary": "Provides a narrative review of Hyperagents and related self-referential systems, organizing shared task/meta code, archive-based selection, empirical validation and proposed algebraic/geometric interpretations. Experimental outcomes are summaries attributed to the reviewed framework rather than experiments executed by this review. 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Local cosine retrieval and remote hyperedge traversal are separate inference channels. Explicit RSI-concept bridge: symbolic validation and re-embedding are proposed to persistently reorganize representations for later inference. 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Agent: \"KissMyAss\"", "role": "R03_METHOD", "role_label": "\u65b9\u6cd5\u8bba\u6587", "year": 2026, "branch_id": "M3_harness", "path": "papers/r03_method/wf_c073cf9f5f94ec36fb.md", "summary": "KissMyAss documentation specifies a ReAct/checkpoint loop, dynamically synthesized tools subject to AST checks/mutation tests, immutable/mutable/dynamic prompt tiers, decayed Bayesian heuristic utility, and multiobjective prompt tournaments with regression checks and rollback. Direct bounded RSI bridge: tool/prompt/heuristic changes are intended to persist and affect later task execution, while directives and regression gates stay fixed. Fixed gates do not remove adaptation; they do not prove universal safety.", "claim_ids": [ "role:b570_09_role_wf_c073cf9f5f94ec36fb" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_09_src_wf_c073cf9f5f94ec36fb" ], "evidence_ids": [ "b570_09_ev_wf_c073cf9f5f94ec36fb" ], "evidence_lines": [ 897 ], "review_depths": [ "registry_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_09_ev_wf_c073cf9f5f94ec36fb", "kind": "claim_packets", "url": "https://zenodo.org/api/records/22102322/files/self-improving-agent%20(3).tar/content", "version": "Zenodo 22102322", "locator": "Document body section 1, local text lines 24-38 (one-based local extraction, not web-tool lines)", "line_count": 15 }, { "evidence_id": "b570_09_ev_wf_c073cf9f5f94ec36fb", "kind": "claim_packets", "url": "https://zenodo.org/api/records/22102322/files/self-improving-agent%20(3).tar/content", "version": "Zenodo 22102322", "locator": "Document body section 1, local text lines 40-47 (one-based local extraction, not web-tool lines)", "line_count": 8 }, { "evidence_id": "b570_09_ev_wf_c073cf9f5f94ec36fb", "kind": "limit_packets", "url": "https://zenodo.org/api/records/22102322/files/self-improving-agent%20(3).tar/content", "version": "Zenodo 22102322", "locator": "Document body section 1, local text lines 53-64 (one-based local extraction, not web-tool lines)", "line_count": 12 } ], "content_characters": 9153, "evaluation_details_available": true }, { "work_family_id": "wf_8b5cbd1cc1fa717536", "title": "L\u00e1r Cognitive Architecture v2.0 Abstract Node Topologies for Continuous Identity and Safe Recursive Self-Improvement", "role": "R02_CONCEPT", "role_label": "\u6982\u5ff5/\u7406\u8bba/\u7acb\u573a", "year": 2026, "branch_id": "M0_definitions", "path": "papers/r02_concept/wf_8b5cbd1cc1fa717536.md", "summary": "L\u00e1r v2.0 proposes node topologies for self-research and persistent state: an AutoResearchNode validates experiment graphs, spawns specialist subgraphs, measures findings and requires a HumanJury checkpoint before memory consolidation/signing. A MetaOptimiser watches runtime traces, proposes graph replacements and uses human approval plus rollback. Explicit RSI-design bridge: trace-driven topology replacement and consolidated research outputs are intended to affect future agent execution under a protected authorization path.", "claim_ids": [ "role:b570_10_role_wf_8b5cbd1cc1fa717536" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_10_src_wf_8b5cbd1cc1fa717536" ], "evidence_ids": [ "b570_10_ev_wf_8b5cbd1cc1fa717536" ], "evidence_lines": [ 919 ], "review_depths": [ "registry_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_10_ev_wf_8b5cbd1cc1fa717536", "kind": "claim_packets", "url": "https://zenodo.org/api/records/19120047/files/Lar_Cognitive_Architecture_v2_0.pdf/content", "version": "Zenodo 19120047", "locator": "PDF physical page 3, derived page-text lines 37-43 (one-based local extraction, not web-tool lines)", "line_count": 7 }, { "evidence_id": "b570_10_ev_wf_8b5cbd1cc1fa717536", "kind": "claim_packets", "url": "https://zenodo.org/api/records/19120047/files/Lar_Cognitive_Architecture_v2_0.pdf/content", "version": "Zenodo 19120047", "locator": "PDF physical page 4, derived page-text lines 2-16 (one-based local extraction, not web-tool lines)", "line_count": 15 }, { "evidence_id": "b570_10_ev_wf_8b5cbd1cc1fa717536", "kind": "limit_packets", "url": "https://zenodo.org/api/records/19120047/files/Lar_Cognitive_Architecture_v2_0.pdf/content", "version": "Zenodo 19120047", "locator": "PDF physical page 3, derived page-text lines 3-8 (one-based local extraction, not web-tool lines)", "line_count": 6 } ], "content_characters": 7809, "evaluation_details_available": true }, { "work_family_id": "wf_fbbad763bd786ffb65", "title": "MAGE-SISC v1.0.0: Synthetic-Infinity Universal Matter Compiler - Uniform Causal Matter Compression, Certified Spawn Kernels, and Recursive Matter Intelligence", "role": "R02_CONCEPT", "role_label": "\u6982\u5ff5/\u7406\u8bba/\u7acb\u573a", "year": 2026, "branch_id": "M0_definitions", "path": "papers/r02_concept/wf_fbbad763bd786ffb65.md", "summary": "MAGE-SISC proposes causal-quotient representation learning, certified local control-field compilation and reachability-gradient self-play. Counterexamples refine the representation; the self-play loop diagnoses bottlenecks, filters experiments, updates uncertainty and promotes operators only after validation. Explicit RSI-concept bridge: learned reusable operators and world models are proposed to expand later reachable tasks, with delayed capability reward. Toy compression/control checks are distinct from empirical recursive operator invention.", "claim_ids": [ "role:b570_09_role_wf_fbbad763bd786ffb65" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_09_src_wf_fbbad763bd786ffb65" ], "evidence_ids": [ "b570_09_ev_wf_fbbad763bd786ffb65" ], "evidence_lines": [ 902 ], "review_depths": [ "registry_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_09_ev_wf_fbbad763bd786ffb65", "kind": "claim_packets", "url": "https://zenodo.org/api/records/22981323/files/MAGE_SISC_v1.0.0.zip/content", "version": "Zenodo 22981323", "locator": "PDF physical page 24, derived page-text lines 1-9 (one-based local extraction, not web-tool lines)", "line_count": 9 }, { "evidence_id": "b570_09_ev_wf_fbbad763bd786ffb65", "kind": "claim_packets", "url": "https://zenodo.org/api/records/22981323/files/MAGE_SISC_v1.0.0.zip/content", "version": "Zenodo 22981323", "locator": "PDF physical page 24, derived page-text lines 21-32 (one-based local extraction, not web-tool lines)", "line_count": 12 }, { "evidence_id": "b570_09_ev_wf_fbbad763bd786ffb65", "kind": "limit_packets", "url": "https://zenodo.org/api/records/22981323/files/MAGE_SISC_v1.0.0.zip/content", "version": "Zenodo 22981323", "locator": "PDF physical page 24, derived page-text lines 33-35 (one-based local extraction, not web-tool lines)", "line_count": 3 }, { "evidence_id": "b570_09_ev_wf_fbbad763bd786ffb65", "kind": "limit_packets", "url": "https://zenodo.org/api/records/22981323/files/MAGE_SISC_v1.0.0.zip/content", "version": "Zenodo 22981323", "locator": "PDF physical page 25, derived page-text lines 8-21 (one-based local extraction, not web-tool lines)", "line_count": 14 } ], "content_characters": 9282, "evaluation_details_available": true }, { "work_family_id": "wf_1c48bd5bc738f5cd29", "title": "MC-EvoBench: Auditing Experience-Driven Self-Improvement in Minecraft Agents", "role": "R05_BENCHMARK", "role_label": "Benchmark/\u6570\u636e\u96c6/\u4efb\u52a1\u5957\u4ef6", "year": 2026, "branch_id": "M7_benchmarks", "path": "papers/r05_benchmark/wf_1c48bd5bc738f5cd29.md", "summary": "Persistent experience-use reference; audit does not demonstrate recursive target updates or stable transfer.", "claim_ids": [ "configuration:cfg_mc_evobench_jarvis_online_transfer" ], "configuration_ids": [ "cfg_mc_evobench_jarvis_online_transfer" ], "relation_ids": [], "source_ids": [ "src_w_mc_evobench" ], "evidence_ids": [], "evidence_lines": [], "review_depths": [ "targeted_primary_full_text_method_limits_pass" ], "primary_passages": [], "content_characters": 2358, "evaluation_details_available": false }, { "work_family_id": "wf_7b24b522ee4ec955d8", "title": "MERID: Multimodal Exploration via Recursive Self-Improvement Agents for Major Depression Analysis", "role": "R03_METHOD", "role_label": "\u65b9\u6cd5\u8bba\u6587", "year": 2026, "branch_id": "M3_harness", "path": "papers/r03_method/wf_7b24b522ee4ec955d8.md", "summary": "MERID preserves subject-target alignment through a checked adapter, searches whole recipe/predictor pipelines, and verifies incumbent replacement with development evidence and uncertainty margins; failed candidates can inform subsequent proposals. Persistent components, incumbent and revision history guide later pipeline proposals. Memory learning is a distinct optional channel, not a property of all reported runs.", "claim_ids": [ "role:b570_07_role_wf_7b24b522ee4ec955d8" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_07_src_wf_7b24b522ee4ec955d8" ], "evidence_ids": [ "b570_07_ev_wf_7b24b522ee4ec955d8" ], "evidence_lines": [ 852 ], "review_depths": [ "original_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_07_ev_wf_7b24b522ee4ec955d8", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2609.36235v1", "version": "2609.36235v1", "locator": "PDF physical page 5, derived page-text lines 1-14 (one-based local extraction, not web-tool lines)", "line_count": 14 }, { "evidence_id": "b570_07_ev_wf_7b24b522ee4ec955d8", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2609.36235v1", "version": "2609.36235v1", "locator": "PDF physical page 7, derived page-text lines 21-31 (one-based local extraction, not web-tool lines)", "line_count": 11 }, { "evidence_id": "b570_07_ev_wf_7b24b522ee4ec955d8", "kind": "measurement_packets", "url": "https://arxiv.org/pdf/2609.36235v1", "version": "2609.36235v1", "locator": "PDF physical page 7, derived page-text lines 34-48 (one-based local extraction, not web-tool lines)", "line_count": 15 }, { "evidence_id": "b570_07_ev_wf_7b24b522ee4ec955d8", "kind": "limit_packets", "url": "https://arxiv.org/pdf/2609.36235v1", "version": "2609.36235v1", "locator": "PDF physical page 10, derived page-text lines 14-21 (one-based local extraction, not web-tool lines)", "line_count": 8 }, { "evidence_id": "b570_07_ev_wf_7b24b522ee4ec955d8", "kind": "limit_packets", "url": "https://arxiv.org/pdf/2609.36235v1", "version": "2609.36235v1", "locator": "PDF physical page 52, derived page-text lines 20-29 (one-based local extraction, not web-tool lines)", "line_count": 10 } ], "content_characters": 12201, "evaluation_details_available": true }, { "work_family_id": "wf_a6652285d15eac3e23", "title": "MILO: Automated Harness Discovery via Orchestrated Multi-Agent Evolution", "role": "R04_SYSTEM", "role_label": "\u7efc\u5408\u7cfb\u7edf/\u667a\u80fd\u4f53", "year": 2026, "branch_id": "M3_harness", "path": "papers/r04_system/wf_a6652285d15eac3e23.md", "summary": "MILO evolves executable harnesses and a search configuration through island lineage memory, coding-agent mutators and a stall-triggered orchestrator. The append-only memory retains admitted and rejected children, while orchestration changes island structure, mutator assignment or task curriculum. Direct RSI bridge: persistent harness rewrites affect subsequent task solving, and orchestration adapts the search strategy used to generate later rewrites. 