Upload graph.jsonl with huggingface_hub

graph.jsonl

@@ -62,18 +62,21 @@
 {"@id":"https://github.com/shimo4228/claude-skill-daily-research","@type":["EcosystemRepo","SoftwareSourceCode"],"name":"daily-research","description":"Pre-AKC ancestor of the Research phase: the author's daily signal-first research pipeline, skillified in April 2026. Documented as implementation history in docs/inspiration.md.","url":"https://github.com/shimo4228/claude-skill-daily-research","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/inspiration.md"}
 {"@id":"https://github.com/shimo4228/shimo4228","@type":"EcosystemRepo","name":"Research Program Hub","description":"Hub repository of the shimo4228 research program; its graph.jsonld is the canonical relationship map of the research ecosystem, federating AKC with its sibling and downstream lines.","url":"https://github.com/shimo4228/shimo4228"}
 {"@id":"https://doi.org/10.5281/zenodo.20578272","sameAs":"https://www.wikidata.org/wiki/Q140090144","@type":"ScholarlyArticle","name":"Harness Alignment and Harness Drift: Why Intent, Unlike Correctness, Resists Automation","author":{"@id":"https://orcid.org/0009-0002-6168-4162"},"datePublished":"2026-06-07","identifier":"10.5281/zenodo.20578272","about":"https://doi.org/10.5281/zenodo.19200726","isBasedOn":"https://github.com/shimo4228/agent-knowledge-cycle","description":"Position paper (Zenodo working paper, v1) deposited from the AKC line. Defines harness alignment — the continuous, human-gated activity of keeping an agent's harness aligned with the operator's evolving intent — and harness drift, its failure mode, against the software-evolution and alignment literatures; argues the three defining properties (continuous, human-gated, bidirectional) follow from a single root: intent, unlike correctness, cannot be automated the same way. Records a bibliographic bridge: audited 2026 drift coinages are severed from the classical software-evolution lineage, which harness drift reconnects by reference. Two-layer design: lean body for human readers, verified-verbatim footnotes as a density layer for LLM consumption. Scoped as provisional judgments from a months-old practice, offered as a position, not an empirical study."}
-{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/coala","@type":"ScholarlyArticle","name":"CoALA: Cognitive Architectures for Language Agents","author":"Sumers et al.","datePublished":"2023","description":"Prior art named in ADR-0013's Related-Work positioning. Provides the framework vocabulary (modular memory, structured action space, decision procedure) that makes the agent-memory literature commensurable. Cited as prior art for positioning, not consulted during AKC's construction; AKC contrasts on loop ownership (human gate), bidirectional human-judgment target, and human-attention scarcity.","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0013-positioning-within-agent-memory-literature.md"}
-{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/voyager","@type":"ScholarlyArticle","name":"Voyager: An Open-Ended Embodied Agent with Large Language Models","author":"Wang et al.","datePublished":"2023","description":"Prior art named in ADR-0013's Related-Work positioning. Maintains an ever-growing skill library of executable code induced from gameplay — in AKC vocabulary, Extract-then-Promote run end to end, autonomously. AKC concedes the operation is not novel and locates its delta: the prior art closes the loop without a human in it; AKC's Promote requires named human sign-off.","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0013-positioning-within-agent-memory-literature.md"}
-{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/generative-agents","@type":"ScholarlyArticle","name":"Generative Agents: Interactive Simulacra of Human Behavior","author":"Park et al.","datePublished":"2023","description":"Prior art named in ADR-0013's Related-Work positioning. Introduced a reflection step that synthesizes observations into higher-level inferences stored for later retrieval — the Extract / reflection operation AKC concedes as precedent. AKC contrasts on who owns the loop and on framing human attention, not agent capability, as the scarce resource.","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0013-positioning-within-agent-memory-literature.md"}