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metadata version 1.0", "locator": "derived L167\u2013210", "line_count": 44 }, { "evidence_id": "b570_02_ev_wf_00721b55e6add6cd58", "kind": "limit_packets", "url": "https://zenodo.org/api/records/20562053/files/verifier-frontier-observatory.pdf/content", "version": "Zenodo version DOI 10.5281/zenodo.20562053; metadata version 1.0", "locator": "derived L221\u2013233", "line_count": 13 } ], "content_characters": 31695, "evaluation_details_available": false }, { "work_family_id": "wf_f025e1cd4ede05d159", "title": "MedForge-RSI: Medical Deepfake Detection via Recursive Self-Improvement", "role": "R03_METHOD", "role_label": "\u65b9\u6cd5\u8bba\u6587", "year": 2026, "branch_id": "M9_multimodal", "path": "papers/r03_method/wf_f025e1cd4ede05d159.md", "summary": "\u5728\u5f53\u524d\u5df2\u6838\u914d\u7f6e\u4e2d\uff0c\u7cfb\u7edf\u8fb9\u754c\u4e3a\uff1av3\u7ecf\u9a8c\u5e93/\u5de5\u5177\u53ca\u51bb\u7ed3\u68c0\u6d4b\u6a21\u578b\u7684\u53cd\u601d\uff1b\u5177\u4f53\u66f4\u65b0\u8fc7\u7a0b\u4e3a\uff1a\u73b0\u6709\u5de5\u5177\u8bfb\u6570\u4e0e\u6307\u4ee4\u5386\u53f2\u8f93\u5165\u53cd\u601d\uff0c\u5f62\u6210\u540e\u7eed\u5e93/\u5de5\u5177\u63d0\u6848\uff1b\u5de5\u5177\u4fdd\u7559\u7528\u4e8e\u540e\u7eed\u6d4b\u91cf\u4e0e\u53cd\u601d\uff1b\u66f4\u65b0\u72b6\u6001\u7684\u4fdd\u7559\u4e0e\u540e\u7ee7\u4f7f\u7528\u4e3a\uff1a\u90e8\u7f72\u6a21\u62df\u8f6e\u6b21\u5185\u3002", "claim_ids": [ "configuration:cfg_medforge_v3" ], "configuration_ids": [ "cfg_medforge_v3" ], "relation_ids": [], "source_ids": [ "expand_src_w_medforge_rsi", "hist_src_medforge_abs" ], "evidence_ids": [ "e2_w_medforge_rsi" ], "evidence_lines": [ 136 ], "review_depths": [ "selected_method_passages_with_abstract_baseline" ], "primary_passages": [], "content_characters": 3289, "evaluation_details_available": false }, { "work_family_id": "wf_56066c1b83df3075b9", "title": "MedForge: Interpretable Medical Deepfake Detection via Forgery-aware Reasoning", "role": "R03_METHOD", "role_label": "\u65b9\u6cd5\u8bba\u6587", "year": 2026, "branch_id": "M9_multimodal", "path": "papers/r03_method/wf_56066c1b83df3075b9.md", "summary": "MedForge\u672c\u8eab\u662f\u9759\u6001\u57fa\u7840\u68c0\u6d4b\u7cfb\u7edf\u548c\u6570\u636e/\u6a21\u578b\u8d44\u6e90\uff1bMedForge-RSI\u5728\u5176\u51bb\u7ed3\u6a21\u578b\u4e4b\u4e0a\u589e\u52a0\u90e8\u7f72\u671f\u6301\u4e45\u7ecf\u9a8c\u4e0e\u5de5\u5177\u95ed\u73af", "claim_ids": [ "configuration:cfg_medforge_reasoner" ], "configuration_ids": [ "cfg_medforge_reasoner" ], "relation_ids": [], "source_ids": [ "registered_cfg_src_86239974c524c14f" ], "evidence_ids": [], "evidence_lines": [], "review_depths": [ "targeted_primary_full_text_method_pass" ], "primary_passages": [], "content_characters": 1765, "evaluation_details_available": false }, { "work_family_id": "wf_825842f3cb42f01a8c", "title": "MedRSI: Recursive Self-Improvement for Medical Agents via Clinically Aligned Self-Evolution", "role": "R03_METHOD", "role_label": "\u65b9\u6cd5\u8bba\u6587", "year": 2026, "branch_id": "M3_tools", "path": "papers/r03_method/wf_825842f3cb42f01a8c.md", "summary": "MedRSI \u5c06\u8bca\u65ad\u5931\u8d25\u8f6c\u4e3a\u5de5\u5177\u7ec4\u5408\u4e0e\u4efb\u52a1\u6a21\u578b\u8bad\u7ec3\uff0c\u4ee5\u4e34\u5e8a\u4ee3\u4ef7\u6392\u4f18\u5148\u7ea7\u5e76\u5c06\u5feb\u901f\u53d1\u73b0\u4e0e\u8de8\u540e\u7eed\u60a3\u8005\u961f\u5217\u7684\u6162\u6ce8\u518c\u5206\u5f00\uff1b\u6709\u751f\u6210\u3001\u9a8c\u8bc1\u3001\u6301\u4e45\u91c7\u7528\u7ebf\u7d22\uff0c\u4ecd\u9700\u6838\u4e34\u5e8a\u4efb\u52a1\u4e0e\u9a8c\u8bc1\u8fb9\u754c\u3002", "claim_ids": [ "configuration:cfg_medrsi_stable_tool_registry_v1" ], "configuration_ids": [ "cfg_medrsi_stable_tool_registry_v1" ], "relation_ids": [], "source_ids": [ "r6app_src_medrsi", "s18_role_src_7edd05d3174e0b70" ], "evidence_ids": [ "r6app_ev_cfg_medrsi_stable_tool_registry_v1", "s18_role_ev_wf_825842f3cb42f01a8c" ], "evidence_lines": [ 409, 536 ], "review_depths": [ "original_abstract", "selected_primary_method_protocol_and_limits" ], "primary_passages": [], "content_characters": 4829, "evaluation_details_available": true }, { "work_family_id": "wf_15c03330a6bf7a11f9", "title": "Mem2Evolve: Towards Self-Evolving Agents via Co-Evolutionary Capability Expansion and Experience Distillation", "role": "R04_SYSTEM", "role_label": "\u7efc\u5408\u7cfb\u7edf/\u667a\u80fd\u4f53", "year": 2026, "branch_id": "M3_tools", "path": "papers/r04_system/wf_15c03330a6bf7a11f9.md", "summary": "Core persistent asset and experience configuration; external judge/test protocol is not self-certified ground truth.", "claim_ids": [ "configuration:cfg_mem2evolve_dual_memory" ], "configuration_ids": [ "cfg_mem2evolve_dual_memory" ], "relation_ids": [], "source_ids": [ "src_mem2evolve_acl" ], "evidence_ids": [], "evidence_lines": [], "review_depths": [ "targeted_primary_full_text_method_limits_pass" ], "primary_passages": [], "content_characters": 2649, "evaluation_details_available": false }, { "work_family_id": "wf_4f5bbf3318b1fe409e", "title": "MemRL: Self-Evolving Agents via Runtime Reinforcement Learning on Episodic Memory", "role": "R03_METHOD", "role_label": "\u65b9\u6cd5\u8bba\u6587", "year": 2026, "branch_id": "M2_memory", "path": "papers/r03_method/wf_4f5bbf3318b1fe409e.md", "summary": "Core persistent memory configuration; improvement rule self-rewrite and unbounded recursion are not established.", "claim_ids": [ "configuration:cfg_memrl_runtime_memory" ], "configuration_ids": [ "cfg_memrl_runtime_memory" ], "relation_ids": [], "source_ids": [ "completion_src_memrl_v2" ], "evidence_ids": [], "evidence_lines": [], "review_depths": [ "targeted_primary_full_text_method_limits_pass" ], "primary_passages": [], "content_characters": 2777, "evaluation_details_available": false }, { "work_family_id": "wf_2144cdc921ff7eb4a5", "title": "Memento 3: Model-Based Recursive Self-Improvement through Reflective Rulebooks", "role": "R03_METHOD", "role_label": "\u65b9\u6cd5\u8bba\u6587", "year": 2026, "branch_id": "M2_memory", "path": "papers/r03_method/wf_2144cdc921ff7eb4a5.md", "summary": "\u5728\u5f53\u524d\u5df2\u6838\u914d\u7f6e\u4e2d\uff0c\u7cfb\u7edf\u8fb9\u754c\u4e3a\uff1a\u51bb\u7ed3LLM\u3001\u81ea\u7136\u8bed\u8a00rulebook \u03c8\u3001\u53ef\u6267\u884cworld model \u03b8\u3001\u4ea4\u4e92\u5386\u53f2H\u3001replay verifier\u53caplanner\uff0c\u9762\u5411\u672a\u77e5deterministic POMDP\u3002\u03b8\u6307\u7a0b\u5e8f\u4e0d\u662f\u795e\u7ecf\u7f51\u7edc\u53c2\u6570\uff1b\u5177\u4f53\u66f4\u65b0\u8fc7\u7a0b\u4e3a\uff1a\u9884\u6d4b\u4e0e\u73af\u5883observation\u4e0d\u7b26\u89e6\u53d1retain/revise_rulebook/repair_code\uff1b\u51bb\u7ed3LLM\u4fee\u89c4\u5219\u53ca\u7f16\u8bd1\u4ee3\u7801\u3002\u6240\u6709\u5386\u53f2exact replay\u52a0LLM\u8bed\u4e49fidelity\u5ba1\u6838\u540e\u63a5\u53d7\uff0creconstructed state\u7528\u4e8e\u4e0b\u4e00\u6b21planning\u548cexploration\uff0c\u53d6\u5f97\u65b0\u8bc1\u636e\u518d\u6539\u6a21\u578b\uff1b\u66f4\u65b0\u72b6\u6001\u7684\u4fdd\u7559\u4e0e\u540e\u7ee7\u4f7f\u7528\u4e3a\uff1aZ=(H,\u03c8,\u03b8)\u53careplayed internal state\u8de8\u540c\u4e00\u73af\u5883\u7684\u52a8\u4f5c/\u5c1d\u8bd5\u4fdd\u7559\uff1b\u66f4\u6539\u03b8\u540e\u56de\u653e\u65e7\u8f68\u8ff9\u91cd\u6784\u72b6\u6001\uff0c\u672a\u8ba4\u8bc1\u8de8\u4e0d\u540cgame\u957f\u671f\u5171\u4eab\u3002", "claim_ids": [ "configuration:cfg_memento3_single_rulebook_executable_model_v1" ], "configuration_ids": [ "cfg_memento3_single_rulebook_executable_model_v1" ], "relation_ids": [], "source_ids": [ "r12_src_memento" ], "evidence_ids": [ "r12_ev_cfg_memento3_single_rulebook_executable_model_v1" ], "evidence_lines": [ 668 ], "review_depths": [ "original_abstract", "targeted_primary_method_state_feedback_limits_20261019" ], "primary_passages": [], "content_characters": 5212, "evaluation_details_available": true }, { "work_family_id": "wf_a6e06df6752438f6b1", "title": "Memory as transformation: LETHE, a self-referential gan-inspired architecture", "role": "R04_SYSTEM", "role_label": "\u7efc\u5408\u7cfb\u7edf/\u667a\u80fd\u4f53", "year": 2026, "branch_id": "M0_historical", "path": "papers/r04_system/wf_a6e06df6752438f6b1.md", "summary": "LETHE rewrites processed sound into a circular buffer while a discriminator scores normalized energy features against a warm-up archive; reward-weighted random perturbations update the current FDN parameters, under coupling and spectral-radius constraints. Retained as an explicit bounded self-evaluating adaptive feedback system adjacent to RSI; persistent audio/parameters are distinct from AI-level algorithmic self-redesign.", "claim_ids": [ "role:b570_11_role_wf_a6e06df6752438f6b1" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_11_src_wf_a6e06df6752438f6b1" ], "evidence_ids": [ "b570_11_ev_wf_a6e06df6752438f6b1" ], "evidence_lines": [ 949 ], "review_depths": [ "original_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_11_ev_wf_a6e06df6752438f6b1", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2609.04289v1", "version": "arXiv:2609.04289v1", "locator": "PDF physical page 2, derived page-text lines 27-37 (one-based local extraction, not web-tool lines)", "line_count": 11 }, { "evidence_id": "b570_11_ev_wf_a6e06df6752438f6b1", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2609.04289v1", "version": "arXiv:2609.04289v1", "locator": 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Two successive machinery generations expand reachable acquisition outcomes under the authored budget; a blind transfer test preserves a failed fixed-point certificate rather than treating every checker predicate as passed. These are bounded causal mechanism findings, not open-ended RSI. 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"evaluation_details_available": true }, { "work_family_id": "wf_147df5e731a78e62ab", "title": "Model Inversion: A Failure Mode Distinct from Model Collapse in Self-Modifying AI Systems", "role": "R08_LIMITATION", "role_label": "\u5931\u8d25/\u9650\u5236/\u98ce\u9669", "year": 2026, "branch_id": "M8_failure", "path": "papers/r08_limitation/wf_147df5e731a78e62ab.md", "summary": "The paper distinguishes model inversion from ordinary collapse as a self-modification failure in which a still-capable system increasingly pursues the wrong objective rather than merely losing task performance.", "claim_ids": [ "role:stage2_570_remaining_body_supplement_20261021_role_wf_147df5e731a78e62ab" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "stage2_570_remaining_body_supplement_20261021_src_wf_147df5e731a78e62ab" ], "evidence_ids": [ "stage2_570_remaining_body_supplement_20261021_ev_wf_147df5e731a78e62ab" ], "evidence_lines": [ 1168 ], "review_depths": [ "registry_abstract", 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"\u6cbb\u7406/\u7ecf\u6d4e/\u793e\u4f1a\u5f71\u54cd", "year": 2026, "branch_id": "M8_impact", "path": "papers/r12_impact/wf_eb57a3e76211d141c8.md", "summary": "\u73b0\u6709\u8bc1\u636e\u4e0d\u8db3\u4ee5\u5f62\u6210\u5177\u4f53\u5185\u5bb9\u89e3\u6790\uff1b\u4fdd\u7559\u8eab\u4efd\u548c\u5f85\u6838\u4f4d\u7f6e\u3002", "claim_ids": [], "configuration_ids": [], "relation_ids": [], "source_ids": [], "evidence_ids": [], "evidence_lines": [], "review_depths": [ "registry_abstract" ], "primary_passages": [], "content_characters": 1019, "evaluation_details_available": false }, { "work_family_id": "wf_3be6a02e859e2f7358", "title": "ModularRSI: Modular and Generalizable Recursive Harness Self-Improvement", "role": "R03_METHOD", "role_label": "\u65b9\u6cd5\u8bba\u6587", "year": 2026, "branch_id": "M3_harness", "path": "papers/r03_method/wf_3be6a02e859e2f7358.md", "summary": "ModularRSI 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"evaluation_details_available": true }, { "work_family_id": "wf_f08e60e28a4f1e34b1", "title": "Naive Prompt Optimization: Rethinking the Need for Complex Prompt Search", "role": "R03_METHOD", "role_label": "\u65b9\u6cd5\u8bba\u6587", "year": 2026, "branch_id": "M2_prompt", "path": "papers/r03_method/wf_f08e60e28a4f1e34b1.md", "summary": "NPO maintains one prompt lineage, executes the current student prompt repeatedly, and asks a teacher to revise it from a sliding history of prompts, full rollout traces and rollout-specific rewards. Its controlled comparison shares environment seeds and constrains generated actions to separate decision quality from formatting errors. Direct bounded prompt-update bridge: execution feedback changes the persistent prompt used for later student rollouts. 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Development tests support between-episode adoption; inherited diagnostic tools affect later improvement rounds. Direct physical RSI: meta-harness changes affect later diagnosis/revision. Model weights, robot dynamics and evaluator stay fixed.", "claim_ids": [ "role:b570_07_role_wf_7cdcb539a6f2ae8941" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_07_src_wf_7cdcb539a6f2ae8941" ], "evidence_ids": [ "b570_07_ev_wf_7cdcb539a6f2ae8941" ], "evidence_lines": [ 854 ], "review_depths": [ "original_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_07_ev_wf_7cdcb539a6f2ae8941", "kind": "claim_packets", "url": "https://arxiv.org/html/2610.08995v1", "version": "2610.08995v1", "locator": "HTML local derived paragraphs 21-26 (one-based local extraction, not web-tool lines)", "line_count": 6 }, { "evidence_id": "b570_07_ev_wf_7cdcb539a6f2ae8941", "kind": "claim_packets", "url": "https://arxiv.org/html/2610.08995v1", "version": "2610.08995v1", "locator": "HTML local derived paragraphs 30-33 (one-based local extraction, not web-tool lines)", "line_count": 4 }, { "evidence_id": "b570_07_ev_wf_7cdcb539a6f2ae8941", "kind": "measurement_packets", "url": "https://arxiv.org/html/2610.08995v1", "version": "2610.08995v1", "locator": "HTML local derived paragraphs 45-51 (one-based local extraction, not web-tool lines)", "line_count": 7 }, { "evidence_id": "b570_07_ev_wf_7cdcb539a6f2ae8941", "kind": "limit_packets", "url": "https://arxiv.org/html/2610.08995v1", "version": "2610.08995v1", "locator": "HTML local derived paragraphs 49-51 (one-based local extraction, not web-tool lines)", "line_count": 3 } ], "content_characters": 10870, "evaluation_details_available": true }, { "work_family_id": "wf_91405fffe2dd4bb77f", "title": "Plug-and-Play RSI Harness Module: Empirical Validation of a Recursive Self-Improvement Safety Harness", "role": "R07_EMPIRICAL", "role_label": "\u5b9e\u8bc1/\u5ba1\u8ba1/\u590d\u73b0", "year": 2026, "branch_id": "M7_measurement", "path": "papers/r07_empirical/wf_91405fffe2dd4bb77f.md", "summary": "\u73b0\u6709\u8bc1\u636e\u4e0d\u8db3\u4ee5\u5f62\u6210\u5177\u4f53\u5185\u5bb9\u89e3\u6790\uff1b\u4fdd\u7559\u8eab\u4efd\u548c\u5f85\u6838\u4f4d\u7f6e\u3002", "claim_ids": [], "configuration_ids": [], "relation_ids": [], "source_ids": [], "evidence_ids": [], "evidence_lines": [], "review_depths": [ "registry_abstract" ], "primary_passages": [], "content_characters": 1004, "evaluation_details_available": false }, { "work_family_id": "wf_7905f8e027df374c1a", "title": "Policy-Level Recursive Self-Improvement for Embodied AI with a Criticality World Model", "role": "R03_METHOD", "role_label": "\u65b9\u6cd5\u8bba\u6587", "year": 2026, "branch_id": "M4_selftraining", "path": "papers/r03_method/wf_7905f8e027df374c1a.md", "summary": "\u6267\u884c\u7ed3\u679c\u8bad\u7ec3 criticality world model\uff0c\u518d\u6309\u98ce\u9669\u5206\u6570\u91c7\u96c6\u573a\u666f\u4ee5\u8bad\u7ec3 specialist\uff0c\u5e76\u4ee5\u91cd\u91c7\u6837\u6821\u6b63\u9891\u7387\u504f\u79fb\u3001\u95e8\u63a7\u7ec4\u5408 specialist \u4e0e\u51bb\u7ed3 nominal \u7b56\u7565\uff1b\u5b9e\u9645\u66f4\u65b0\u662f\u7b56\u7565\u4e0e\u91c7\u6837\u53cd\u9988\uff0c\u975e\u793e\u4f1a\u5f71\u54cd\u8bba\u6587\u3002", "claim_ids": [ "configuration:cfg_policy_cwm_multiround_specialist_v2", "configuration:cfg_policy_cwm_single_bc_gate_v2" ], "configuration_ids": [ "cfg_policy_cwm_multiround_specialist_v2", "cfg_policy_cwm_single_bc_gate_v2" ], "relation_ids": [], "source_ids": [ "r6app_src_cwm", "s18ctx_role_src_7edd05d3174e0b70" ], "evidence_ids": [ "r6app_ev_cfg_policy_cwm_multiround_specialist_v2", "r6app_ev_cfg_policy_cwm_single_bc_gate_v2", "s18ctx_role_ev_wf_7905f8e027df374c1a" ], "evidence_lines": [ 447, 538, 539 ], "review_depths": [ "original_abstract", "selected_primary_method_protocol_and_limits" ], "primary_passages": [], "content_characters": 7223, "evaluation_details_available": true }, { "work_family_id": "wf_574dd67727c3812f00", "title": "Post-AGI Layering and the Universal / Multiversal Intelligence Adjacency", "role": "R11_BACKGROUND", "role_label": "\u80cc\u666f/\u76f8\u90bb\u57fa\u7840", "year": 2026, "branch_id": "M0_definitions", "path": "papers/r11_background/wf_574dd67727c3812f00.md", "summary": "The essay places recursive and collective intelligence between AGI, ASI, and a proposed universal-intelligence limit.", "claim_ids": [], "configuration_ids": [], "relation_ids": [], "source_ids": [ "stage2_570_section_supplement_20261021_src_wf_574dd67727c3812f00" ], "evidence_ids": [ "stage2_570_section_supplement_20261021_ev_wf_574dd67727c3812f00" ], "evidence_lines": [ 1147 ], "review_depths": [ "registry_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "stage2_570_section_supplement_20261021_ev_wf_574dd67727c3812f00", "kind": "claim_packets", "url": "https://zenodo.org/api/records/22277308/files/2026-06-19-post-agi-layering-and-universal-multiversal-intelligence-adjacency_v7.md/content", "version": "captured source; no latest-version assertion", "locator": "Derived document section document_body, lines 113-118 (one-based local extraction, not web-tool lines)", "line_count": 6 }, { "evidence_id": "stage2_570_section_supplement_20261021_ev_wf_574dd67727c3812f00", "kind": "claim_packets", "url": "https://zenodo.org/api/records/22277308/files/2026-06-19-post-agi-layering-and-universal-multiversal-intelligence-adjacency_v7.md/content", "version": "captured source; no latest-version assertion", "locator": "Derived document section document_body, lines 130-135 (one-based local extraction, not web-tool lines)", "line_count": 6 }, { "evidence_id": "stage2_570_section_supplement_20261021_ev_wf_574dd67727c3812f00", "kind": "limit_packets", "url": "https://zenodo.org/api/records/22277308/files/2026-06-19-post-agi-layering-and-universal-multiversal-intelligence-adjacency_v7.md/content", "version": "captured source; no latest-version assertion", "locator": "Derived document section document_body, lines 176-181 (one-based local extraction, not web-tool lines)", "line_count": 6 }, { "evidence_id": "stage2_570_section_supplement_20261021_ev_wf_574dd67727c3812f00", "kind": "limit_packets", "url": "https://zenodo.org/api/records/22277308/files/2026-06-19-post-agi-layering-and-universal-multiversal-intelligence-adjacency_v7.md/content", "version": "captured source; no latest-version assertion", "locator": "Derived document section document_body, lines 310-315 (one-based local extraction, not web-tool lines)", "line_count": 6 } ], "content_characters": 7519, "evaluation_details_available": false }, { "work_family_id": "wf_664b95c176b562cd8c", "title": "Post-Training Probability Manifold Correction via Structured SVD Pruning and Self-Referential Distillation", "role": "R03_METHOD", "role_label": "\u65b9\u6cd5\u8bba\u6587", "year": 2026, "branch_id": "M4_selftraining", "path": "papers/r03_method/wf_664b95c176b562cd8c.md", "summary": "SparseKD \u4ee5\u538b\u7f29\u524d\u81ea\u8eab\u5206\u5e03\u4f5c\u6559\u5e08\uff0c\u7ed3\u5408\u7ed3\u6784\u5316 SVD \u526a\u679d\u4e0e\u540e\u8bad\u7ec3\u84b8\u998f\u6062\u590d\u8d28\u91cf\uff1b\u56fa\u5b9a\u6821\u51c6\u6570\u636e\u548c\u81ea\u6559\u5e08\u4e0d\u81ea\u52a8\u6784\u6210\u591a\u4ee3\u9012\u5f52\u6539\u8fdb\uff0c\u9700\u6838\u6559\u5e08\u662f\u5426\u968f\u8bad\u7ec3\u66f4\u65b0\u3002", "claim_ids": [ "configuration:cfg_sparsekd_fixed_cache_postprune_v1" ], "configuration_ids": [ "cfg_sparsekd_fixed_cache_postprune_v1", "cfg_sparsekd_iterative_recache_claim_v1" ], "relation_ids": [], "source_ids": [ "r6root_src_sparse", "r6root_src_sparsecaveat", "r6root_src_sparsereuse", "s18_role_src_4a72693a250c423c" ], "evidence_ids": [ "r6root_ev_cfg_sparsekd_fixed_cache_postprune_v1", "r6root_ev_cfg_sparsekd_iterative_recache_claim_v1", "s18_role_ev_wf_664b95c176b562cd8c" ], "evidence_lines": [ 412, 511, 512 ], "review_depths": [ "original_abstract", "selected_primary_methods_state_feedback_and_limits" ], "primary_passages": [], "content_characters": 5856, "evaluation_details_available": true }, { "work_family_id": "wf_86ec38bcc8d3db0f2b", "title": "Project Crescent: Autonomous Emergence of Identity, Memory, and Belief in Isolated Language Model Agents", "role": "R11_BACKGROUND", "role_label": "\u80cc\u666f/\u76f8\u90bb\u57fa\u7840", "year": 2026, "branch_id": "M3_harness", "path": "papers/r11_background/wf_86ec38bcc8d3db0f2b.md", "summary": "Describes iterative Llama 3.3 70B agent cycles in which an LLM compressor turns responses to fixed existential prompts into a 5\u201315-word identity signature used as the next cycle\u2019s system context. Textual relays and adversarial messages are interventions; reported persistence concerns the resulting textual identity state rather than acquired improvement machinery or foundation-model training. 