-{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/memgpt","@type":"ScholarlyArticle","name":"MemGPT: Towards LLMs as Operating Systems","author":"Packer et al.","datePublished":"2023","description":"Prior art named in ADR-0013's Related-Work positioning. Formalizes a memory hierarchy with paging between context and external store. Its binding constraint is the context window; AKC (ADR-0010) names a different ceiling — human attention and judgment — so the two solve scarcity on different resources and can coexist.","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0013-positioning-within-agent-memory-literature.md"}
-{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/reme","@type":"ScholarlyArticle","name":"ReMe: Remember Me, Refine Me","author":"Cao et al.","datePublished":"2025","description":"Prior art named in ADR-0013's Related-Work positioning. A dynamic procedural-memory framework that continuously refines what is stored — a Curate-and-Promote loop by another name, run autonomously. AKC concedes the refinement operation as precedent and locates its delta in the structural human approval gate where ReMe runs without a human in the write path.","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0013-positioning-within-agent-memory-literature.md"}
-{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/agent-workflow-memory","@type":"ScholarlyArticle","name":"Agent Workflow Memory","author":"Wang et al.","datePublished":"2024","description":"Prior art named in ADR-0013's Related-Work positioning. Induces commonly reused routines (workflows) from agent trajectories and feeds them back into subsequent generations — Extract-then-Promote without a human in the write path. AKC concedes the induction operation and contrasts on loop ownership and on optimizing the operator's judgment, not only the agent's task success.","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0013-positioning-within-agent-memory-literature.md"}
+{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/coala","sameAs":"https://www.wikidata.org/wiki/Q140181234","@type":"ScholarlyArticle","name":"CoALA: Cognitive Architectures for Language Agents","author":"Sumers et al.","datePublished":"2023","identifier":"arXiv:2309.02427","url":"https://arxiv.org/abs/2309.02427","description":"Prior art named in ADR-0013's Related-Work positioning. Provides the framework vocabulary (modular memory, structured action space, decision procedure) that makes the agent-memory literature commensurable. Cited as prior art for positioning, not consulted during AKC's construction; AKC contrasts on loop ownership (human gate), bidirectional human-judgment target, and human-attention scarcity.","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0013-positioning-within-agent-memory-literature.md"}
+{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/voyager","sameAs":"https://www.wikidata.org/wiki/Q140181233","@type":"ScholarlyArticle","name":"Voyager: An Open-Ended Embodied Agent with Large Language Models","author":"Wang et al.","datePublished":"2023","identifier":"arXiv:2305.16291","url":"https://arxiv.org/abs/2305.16291","description":"Prior art named in ADR-0013's Related-Work positioning. Maintains an ever-growing skill library of executable code induced from gameplay — in AKC vocabulary, Extract-then-Promote run end to end, autonomously. AKC concedes the operation is not novel and locates its delta: the prior art closes the loop without a human in it; AKC's Promote requires named human sign-off.","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0013-positioning-within-agent-memory-literature.md"}
+{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/generative-agents","sameAs":"https://www.wikidata.org/wiki/Q130846143","@type":"ScholarlyArticle","name":"Generative Agents: Interactive Simulacra of Human Behavior","author":"Park et al.","datePublished":"2023","identifier":"arXiv:2304.03442","url":"https://arxiv.org/abs/2304.03442","description":"Prior art named in ADR-0013's Related-Work positioning. Introduced a reflection step that synthesizes observations into higher-level inferences stored for later retrieval — the Extract / reflection operation AKC concedes as precedent. AKC contrasts on who owns the loop and on framing human attention, not agent capability, as the scarce resource.","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0013-positioning-within-agent-memory-literature.md"}