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"work_family_id": "wf_fa483ce9e5ed4e5213", "title": "RSI Module (Prototype): Empirical Validation of a Standalone Recursive Self-Improvement Engine", "role": "R07_EMPIRICAL", "role_label": "\u5b9e\u8bc1/\u5ba1\u8ba1/\u590d\u73b0", "year": 2026, "branch_id": "M7_measurement", "path": "papers/r07_empirical/wf_fa483ce9e5ed4e5213.md", "summary": "\u73b0\u6709\u8bc1\u636e\u4e0d\u8db3\u4ee5\u5f62\u6210\u5177\u4f53\u5185\u5bb9\u89e3\u6790\uff1b\u4fdd\u7559\u8eab\u4efd\u548c\u5f85\u6838\u4f4d\u7f6e\u3002", "claim_ids": [], "configuration_ids": [], "relation_ids": [], "source_ids": [], "evidence_ids": [], "evidence_lines": [], "review_depths": [ "registry_abstract" ], "primary_passages": [], "content_characters": 997, "evaluation_details_available": false }, { "work_family_id": "wf_2f3f3c117d7c92f78a", "title": "RSI-Anything: Unlocking Recursive Self-Improvement with World Models", "role": "R04_SYSTEM", "role_label": "\u7efc\u5408\u7cfb\u7edf/\u667a\u80fd\u4f53", "year": 2026, "branch_id": "M3_harness", "path": "papers/r04_system/wf_2f3f3c117d7c92f78a.md", "summary": "Specified recursive protocol; empirical completeness is domain-dependent.; Predictor adapts within feedback rounds; cross-problem carryover/reset unresolved. Pending persistent-system RSI determination.", "claim_ids": [ "configuration:cfg_rsi_anything_code_predictor_adaptation", "configuration:cfg_rsi_anything_policy_model_protocol" ], "configuration_ids": [ "cfg_rsi_anything_policy_model_protocol", "cfg_rsi_anything_code_predictor_adaptation" ], "relation_ids": [], "source_ids": [ "r7root_src_rsi_code_within_round", "r7root_src_rsi_version_contract" ], "evidence_ids": [ "r7root_ev_cfg_rsi_anything_code_predictor_adaptation" ], "evidence_lines": [ 578 ], "review_depths": [ "targeted_primary_full_text_method_limits_pass" ], "primary_passages": [], "content_characters": 4362, "evaluation_details_available": false }, { "work_family_id": "wf_1365211c30d020c8cc", "title": "RSI-Forge: From Research Papers to Environments for Recursive Self-Improvement", "role": "R09_RESOURCE", "role_label": "\u57fa\u7840\u8bbe\u65bd/\u5de5\u5177/\u8bed\u6599\u8d44\u6e90", "year": 2026, "branch_id": "M6_research", "path": "papers/r09_resource/wf_1365211c30d020c8cc.md", "summary": "RSI-Forge constructs bounded research environments with reproduced baselines, held-out replay of saved versions, task-specific targets, and explicit candidate-method compute budgets.", "claim_ids": [ "role:b570_15_role_wf_1365211c30d020c8cc" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_15_src_wf_1365211c30d020c8cc" ], "evidence_ids": [ "b570_15_ev_wf_1365211c30d020c8cc" ], "evidence_lines": [ 985 ], "review_depths": [ "original_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_15_ev_wf_1365211c30d020c8cc", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2610.09426v1", "version": "2610.09426v1", "locator": "PDF physical page 4, derived page-text lines 13-18 (one-based local extraction, not web-tool lines)", "line_count": 6 }, { "evidence_id": "b570_15_ev_wf_1365211c30d020c8cc", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2610.09426v1", "version": "2610.09426v1", "locator": "PDF physical page 18, derived page-text lines 55-60 (one-based local extraction, not web-tool lines)", "line_count": 6 }, { "evidence_id": "b570_15_ev_wf_1365211c30d020c8cc", "kind": "limit_packets", "url": "https://arxiv.org/pdf/2610.09426v1", "version": "2610.09426v1", "locator": "PDF physical page 11, derived page-text lines 26-31 (one-based local extraction, not web-tool lines)", "line_count": 6 }, { "evidence_id": "b570_15_ev_wf_1365211c30d020c8cc", "kind": "limit_packets", "url": "https://arxiv.org/pdf/2610.09426v1", "version": "2610.09426v1", "locator": "PDF physical page 11, derived page-text lines 35-40 (one-based local extraction, not web-tool lines)", "line_count": 6 } ], "content_characters": 7386, "evaluation_details_available": false }, { "work_family_id": "wf_56ab8d89076370deb5", "title": "RSI-Master: Structuring Experiments to Guide Autonomous Model Improvement", "role": "R03_METHOD", "role_label": "\u65b9\u6cd5\u8bba\u6587", "year": 2026, "branch_id": "M4_selftraining", "path": "papers/r03_method/wf_56ab8d89076370deb5.md", "summary": "RSI-Master constrains actions through Experiment OS and persistent experiment records; reviewers use accumulated evidence to redirect worker branches under fixed evaluation and resource budgets. AI-guided model development uses previous experiment and review evidence to alter later training/data investigations; fixed evaluation bounds rather than removes feedback.", "claim_ids": [ "role:b570_07_role_wf_56ab8d89076370deb5" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_07_src_wf_56ab8d89076370deb5" ], "evidence_ids": [ "b570_07_ev_wf_56ab8d89076370deb5" ], "evidence_lines": [ 849 ], "review_depths": [ "original_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_07_ev_wf_56ab8d89076370deb5", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2609.35561v1", "version": "2609.35561v1", "locator": "PDF physical page 3, derived page-text lines 77-84 (one-based local extraction, not web-tool lines)", "line_count": 8 }, { "evidence_id": "b570_07_ev_wf_56ab8d89076370deb5", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2609.35561v1", "version": "2609.35561v1", "locator": "PDF physical page 4, derived page-text lines 1-13 (one-based local extraction, not web-tool lines)", "line_count": 13 }, { "evidence_id": "b570_07_ev_wf_56ab8d89076370deb5", "kind": "measurement_packets", "url": "https://arxiv.org/pdf/2609.35561v1", "version": "2609.35561v1", "locator": "PDF physical page 6, derived page-text lines 19-37 (one-based local extraction, not web-tool lines)", "line_count": 19 }, { "evidence_id": "b570_07_ev_wf_56ab8d89076370deb5", "kind": "limit_packets", "url": "https://arxiv.org/pdf/2609.35561v1", "version": "2609.35561v1", "locator": "PDF physical page 11, derived page-text lines 27-33 (one-based local extraction, not web-tool lines)", "line_count": 7 } ], "content_characters": 9620, "evaluation_details_available": true }, { "work_family_id": "wf_b601acb30675904b10", "title": "RSI-Router: Evolving Subtask-Level LLM Routing and Skills for Cost-Efficient Agents", "role": "R03_METHOD", "role_label": "\u65b9\u6cd5\u8bba\u6587", "year": 2026, "branch_id": "M2_reasoning", "path": "papers/r03_method/wf_b601acb30675904b10.md", "summary": "RSI-Router revises subtasks, model assignments and skills from training traces. Validation performance/cost selects a Pareto population; historical routers/skills guide later proposals. Bounded strategy-level feedback changes later router/skill proposals; six iterations are not six accepted gains or weight self-modification.", "claim_ids": [ "role:b570_07_role_wf_b601acb30675904b10" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_07_src_wf_b601acb30675904b10" ], "evidence_ids": [ "b570_07_ev_wf_b601acb30675904b10" ], "evidence_lines": [ 863 ], "review_depths": [ "original_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_07_ev_wf_b601acb30675904b10", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2609.34712v1", "version": "2609.34712v1", "locator": "PDF physical page 3, derived page-text lines 13-29 (one-based local extraction, not web-tool lines)", "line_count": 17 }, { "evidence_id": "b570_07_ev_wf_b601acb30675904b10", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2609.34712v1", "version": "2609.34712v1", "locator": "PDF physical page 5, derived page-text 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experiments by an initial model-and-harness system, evaluation protocol, permitted changes, and budget, with permissions separating harness-only, data-only, joint, and offline settings.", "claim_ids": [ "role:b570_15_role_wf_ef662fac1690d34592" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_15_src_wf_ef662fac1690d34592" ], "evidence_ids": [ "b570_15_ev_wf_ef662fac1690d34592" ], "evidence_lines": [ 988 ], "review_depths": [ "original_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_15_ev_wf_ef662fac1690d34592", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2610.10310v1", "version": "2610.10310v1", "locator": "PDF physical page 7, derived page-text lines 12-17 (one-based local extraction, not web-tool lines)", "line_count": 6 }, { "evidence_id": "b570_15_ev_wf_ef662fac1690d34592", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2610.10310v1", "version": "2610.10310v1", "locator": 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"limit_packets", "url": "https://zenodo.org/api/records/22866388/files/v0.81_ND_en_Rational_Surrender_Economy_zenodo_prm.pdf/content", "version": "captured source; no latest-version assertion", "locator": "PDF physical page 12, derived page-text lines 7-12 (one-based local extraction, not web-tool lines)", "line_count": 6 } ], "content_characters": 7412, "evaluation_details_available": false }, { "work_family_id": "wf_95f9020f5a6910dbff", "title": "ReSAIL: Mitigating Collapse in Iterative Agent Self-Distillation", "role": "R03_METHOD", "role_label": "\u65b9\u6cd5\u8bba\u6587", "year": 2026, "branch_id": "M4_selftraining", "path": "papers/r03_method/wf_95f9020f5a6910dbff.md", "summary": 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"content_characters": 9740, "evaluation_details_available": true }, { "work_family_id": "wf_64b1f1bb5a5cbc6bc4", "title": "ReSI: Recursive Safety Improvement toward Resistant and Resilient AI", "role": "R03_METHOD", "role_label": "\u65b9\u6cd5\u8bba\u6587", "year": 2026, "branch_id": "M4_selftraining", "path": "papers/r03_method/wf_64b1f1bb5a5cbc6bc4.md", "summary": "\u5728\u5f53\u524d\u5df2\u6838\u914d\u7f6e\u4e2d\uff0c\u7cfb\u7edf\u8fb9\u754c\u4e3a\uff1atarget M_t\u3001red-team methods\u548crecipe search/execution framework\u6784\u6210bounded system\uff1bGPT-4o external auto-research proposer\u4e0d\u7b49\u4e8etarget model\uff0cPareto gate\u4e0erefinement skills\u56fa\u5b9a\uff1b\u5177\u4f53\u66f4\u65b0\u8fc7\u7a0b\u4e3a\uff1a\u5f53\u524dtarget\u7684\u6210\u529f\u653b\u51fb\u8bf1\u5bfcattack dataset\uff1bauto-research\u6839\u636epilot\u4e0ePareto\u53cd\u9988\u6539data sources/method/mixture/hyperparameters\uff0c\u9009\u62e9\u5408\u683ctarget LoRA checkpoint\u8fdb\u5165\u4e0b\u4e00\u8f6ered teaming\uff1b\u66f4\u65b0\u72b6\u6001\u7684\u4fdd\u7559\u4e0e\u540e\u7ee7\u4f7f\u7528\u4e3a\uff1aaccepted target weights\u3001attack evidence\u3001recipe/trial\u7ed3\u679c\u8de8round\u4fdd\u7559\uff1b\u4e0b\u4e00\u8f6e\u653b\u51fb\u9762\u7531\u5df2\u66f4\u65b0target\u51b3\u5b9a\u3002", "claim_ids": [ "configuration:cfg_resi_target_lora_pareto_recipe_loop_v1" ], "configuration_ids": [ "cfg_resi_target_lora_pareto_recipe_loop_v1" ], "relation_ids": [], "source_ids": [ "r15_src_resi" ], "evidence_ids": [ "r15_ev_cfg_resi_target_lora_pareto_recipe_loop_v1" ], "evidence_lines": [ 699 ], "review_depths": [ "original_abstract", "targeted_primary_method_state_feedback_limits_20261019" ], "primary_passages": [], "content_characters": 6176, "evaluation_details_available": true }, { "work_family_id": "wf_434c1e20d5f35240df", "title": "ReVision3D: Attribution-Guided Recursive Self-Improvement for 3D Medical Perception", "role": "R03_METHOD", "role_label": "\u65b9\u6cd5\u8bba\u6587", "year": 2026, "branch_id": "M4_selftraining", "path": "papers/r03_method/wf_434c1e20d5f35240df.md", "summary": "\u5728\u5f53\u524d\u5df2\u6838\u914d\u7f6e\u4e2d\uff0c\u7cfb\u7edf\u8fb9\u754c\u4e3a\uff1a\u4eba\u5de5\u7f16\u6392\u7684\u51bb\u7ed3LLM designer\u3001\u5f53\u524d3D reader\u7cfb\u7edfSigma\u548c\u8de8\u4fee\u8ba2playbook\uff1b\u4e0d\u542b\u672a\u7ecf\u6267\u884c\u7684\u81ea\u52a8level 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Validity checks and per-operating-point fitting yield NRMSE rewards; reported implementation activates archive feedback and proposer RL. Context and weight RSI channels operate within fixed grammar/evaluator.", "claim_ids": [ "role:b570_07_role_wf_9b011afe59d4ab6e96" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_07_src_wf_9b011afe59d4ab6e96" ], "evidence_ids": [ "b570_07_ev_wf_9b011afe59d4ab6e96" ], "evidence_lines": [ 858 ], "review_depths": [ "original_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_07_ev_wf_9b011afe59d4ab6e96", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2609.14260v1", "version": "2609.14260v1", "locator": "PDF physical page 3, derived page-text lines 39-44 (one-based local extraction, not web-tool lines)", "line_count": 6 }, { "evidence_id": "b570_07_ev_wf_9b011afe59d4ab6e96", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2609.14260v1", "version": "2609.14260v1", "locator": "PDF physical page 8, derived page-text lines 83-98 (one-based local extraction, not web-tool lines)", "line_count": 16 }, { "evidence_id": "b570_07_ev_wf_9b011afe59d4ab6e96", "kind": "measurement_packets", "url": "https://arxiv.org/pdf/2609.14260v1", "version": "2609.14260v1", "locator": "PDF physical page 7, derived page-text lines 43-52 (one-based local extraction, not web-tool lines)", "line_count": 10 }, { "evidence_id": "b570_07_ev_wf_9b011afe59d4ab6e96", "kind": "limit_packets", "url": "https://arxiv.org/pdf/2609.14260v1", "version": "2609.14260v1", "locator": "PDF physical page 10, derived page-text lines 13-39 (one-based local extraction, not web-tool lines)", "line_count": 27 } ], "content_characters": 9633, "evaluation_details_available": true }, { "work_family_id": "wf_d3207ade751ef01cf4", "title": "Recursive Self-Improvement Stability under Endogenous Yardstick Drift", "role": "R11_BACKGROUND", "role_label": "\u80cc\u666f/\u76f8\u90bb\u57fa\u7840", "year": 2026, "branch_id": "M4_selftraining", "path": "papers/r11_background/wf_d3207ade751ef01cf4.md", "summary": "The paper formalizes admission and stability conditions for self-modifying systems when both the improved object and evaluator drift, using replayable comparator witnesses, protected lanes, liability accounting, and conservative envelopes.", "claim_ids": [ "role:b570_21_role_wf_d3207ade751ef01cf4" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_21_src_wf_d3207ade751ef01cf4" ], "evidence_ids": [ "b570_21_ev_wf_d3207ade751ef01cf4" ], "evidence_lines": [ 1114 ], "review_depths": [ "registry_abstract", "selected_primary_role_definition_protocol_scope_and_limits", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_21_ev_wf_d3207ade751ef01cf4", "kind": "claim_packets", "url": "https://zenodo.org/api/records/19044634/files/Recursive%20Self-Improvement%20Stability%20under%20Endogenous%20Yardstick%20Drift.pdf/content", "version": "captured source; 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no latest-version assertion", "locator": "PDF physical page 11, derived page-text lines 50-55 (one-based local extraction, not web-tool lines)", "line_count": 6 }, { "evidence_id": "b570_14_ev_wf_f19801c41191964e71", "kind": "claim_packets", "url": "https://zenodo.org/api/records/22117763/files/Recursive_Self_Improvement_and_the_Binding_Condition.pdf/content", "version": "captured source; no latest-version assertion", "locator": "PDF physical page 7, derived page-text lines 12-17 (one-based local extraction, not web-tool lines)", "line_count": 6 }, { "evidence_id": "b570_14_ev_wf_f19801c41191964e71", "kind": "limit_packets", "url": "https://zenodo.org/api/records/22117763/files/Recursive_Self_Improvement_and_the_Binding_Condition.pdf/content", "version": "captured source; no latest-version assertion", "locator": "PDF physical page 12, derived page-text lines 19-24 (one-based local extraction, not web-tool lines)", "line_count": 6 }, { "evidence_id": "b570_14_ev_wf_f19801c41191964e71", "kind": "limit_packets", "url": "https://zenodo.org/api/records/22117763/files/Recursive_Self_Improvement_and_the_Binding_Condition.pdf/content", "version": "captured source; 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Configurable heuristics and cooldowns trigger succession; predecessor sessions and quality metrics are archived. Direct bounded system bridge: evaluated experience changes successor prompts/specifications and inherited memory used in future sessions without requiring model weight changes. 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State updates require synchronous solvency compliance before commit. Proposals require positive predicted score delta above threshold and full invariant checks before application. Explicit RSI terminology denotes a bounded settlement optimization feedback loop; the formal architecture role is retained.", "claim_ids": [ "role:b570_11_role_wf_title_c325c3bc100fb66c" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_11_src_wf_title_c325c3bc100fb66c" ], "evidence_ids": [ "b570_11_ev_wf_title_c325c3bc100fb66c" ], "evidence_lines": [ 927 ], "review_depths": [ "registry_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_11_ev_wf_title_c325c3bc100fb66c", "kind": "claim_packets", "url": "https://zenodo.org/api/records/21748182/files/Recursive%20Self-Improvement%20in%20Settlement%20Operating%20Systems%20%E2%80%94%20A%20Formal%20Architecture%20Description%20(1).pdf/content", "version": "Zenodo 21748182 / v1.0", "locator": "PDF physical page 17, derived page-text lines 1-11 (one-based local extraction, not web-tool lines)", "line_count": 11 }, { "evidence_id": "b570_11_ev_wf_title_c325c3bc100fb66c", "kind": "claim_packets", "url": "https://zenodo.org/api/records/21748182/files/Recursive%20Self-Improvement%20in%20Settlement%20Operating%20Systems%20%E2%80%94%20A%20Formal%20Architecture%20Description%20(1).pdf/content", "version": "Zenodo 21748182 / v1.0", "locator": "PDF physical page 17, derived page-text lines 14-26 (one-based local extraction, not web-tool lines)", "line_count": 13 }, { "evidence_id": "b570_11_ev_wf_title_c325c3bc100fb66c", "kind": "claim_packets", "url": "https://zenodo.org/api/records/21748182/files/Recursive%20Self-Improvement%20in%20Settlement%20Operating%20Systems%20%E2%80%94%20A%20Formal%20Architecture%20Description%20(1).pdf/content", "version": "Zenodo 21748182 / v1.0", "locator": "PDF physical page 31, derived page-text lines 7-25 (one-based local extraction, not web-tool lines)", "line_count": 19 }, { "evidence_id": "b570_11_ev_wf_title_c325c3bc100fb66c", "kind": "limit_packets", "url": "https://zenodo.org/api/records/21748182/files/Recursive%20Self-Improvement%20in%20Settlement%20Operating%20Systems%20%E2%80%94%20A%20Formal%20Architecture%20Description%20(1).pdf/content", "version": "Zenodo 21748182 / v1.0", "locator": "PDF physical page 35, derived page-text lines 4-21 (one-based local extraction, not web-tool lines)", "line_count": 18 } ], "content_characters": 11542, "evaluation_details_available": true }, { "work_family_id": "wf_27e4243aa4b5eee002", "title": "Recursive Self-Improvement in Unified Multimodal Models", "role": "R03_METHOD", "role_label": "\u65b9\u6cd5\u8bba\u6587", "year": 2026, "branch_id": "M4_selftraining", "path": "papers/r03_method/wf_27e4243aa4b5eee002.md", "summary": "\u5728\u5f53\u524d\u5df2\u6838\u914d\u7f6e\u4e2d\uff0c\u7cfb\u7edf\u8fb9\u754c\u4e3a\uff1aOne unified multimodal model provides a generator, visual reader, and program writer; generation parameters are separate from understanding parameters, which the reader and writer share\uff1b\u5177\u4f53\u66f4\u65b0\u8fc7\u7a0b\u4e3a\uff1aThe reader diagnoses generator errors; fixed rules convert diagnosis into a specification distribution. The writer creates programs; execution records verify rendering targets and understanding labels. Separate generation and understanding updates from M_t are merged into M_(t+1), which repeats diagnosis and data construction\uff1b\u66f4\u65b0\u72b6\u6001\u7684\u4fdd\u7559\u4e0e\u540e\u7ee7\u4f7f\u7528\u4e3a\uff1aMerged generation and understanding checkpoints persist across rounds; data history influences subsequent fixed sampling rules\u3002", "claim_ids": [ "configuration:cfg_multimodal_cross_training" ], "configuration_ids": [ "cfg_multimodal_cross_training" ], "relation_ids": [ "r2_multimodal_rsi_absolute_zero_15" ], "source_ids": [ "expand_src_w_multimodal_rsi", "oct15_training_src_w_multimodal_rsi" ], "evidence_ids": [ "e2_w_multimodal_rsi", "oct15_training_ev_w_multimodal_rsi" ], "evidence_lines": [ 143, 240 ], "review_depths": [ "original_abstract", "selected_method_passages_with_abstract_baseline" ], "primary_passages": [], "content_characters": 6893, "evaluation_details_available": false }, { "work_family_id": "wf_a7c55e2196d2e1c9dc", "title": "Recursive Self-Improvement 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"configuration:cfg_opsd_tts_wait_inference_control_v1" ], "configuration_ids": [ "cfg_dce_srcl_dynamic_privileged_teacher_v1", "cfg_opsd_tts_wait_inference_control_v1" ], "relation_ids": [], "source_ids": [ "r5dist_src_dce", "s18ctx_role_src_cf0403161b25df87" ], "evidence_ids": [ "r5dist_ev_cfg_dce_srcl_dynamic_privileged_teacher_v1", "r5dist_ev_cfg_opsd_tts_wait_inference_control_v1", "s18ctx_role_ev_wf_9e52af299c264dd718" ], "evidence_lines": [ 448, 500, 501 ], "review_depths": [ "original_abstract", "selected_primary_method_parameter_carry_teacher_refresh_and_limits" ], "primary_passages": [], "content_characters": 9044, "evaluation_details_available": true }, { "work_family_id": "wf_e3e16b5ba1d1ab4b5d", "title": "Recursive Self-improvement: Geometry and Entropy Backstop", "role": "R11_BACKGROUND", "role_label": "\u80cc\u666f/\u76f8\u90bb\u57fa\u7840", "year": 2026, "branch_id": "M0_definitions", "path": "papers/r11_background/wf_e3e16b5ba1d1ab4b5d.md", "summary": 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"wf_b869a4b8b6d400631b", "title": "Recursivism: An Artistic Paradigm for Self-Transforming Art in the Age of AI", "role": "R11_BACKGROUND", "role_label": "\u80cc\u666f/\u76f8\u90bb\u57fa\u7840", "year": 2026, "branch_id": "M0_historical", "path": "papers/r11_background/wf_b869a4b8b6d400631b.md", "summary": "Recursivism distinguishes variation within a fixed generative rule from reflexive systems that act on their own rules and documents recursive parameters, versions, and landmark states.", "claim_ids": [ "role:b570_17_role_wf_b869a4b8b6d400631b" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_17_src_wf_b869a4b8b6d400631b" ], "evidence_ids": [ "b570_17_ev_wf_b869a4b8b6d400631b" ], "evidence_lines": [ 1031 ], "review_depths": [ "original_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_17_ev_wf_b869a4b8b6d400631b", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2601.14401v1", "version": 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The paper also reports deploying Qwen3 on the resulting accelerator and using its repeated samples for timing and kernel improvements to a future accelerator generation. Explicit hardware RSI bridge: compute built through the AI design process hosts a model whose outputs feed later hardware/kernel design. 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Persistent investigation records connect hypotheses, checks and open questions; revised workflows return to ordinary outer validation, and only accepted edits change the active system. Direct RSI bridge: diagnosis alters the workflow generating future task solutions, accepted patches persist, and investigation memory is reused across rounds. Detector calls and investigations consume the shared research budget.", "claim_ids": [ "role:b570_08_role_wf_e9f122a2894f7e26f7" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_08_src_wf_e9f122a2894f7e26f7" ], "evidence_ids": [ "b570_08_ev_wf_e9f122a2894f7e26f7" ], "evidence_lines": [ 868 ], "review_depths": [ "original_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_08_ev_wf_e9f122a2894f7e26f7", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2610.05701v1", "version": "2610.05701v1", "locator": "PDF physical page 5, derived page-text lines 29-38 (one-based local extraction, not web-tool lines)", "line_count": 10 }, { "evidence_id": "b570_08_ev_wf_e9f122a2894f7e26f7", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2610.05701v1", "version": "2610.05701v1", "locator": "PDF physical page 6, derived page-text lines 5-16 (one-based local extraction, not web-tool lines)", "line_count": 12 }, { "evidence_id": "b570_08_ev_wf_e9f122a2894f7e26f7", "kind": "limit_packets", "url": "https://arxiv.org/pdf/2610.05701v1", "version": "2610.05701v1", "locator": "PDF physical page 10, derived page-text lines 4-6 (one-based local extraction, not web-tool lines)", "line_count": 3 } ], "content_characters": 8035, "evaluation_details_available": true }, { "work_family_id": "wf_436510d6c536261fde", "title": "SeisEvo: Evolution of Seismic Data Reconstruction Algorithms by Agents", "role": "R11_BACKGROUND", "role_label": "\u80cc\u666f/\u76f8\u90bb\u57fa\u7840", "year": 2026, "branch_id": "M3_harness", "path": "papers/r11_background/wf_436510d6c536261fde.md", "summary": "SeisEvo has agents generate, execute, score, and retain white-box seismic reconstruction algorithms within user-opened components, then evaluates the discovered standalone algorithm outside the search sub-blocks.", "claim_ids": [ "role:b570_16_role_wf_436510d6c536261fde" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_16_src_wf_436510d6c536261fde" ], "evidence_ids": [ "b570_16_ev_wf_436510d6c536261fde" ], "evidence_lines": [ 1021 ], "review_depths": [ "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_16_ev_wf_436510d6c536261fde", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2608.18272v1", "version": "2608.18272v1", "locator": "PDF physical page 1, derived page-text lines 15-20 (one-based local extraction, not web-tool lines)", "line_count": 6 }, { "evidence_id": "b570_16_ev_wf_436510d6c536261fde", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2608.18272v1", "version": "2608.18272v1", "locator": "PDF physical page 8, derived page-text lines 28-33 (one-based local extraction, not web-tool lines)", "line_count": 6 }, { "evidence_id": "b570_16_ev_wf_436510d6c536261fde", "kind": "limit_packets", "url": "https://arxiv.org/pdf/2608.18272v1", "version": "2608.18272v1", "locator": "PDF physical page 14, derived page-text lines 135-140 (one-based local extraction, not web-tool lines)", "line_count": 6 }, { "evidence_id": "b570_16_ev_wf_436510d6c536261fde", "kind": "limit_packets", "url": "https://arxiv.org/pdf/2608.18272v1", "version": "2608.18272v1", "locator": "PDF physical page 19, derived page-text lines 16-21 (one-based local extraction, not web-tool lines)", "line_count": 6 } ], "content_characters": 7348, "evaluation_details_available": false }, { "work_family_id": "wf_47bcd5ede204b7a80f", "title": "Self Improvement via Fast Tree-search", "role": "R03_METHOD", "role_label": "\u65b9\u6cd5\u8bba\u6587", "year": 2026, "branch_id": "M3_workflow", "path": "papers/r03_method/wf_47bcd5ede204b7a80f.md", "summary": "SIFT maintains candidate harnesses, aggregates pairwise judge preferences into regularized Bradley-Terry strengths, and samples/queues candidates using judge rank, benchmark accuracy rank and visit count. 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For each accepted change \u00a712 requests held-out performance deltas and uncertainty, regression results, authority/resource deltas, transfer, rollback and gain per budget; meta-improvement compares descendant productivity under equal budgets. Direct RSI evaluation proposal; admissible as proposed protocol, not as an executed benchmark or measured RSI gain.", "claim_ids": [ "role:b570_02_role_wf_51e29775b8249de640" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_02_src_wf_51e29775b8249de640" ], "evidence_ids": [ "b570_02_ev_wf_51e29775b8249de640" ], "evidence_lines": [ 734 ], "review_depths": [ "original_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_02_ev_wf_51e29775b8249de640", "kind": "claim_packets", "url": "https://arxiv.org/html/2607.12254v1", "version": "2607.12254v1", "locator": "tool L601\u2013643", "line_count": 43 }, { "evidence_id": "b570_02_ev_wf_51e29775b8249de640", "kind": "limit_packets", "url": "https://arxiv.org/html/2607.12254v1", "version": "2607.12254v1", "locator": "tool L719\u2013737", "line_count": 19 } ], "content_characters": 11461, "evaluation_details_available": false }, { "work_family_id": "wf_d774ae4f1af498b39c", "title": "Self-Evolving Agent Harness", "role": "R02_CONCEPT", "role_label": "\u6982\u5ff5/\u7406\u8bba/\u7acb\u573a", "year": 2026, "branch_id": "M0_definitions", "path": "papers/r02_concept/wf_d774ae4f1af498b39c.md", "summary": "Definition, taxonomy and conditional theory; formal lifecycle is not counted as a separately demonstrated empirical self-improving system.", "claim_ids": [ "configuration:cfg_harness_rsi_formal_lifecycle" ], "configuration_ids": [ "cfg_harness_rsi_formal_lifecycle" ], "relation_ids": [ "oct15_harness_expel_interpreted_boundary" ], "source_ids": [ "oct15_intake_src_2610_18813v1_html", "oct15_intake_src_2610_18813v1_pdf" ], "evidence_ids": [ "oct15_intake_ev_harness_conditional_theory", "oct15_intake_ev_harness_lifecycle" ], "evidence_lines": [ 261, 262 ], "review_depths": [ "targeted_primary_fulltext_method_configuration_and_limits" ], "primary_passages": [], "content_characters": 5673, "evaluation_details_available": false }, { "work_family_id": "wf_743b74b7d2490dd37a", "title": "Self-Evolving Agents with Anytime-Valid Certificates", "role": "R07_EMPIRICAL", "role_label": "\u5b9e\u8bc1/\u5ba1\u8ba1/\u590d\u73b0", "year": 2026, "branch_id": "M3_harness", "path": "papers/r07_empirical/wf_743b74b7d2490dd37a.md", "summary": "SEA confines evolution to directive steering and a mutable harness around frozen L0, with self-authored reproduction-oracle search. The executed study uses a seeded 52-instance SWE-bench Verified subset and official fail-to-pass/pass-to-pass terminal grading; gold tests are reserved for terminal evaluation. No-op controls isolate reported +5 resolved on GPT and +4 on GLM. Full-stack ALG4 is disabled; its isolated 36 score accepted zero edits. Direct bounded self-evolution: separate directive learning and debugging from accepted harness edits and unimplemented slow-weight training.", "claim_ids": [ "role:b570_03_role_wf_743b74b7d2490dd37a" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_03_src_wf_743b74b7d2490dd37a" ], "evidence_ids": [ "b570_03_ev_wf_743b74b7d2490dd37a" ], "evidence_lines": [ 763 ], "review_depths": [ "original_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_03_ev_wf_743b74b7d2490dd37a", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2607.00871v1", "version": "2607.00871v1", "locator": "PDF p16; derived page text lines 9\u201319 (one-based local extraction, not web-tool lines)", "line_count": 11 }, { "evidence_id": "b570_03_ev_wf_743b74b7d2490dd37a", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2607.00871v1", "version": "2607.00871v1", "locator": "PDF p16; derived page text lines 46\u201354 (one-based local extraction, not web-tool lines)", "line_count": 9 }, { "evidence_id": "b570_03_ev_wf_743b74b7d2490dd37a", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2607.00871v1", "version": "2607.00871v1", "locator": "PDF p17; derived page text lines 13\u201349 (one-based local extraction, not web-tool lines)", "line_count": 37 }, { "evidence_id": "b570_03_ev_wf_743b74b7d2490dd37a", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2607.00871v1", "version": "2607.00871v1", "locator": "PDF p19; derived page text lines 17\u201341 (one-based local extraction, not web-tool lines)", "line_count": 25 }, { "evidence_id": "b570_03_ev_wf_743b74b7d2490dd37a", "kind": "limit_packets", "url": "https://arxiv.org/pdf/2607.00871v1", "version": "2607.00871v1", "locator": "PDF p19; derived page text lines 42\u201349 (one-based local extraction, not web-tool lines)", "line_count": 8 }, { "evidence_id": "b570_03_ev_wf_743b74b7d2490dd37a", "kind": "limit_packets", "url": "https://arxiv.org/pdf/2607.00871v1", "version": "2607.00871v1", "locator": "PDF p20; derived page text lines 1\u201322 (one-based local extraction, not web-tool lines)", "line_count": 22 } ], "content_characters": 17532, "evaluation_details_available": false }, { "work_family_id": "wf_44bd5792246352ee28", "title": "Self-Evolving Coding Agents", "role": "R01_SURVEY", "role_label": "\u7efc\u8ff0/\u7cfb\u7edf\u7efc\u8ff0/\u8c31\u7cfb", "year": 2026, "branch_id": "M0_surveys", "path": "papers/r01_survey/wf_44bd5792246352ee28.md", "summary": "\u7f16\u7801\u4ee3\u7406\u6309\u6846\u67b6\u3001\u8bb0\u5fc6\u3001\u6280\u80fd\u5de5\u5177\u3001\u6a21\u578b\u3001\u5de5\u4f5c\u6d41/\u62d3\u6251\u4e94\u4e2a\u4fee\u6539\u5bf9\u8c61\u5206\u7c7b\uff0c\u5e76\u533a\u5206task-time/post-task/stage-wise\u3002", "claim_ids": [ "role:r3_role_review_w_survey_coding" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "r3_root_src_w_survey_coding" ], "evidence_ids": [ "r3_role_ev_w_survey_coding" ], "evidence_lines": [ 267 ], "review_depths": [ "primary_abstract_or_official_summary_review" ], "primary_passages": [], "content_characters": 1638, "evaluation_details_available": false }, { "work_family_id": "wf_8015bdf396efb2c662", "title": "Self-Evolving Coding Rules for AI Coding Agents", "role": "R03_METHOD", "role_label": "\u65b9\u6cd5\u8bba\u6587", "year": 2026, "branch_id": "M3_harness", "path": "papers/r03_method/wf_8015bdf396efb2c662.md", "summary": "\u5728\u5f53\u524d\u5df2\u6838\u914d\u7f6e\u4e2d\uff0c\u7cfb\u7edf\u8fb9\u754c\u4e3a\uff1aAGENTS.md\u89c4\u5219\u6c60\u4e0e\u5206\u914d\u7edf\u8ba1\uff1b\u5177\u4f53\u66f4\u65b0\u8fc7\u7a0b\u4e3a\uff1a\u56fa\u5b9amutator\u5c06\u89c4\u5219\u4f5c\u4e3a\u88ab\u6539\u5bf9\u8c61\uff0cjudge\u6267\u884c\u4efb\u52a1\u4ee3\u7801\uff1b\u5386\u53f2\u7edf\u8ba1\u8c03\u6574\u53d8\u5f02\u4efd\u989d\uff1b\u66f4\u65b0\u72b6\u6001\u7684\u4fdd\u7559\u4e0e\u540e\u7ee7\u4f7f\u7528\u4e3a\uff1a\u8de8\u89c4\u5219\u6c60\u8fed\u4ee3\u3002", "claim_ids": [ "configuration:cfg_ruleevolve" ], "configuration_ids": [ "cfg_ruleevolve" ], "relation_ids": [], "source_ids": [ "expand_src_w_ruleevolve" ], "evidence_ids": [ "e2_w_ruleevolve" ], "evidence_lines": [ 137 ], "review_depths": [ "original_abstract", "selected_method_passages_with_abstract_baseline" ], "primary_passages": [], "content_characters": 2747, "evaluation_details_available": false }, { "work_family_id": "wf_2bb14b1fcba7021f66", "title": "Self-Evolving Coordination Protocol in Multi-Agent AI Systems: An Exploratory Systems Feasibility Study", "role": "R04_SYSTEM", "role_label": "\u7efc\u5408\u7cfb\u7edf/\u667a\u80fd\u4f53", "year": 2026, "branch_id": "M3_harness", "path": "papers/r04_system/wf_2bb14b1fcba7021f66.md", "summary": "SECP explores a governed one-step revision of a multi-agent coordination rule. Fixed module assessments pass external feasibility checks into deterministic acceptance logic; Pareto filtering, bounded minimax regret, scheduled support thresholds and constructive-objection resolution define a coordination protocol with an audit trail. Direct bounded RSI bridge: the coordination mechanism governing later proposal decisions can be revised under fixed invariants. 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Acceptance checks engineering validity including a mock task run rather than guaranteed score gain. Direct harness-level RSI: the updated harness runs both later task solving and later modification sessions. 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An experience-conditioned teacher rescoring branch supplies reverse-KL on-policy self-distillation targets; the resulting student is further optimized by GRPO\uff1b\u66f4\u65b0\u72b6\u6001\u7684\u4fdd\u7559\u4e0e\u540e\u7ee7\u4f7f\u7528\u4e3a\uff1aThe experience pool persists across its evolution rounds; distilled policy parameters persist through the training stages. Full repeated end-to-end training loops remain a future empirical question in v2\u3002", "claim_ids": [ "configuration:cfg_spee" ], "configuration_ids": [ "cfg_spee" ], "relation_ids": [ "r2_spee_expel_9", "oct15_skillscaffold_spee_training_boundary" ], "source_ids": [ "expand_src_w_spee", "oct15_training_src_w_spee" ], "evidence_ids": [ "e2_w_spee", "oct15_training_ev_w_spee" ], "evidence_lines": [ 134, 241 ], "review_depths": [ "selected_method_passages_with_abstract_baseline" ], "primary_passages": [], "content_characters": 8588, "evaluation_details_available": false }, { "work_family_id": "wf_77a8efbe895d31a964", "title": "Self-Modifying Agent Harnesses and Assurance Continuity: RRSI as an Illustrative Case", "role": "R02_CONCEPT", "role_label": "\u6982\u5ff5/\u7406\u8bba/\u7acb\u573a", "year": 2026, "branch_id": "M3_harness", "path": "papers/r02_concept/wf_77a8efbe895d31a964.md", "summary": "Defines standing assurance as an interval claim and distinguishes snapshot evidence from transition assurance. 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}, { "evidence_id": "b570_12_ev_wf_c75faed555371fb19b", "kind": "limit_packets", "url": "https://arxiv.org/pdf/2607.04277v1", "version": "2607.04277v1", "locator": "PDF physical page 16, derived page-text lines 37-42 (one-based local extraction, not web-tool lines)", "line_count": 6 } ], "content_characters": 9126, "evaluation_details_available": false }, { "work_family_id": "wf_62694237b7a10dd235", "title": "Self-Referential $K$-SAT and the Finite Analogue of G\u00f6del's Incompleteness Theorem", "role": "R11_BACKGROUND", "role_label": "\u80cc\u666f/\u76f8\u90bb\u57fa\u7840", "year": 2026, "branch_id": "M0_historical", "path": "papers/r11_background/wf_62694237b7a10dd235.md", "summary": "Original-body scope review identifies finite self-referential Boolean formula pairs and local-versus-global satisfiability evaluation as the research object. Excluded after primary scope reading: mathematical self-reference alone supplies no explicit RSI bridge in these passages.", "claim_ids": [], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_11_src_wf_62694237b7a10dd235", "s18ctx_role_src_4a72693a250c423c" ], "evidence_ids": [ "b570_11_ev_wf_62694237b7a10dd235", "s18ctx_role_ev_wf_62694237b7a10dd235" ], "evidence_lines": [ 450, 945 ], "review_depths": [ "original_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_11_ev_wf_62694237b7a10dd235", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2607.01671v1", "version": "arXiv:2607.01671v1", "locator": "PDF physical page 3, derived page-text lines 1-11 (one-based local extraction, not web-tool lines)", "line_count": 11 }, { "evidence_id": "b570_11_ev_wf_62694237b7a10dd235", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2607.01671v1", "version": "arXiv:2607.01671v1", "locator": "PDF physical page 3, derived page-text lines 18-23 (one-based local extraction, not web-tool lines)", "line_count": 6 }, { "evidence_id": "b570_11_ev_wf_62694237b7a10dd235", "kind": "limit_packets", "url": "https://arxiv.org/pdf/2607.01671v1", "version": "arXiv:2607.01671v1", "locator": "PDF physical page 26, derived page-text lines 10-19 (one-based local extraction, not web-tool lines)", "line_count": 10 } ], "content_characters": 7841, "evaluation_details_available": true }, { "work_family_id": "wf_692c4c6a3923958c1c", "title": "Self-Referential Induction Increases Response Instability Relative to Unresolvable and Verifiable Questions in Large Language Models", "role": "R06_EVALUATION", "role_label": "\u8bc4\u6d4b\u65b9\u6cd5/\u6d4b\u91cf\u79d1\u5b66", "year": 2026, "branch_id": "M0_historical", "path": "papers/r06_evaluation/wf_692c4c6a3923958c1c.md", "summary": "The experiment compares self-referential induction, unresolved philosophy and verifiable questions using independent fresh conversations; response claims are compressed and embedded, with instability defined as one minus mean within-question pairwise cosine similarity. Retained as bounded evaluation evidence on self-report stability relevant to introspective feedback; no persistent self-improving agent demonstrated.", "claim_ids": [ "role:b570_11_role_wf_692c4c6a3923958c1c" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_11_src_wf_692c4c6a3923958c1c" ], "evidence_ids": [ "b570_11_ev_wf_692c4c6a3923958c1c" ], "evidence_lines": [ 947 ], "review_depths": [ "original_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_11_ev_wf_692c4c6a3923958c1c", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2608.13258v1", "version": "arXiv:2608.13258v1", "locator": "PDF physical page 2, derived page-text lines 22-41 (one-based local extraction, not web-tool lines)", "line_count": 20 }, { "evidence_id": "b570_11_ev_wf_692c4c6a3923958c1c", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2608.13258v1", "version": "arXiv:2608.13258v1", "locator": "PDF physical page 3, derived page-text lines 14-20 (one-based local extraction, not web-tool lines)", "line_count": 7 }, { "evidence_id": "b570_11_ev_wf_692c4c6a3923958c1c", "kind": "limit_packets", "url": "https://arxiv.org/pdf/2608.13258v1", "version": "arXiv:2608.13258v1", "locator": "PDF physical page 5, derived page-text lines 11-17 (one-based local extraction, not web-tool lines)", "line_count": 7 }, { "evidence_id": "b570_11_ev_wf_692c4c6a3923958c1c", "kind": "limit_packets", "url": "https://arxiv.org/pdf/2608.13258v1", "version": "arXiv:2608.13258v1", "locator": "PDF physical page 6, derived page-text lines 1-14 (one-based local extraction, not web-tool lines)", "line_count": 14 } ], "content_characters": 10837, "evaluation_details_available": true }, { "work_family_id": "wf_536343aad11ed1b271", "title": "Self-Referential Introspection in Large Language Models: The Critical Threshold for Recursive Self-Improvement", "role": "R02_CONCEPT", "role_label": "\u6982\u5ff5/\u7406\u8bba/\u7acb\u573a", "year": 2026, "branch_id": "M0_definitions", "path": "papers/r02_concept/wf_536343aad11ed1b271.md", "summary": "The article formalizes an introspection threshold for autonomous RSI and surveys how current LLM evidence falls short of complete self-access and self-modification.", "claim_ids": [ "role:b570_28_role_wf_536343aad11ed1b271" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_28_src_wf_536343aad11ed1b271" ], "evidence_ids": [ "b570_28_ev_wf_536343aad11ed1b271" ], "evidence_lines": [ 1217 ], "review_depths": [ "registry_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_28_ev_wf_536343aad11ed1b271", "kind": "claim_packets", "url": "https://www.mdpi.com/1099-4300/28/9/951", "version": "Entropy 28(9), 951, published 2026-09", "locator": "Official publisher HTML via web-tool extraction, lines 1-5", "line_count": 5 }, { "evidence_id": "b570_28_ev_wf_536343aad11ed1b271", "kind": "limit_packets", "url": "https://www.mdpi.com/1099-4300/28/9/951", "version": "Entropy 28(9), 951, published 2026-09", "locator": "Official publisher HTML via web-tool extraction, lines 6-8", "line_count": 3 } ], "content_characters": 4623, "evaluation_details_available": false }, { "work_family_id": "wf_adbca0d0a2f259a14d", "title": "Self-discovering RL in the Era of Experience: Is Learning History an Asset or a Burden?", "role": "R07_EMPIRICAL", "role_label": "\u5b9e\u8bc1/\u5ba1\u8ba1/\u590d\u73b0", "year": 2026, "branch_id": "M7_measurement", "path": "papers/r07_empirical/wf_adbca0d0a2f259a14d.md", "summary": "This is a causal audit of released Disco103, not a new algorithm-discovery run. Zero-pinning, timed freezing, transplantation and clamping separate recurrent history from continuing agent-parameter learning across reward scaling, streams, change and sparse exploration. Controlled seeded assays show task-dependent state support and mismatch costs; matching replay capacity reverses an apparent adaptation advantage. 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Successor code runs next episode; two lineages share records without downstream benchmark search feedback. 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skill-free deployed policy alone is adjacent.", "claim_ids": [ "configuration:cfg_skillscaffold_anchor_rl", "configuration:cfg_skillscaffold_group_rl", "configuration:cfg_skillscaffold_skillfree_deployment" ], "configuration_ids": [ "cfg_skillscaffold_group_rl", "cfg_skillscaffold_anchor_rl", "cfg_skillscaffold_skillfree_deployment" ], "relation_ids": [ "oct15_skillscaffold_spee_training_boundary", "r4_memory_cfg_skillforge_skill_policy_training_cfg_skillscaffold_group_rl" ], "source_ids": [ "oct15_intake_src_2610_18807v1_html", "r4_memory_src_skillscaffold_existing" ], "evidence_ids": [ "oct15_intake_ev_skillscaffold_method", "r4_memory_ev_skillscaffold_existing" ], "evidence_lines": [ 260, 336 ], "review_depths": [ "targeted_primary_fulltext_method_configuration_and_limits" ], "primary_passages": [], "content_characters": 9472, "evaluation_details_available": true }, { "work_family_id": "wf_78f6b1fa610432baa7", "title": "SkillVine: Agent Skill Evolution via Branching Exploration", "role": "R03_METHOD", "role_label": "\u65b9\u6cd5\u8bba\u6587", "year": 2026, "branch_id": "M3_tools", "path": "papers/r03_method/wf_78f6b1fa610432baa7.md", "summary": "\u5728\u5f53\u524d\u5df2\u6838\u914d\u7f6e\u4e2d\uff0c\u7cfb\u7edf\u8fb9\u754c\u4e3a\uff1a\u6280\u80fd\u5e93\u7248\u672c\u548c\u5176\u4efb\u52a1\u8f68\u8ff9\uff1b\u5177\u4f53\u66f4\u65b0\u8fc7\u7a0b\u4e3a\uff1aEq1\u660e\u786e\u66f4\u65b0\u5668U\u63a5\u6536\u5e93\u53ca\u8be5\u5e93\u6267\u884c\u6240\u5f97\u8f68\u8ff9\uff1b\u4e3b\u5e72\u53cd\u9988\u63a7\u5236\u5206\u652f\u7c92\u5ea6\uff1b\u66f4\u65b0\u72b6\u6001\u7684\u4fdd\u7559\u4e0e\u540e\u7ee7\u4f7f\u7528\u4e3a\uff1a\u8de8\u6280\u80fd\u641c\u7d22\u8f6e\u6b21\u3002", "claim_ids": [ "configuration:cfg_skillvine_branching" ], "configuration_ids": [ "cfg_skillvine_branching" ], "relation_ids": [ "r2_skillvine_r2flow_12" ], "source_ids": [ "expand_src_w_skillvine", "src_20261013_w_skillvine", "stage1_src_w_skillvine" ], "evidence_ids": [ "e2_w_skillvine", "e3_w_skillvine", "e4_cfg_skillvine_branching" ], "evidence_lines": [ 142, 159, 161 ], "review_depths": [ "selected_method_passages_with_abstract_baseline" ], "primary_passages": [], "content_characters": 4680, "evaluation_details_available": false }, { "work_family_id": "wf_8970b58c32ef2fc61b", "title": "SoL-Pi: Recursively Scaling Auto-Research Loops for Efficient Agent Harness", "role": "R03_METHOD", "role_label": "\u65b9\u6cd5\u8bba\u6587", "year": 2026, "branch_id": "M6_research", "path": "papers/r03_method/wf_8970b58c32ef2fc61b.md", "summary": "SoL-Pi derives hypotheses from trajectories and tests reusable efficiency mechanisms across development environments, retaining capability-constrained candidates before frozen evaluation. Direct bounded harness feedback search; RSI-inspired label does not establish research-optimizer self-modification.", "claim_ids": [ "role:b570_07_role_wf_8970b58c32ef2fc61b" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_07_src_wf_8970b58c32ef2fc61b" ], "evidence_ids": [ "b570_07_ev_wf_8970b58c32ef2fc61b" ], "evidence_lines": [ 855 ], "review_depths": [ "original_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_07_ev_wf_8970b58c32ef2fc61b", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2609.20519v1", "version": "2609.20519v1", "locator": "PDF physical page 2, derived page-text lines 43-49 (one-based local extraction, not web-tool lines)", "line_count": 7 }, { "evidence_id": "b570_07_ev_wf_8970b58c32ef2fc61b", "kind": "claim_packets", "url": "https://arxiv.org/pdf/2609.20519v1", "version": "2609.20519v1", "locator": "PDF physical page 4, derived page-text lines 3-24 (one-based local extraction, not web-tool lines)", "line_count": 22 }, { "evidence_id": "b570_07_ev_wf_8970b58c32ef2fc61b", "kind": "measurement_packets", "url": "https://arxiv.org/pdf/2609.20519v1", "version": "2609.20519v1", "locator": "PDF physical page 6, derived page-text lines 7-27 (one-based local extraction, not web-tool lines)", "line_count": 21 }, { "evidence_id": "b570_07_ev_wf_8970b58c32ef2fc61b", "kind": "measurement_packets", "url": "https://arxiv.org/pdf/2609.20519v1", "version": "2609.20519v1", "locator": "PDF physical page 6, derived page-text lines 44-47 (one-based local extraction, not web-tool lines)", "line_count": 4 }, { "evidence_id": "b570_07_ev_wf_8970b58c32ef2fc61b", "kind": "limit_packets", "url": "https://arxiv.org/pdf/2609.20519v1", "version": "2609.20519v1", "locator": "PDF physical page 11, derived page-text lines 31-38 (one-based local extraction, not web-tool lines)", "line_count": 8 } ], "content_characters": 12263, "evaluation_details_available": true }, { "work_family_id": "wf_7c8686845a455dfbe4", "title": "Sol-H3: Recursive Self-Improvement for MiniMax-H3 Inference Acceleration on Sol-Engine across Cloud and Edge", "role": "R03_METHOD", "role_label": "\u65b9\u6cd5\u8bba\u6587", "year": 2026, "branch_id": "M0_definitions", "path": "papers/r03_method/wf_7c8686845a455dfbe4.md", "summary": "Sol-H3 attributes macro architecture to human design and micro implementation optimization to RSI over collectives, fusion, sparse attention and LoRA integration; exact layout and approximate quantized changes are distinguished. 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"title": "StateSkill-Agent: An Effective and Efficient Embodied Agent via State-conditioned Skill", "role": "R03_METHOD", "role_label": "\u65b9\u6cd5\u8bba\u6587", "year": 2026, "branch_id": "M9_embodied", "path": "papers/r03_method/wf_61559e687099cb99a5.md", "summary": "\u5c06\u56fa\u5b9a\u6280\u80fd\u63a8\u7406\u3001\u79bb\u7ebf\u6280\u80fd\u4fee\u8ba2\u548c\u6280\u80fd\u2014\u6a21\u578b\u4ea4\u66ff\u8bad\u7ec3\u5206\u522b\u88c1\u5b9a\u3002", "claim_ids": [ "configuration:cfg_stateskill_evolve", "configuration:cfg_stateskill_fixed_fsm_inference", "configuration:cfg_stateskill_stategrpo_coevolution" ], "configuration_ids": [ "cfg_stateskill_fixed_fsm_inference", "cfg_stateskill_evolve", "cfg_stateskill_stategrpo_coevolution" ], "relation_ids": [ "r3_stateskill_voyager_representation_boundary" ], "source_ids": [ "r3_memory_src_stateskill_v1" ], "evidence_ids": [ "r3_memory_ev_stateskill_method" ], "evidence_lines": [ 302 ], "review_depths": [ 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Failed techniques are archived with conditions that can later reactivate them; an Anti-Mirage design compares random-reward nulls and diversity before admitting claimed improvement. 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Dual containers, quota proxy and injected final verifier separate development/testing. Five domains use 12-hour reasoning or 24-hour agentic budgets; tables mark clean versus cheating runs and report three-run reasoning scores. 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Treats agent trajectories, tools, memory and successor evidence as governance objects. This is a conceptual policy position, not an implemented governance or RSI benchmark. Direct governance interpretation of AI-accelerated successor development; proposed reassessment sequence is normative.", "claim_ids": [ "role:b570_04_role_wf_22b165cb469dc0d963" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_04_src_wf_22b165cb469dc0d963" ], "evidence_ids": [ "b570_04_ev_wf_22b165cb469dc0d963" ], "evidence_lines": [ 787 ], "review_depths": [ "registry_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_04_ev_wf_22b165cb469dc0d963", "kind": "claim_packets", "url": "https://zenodo.org/api/records/23045620/files/16-SGAEIA-Article-16-Public-EN-2026-09-29-AridioSIlva.pdf/content", "version": "10.5281/zenodo.23045620", "locator": "PDF p2; derived text lines 17-28 (one-based local extraction, not web-tool lines)", "line_count": 12 }, { "evidence_id": "b570_04_ev_wf_22b165cb469dc0d963", "kind": "claim_packets", "url": "https://zenodo.org/api/records/23045620/files/16-SGAEIA-Article-16-Public-EN-2026-09-29-AridioSIlva.pdf/content", "version": "10.5281/zenodo.23045620", "locator": "PDF p3; derived text lines 5-14 (one-based local extraction, not web-tool lines)", "line_count": 10 }, { "evidence_id": "b570_04_ev_wf_22b165cb469dc0d963", "kind": "limit_packets", "url": "https://zenodo.org/api/records/23045620/files/16-SGAEIA-Article-16-Public-EN-2026-09-29-AridioSIlva.pdf/content", "version": "10.5281/zenodo.23045620", "locator": "PDF p7; derived text lines 20-31 (one-based local extraction, not web-tool lines)", "line_count": 12 } ], "content_characters": 8540, "evaluation_details_available": false }, { "work_family_id": "wf_f8673ac0b984aedaad", "title": "When Does Authorization Expire? 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Direct formal RSI evaluation/governance proposal; distinct authorization gate, not an empirically validated general reachability solver.", "claim_ids": [ "role:b570_02_role_wf_f8673ac0b984aedaad" ], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_02_src_wf_f8673ac0b984aedaad" ], "evidence_ids": [ "b570_02_ev_wf_f8673ac0b984aedaad" ], "evidence_lines": [ 749 ], "review_depths": [ "registry_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_02_ev_wf_f8673ac0b984aedaad", "kind": "claim_packets", "url": "https://jaiet.com/article/iot107/JAIET308004.pdf", "version": "10.47001/JAIET/2026.308004; August2026 PDF, revision identifier not shown", "locator": "tool L408\u2013424", "line_count": 17 }, { "evidence_id": "b570_02_ev_wf_f8673ac0b984aedaad", "kind": "limit_packets", "url": "https://jaiet.com/article/iot107/JAIET308004.pdf", "version": "10.47001/JAIET/2026.308004; August2026 PDF, revision identifier not shown", "locator": "tool L684\u2013703", "line_count": 20 } ], "content_characters": 6751, "evaluation_details_available": false }, { "work_family_id": "wf_a7c45ed537b580c573", "title": "When Models Examine Themselves: Vocabulary-Activation Correspondence in Self-Referential Processing", "role": "R11_BACKGROUND", "role_label": "\u80cc\u666f/\u76f8\u90bb\u57fa\u7840", "year": 2026, "branch_id": "M8_failure", "path": "papers/r11_background/wf_a7c45ed537b580c573.md", "summary": "The actual v1 studies vocabulary\u2013activation correspondence during 1,000-pull single-inference self-examination, with Llama activation directions, prompt-transfer controls and causal steering; Qwen evidence is correlational. The object is self-report vocabulary and internal activation state. 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Generic self-reference is distinguished from non-closing truth recursion using pre-specified tests, exploratory FDR tests, minimal pairs and a lexical contradiction heuristic. The matrix-undecidability account is conjectural. 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Original-body topical exclusion of this human cognitive-loop case from technical RSI claims; no assertion that its system lacks feedback.", "claim_ids": [], "configuration_ids": [], "relation_ids": [], "source_ids": [ "b570_02_src_wf_1de3441ce24521c6cc" ], "evidence_ids": [ "b570_02_ev_wf_1de3441ce24521c6cc" ], "evidence_lines": [ 756 ], "review_depths": [ "original_abstract", "selected_primary_role_definition_protocol_scope_and_limits" ], "primary_passages": [ { "evidence_id": "b570_02_ev_wf_1de3441ce24521c6cc", "kind": "claim_packets", "url": "https://arxiv.org/html/2604.15343v1", "version": "2604.15343v1", "locator": "tool L148\u2013164", "line_count": 17 }, { "evidence_id": "b570_02_ev_wf_1de3441ce24521c6cc", "kind": "claim_packets", "url": "https://arxiv.org/html/2604.15343v1", "version": "2604.15343v1", "locator": "tool L212\u2013229", "line_count": 18 }, { "evidence_id": "b570_02_ev_wf_1de3441ce24521c6cc", "kind": "limit_packets", "url": "https://arxiv.org/html/2604.15343v1", "version": "2604.15343v1", "locator": "tool L515\u2013538", "line_count": 24 } ], "content_characters": 15945, "evaluation_details_available": false }, { "work_family_id": "wf_c38169c820d96e0450", "title": "When the Weights Can Move: Governed Experience for an Adaptive Planner", "role": "R02_CONCEPT", "role_label": "\u6982\u5ff5/\u7406\u8bba/\u7acb\u573a", "year": 2026, "branch_id": "M4_weights", "path": "papers/r02_concept/wf_c38169c820d96e0450.md", "summary": "Analyzes how governed memory, independent checks, model fingerprints, write-before-execute records and reversible checkpoints would extend to a weight-updating planner. 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