+{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/memgpt","sameAs":"https://www.wikidata.org/wiki/Q140181237","@type":"ScholarlyArticle","name":"MemGPT: Towards LLMs as Operating Systems","author":"Packer et al.","datePublished":"2023","identifier":"arXiv:2310.08560","url":"https://arxiv.org/abs/2310.08560","description":"Prior art named in ADR-0013's Related-Work positioning. Formalizes a memory hierarchy with paging between context and external store. Its binding constraint is the context window; AKC (ADR-0010) names a different ceiling — human attention and judgment — so the two solve scarcity on different resources and can coexist.","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0013-positioning-within-agent-memory-literature.md"}
+{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/reme","sameAs":"https://www.wikidata.org/wiki/Q140181257","@type":"ScholarlyArticle","name":"ReMe: Remember Me, Refine Me","author":"Cao et al.","datePublished":"2025","identifier":"arXiv:2512.10696","url":"https://arxiv.org/abs/2512.10696","description":"Prior art named in ADR-0013's Related-Work positioning. A dynamic procedural-memory framework that continuously refines what is stored — a Curate-and-Promote loop by another name, run autonomously. AKC concedes the refinement operation as precedent and locates its delta in the structural human approval gate where ReMe runs without a human in the write path.","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0013-positioning-within-agent-memory-literature.md"}
+{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/agent-workflow-memory","sameAs":"https://www.wikidata.org/wiki/Q140181241","@type":"ScholarlyArticle","name":"Agent Workflow Memory","author":"Wang et al.","datePublished":"2024","identifier":"arXiv:2409.07429","url":"https://arxiv.org/abs/2409.07429","description":"Prior art named in ADR-0013's Related-Work positioning. Induces commonly reused routines (workflows) from agent trajectories and feeds them back into subsequent generations — Extract-then-Promote without a human in the write path. AKC concedes the induction operation and contrasts on loop ownership and on optimizing the operator's judgment, not only the agent's task success.","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0013-positioning-within-agent-memory-literature.md"}
 {"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/intent-alignment-christiano","@type":"TechArticle","name":"Clarifying \"AI alignment\"","author":"Christiano, P.","datePublished":"2018","description":"Vocabulary lineage named in ADR-0017. Coined intent alignment: an aligned AI \"is trying to do what H wants it to do\" — motivation, not competence. Treats the operator's wants as static; AKC's harness alignment extends the term to the configuration layer and across time, where intent itself evolves.","url":"https://ai-alignment.com/clarifying-ai-alignment-cec47cd69dd6","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0017-harness-alignment-and-drift.md"}
-{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/lehman-laws","@type":"ScholarlyArticle","name":"Programs, Life Cycles, and Laws of Software Evolution","author":"Lehman, M. M.","datePublished":"1980","description":"Vocabulary lineage named in ADR-0017. Law I (Continuing Change): an E-type program \"undergoes continual change or becomes progressively less useful\"; \"evolution is an intrinsic, feedback driven, property of software\" (Proceedings of the IEEE 68(9)). Lehman's driver of change is the world the program is embedded in; harness alignment's driver is the operator's evolving intent.","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0017-harness-alignment-and-drift.md"}
-{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/architectural-drift-perry-wolf","@type":"ScholarlyArticle","name":"Foundations for the Study of Software Architecture","author":"Perry, D. E. & Wolf, A. L.","datePublished":"1992","description":"Vocabulary lineage named in ADR-0017. Defines architectural drift — \"due to insensitivity about the architecture\", leading \"more to inadaptability than to disasters\" (ACM SIGSOFT SEN 17(4)) — the canonical academic name for divergence-by-insensitivity. Assumes a fixed intended architecture; harness drift's reference point (operator intent) itself evolves.","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0017-harness-alignment-and-drift.md"}
+{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/lehman-laws","sameAs":"https://www.wikidata.org/wiki/Q57311412","@type":"ScholarlyArticle","name":"Programs, Life Cycles, and Laws of Software Evolution","author":"Lehman, M. M.","datePublished":"1980","identifier":"doi:10.1109/PROC.1980.11805","url":"https://doi.org/10.1109/PROC.1980.11805","description":"Vocabulary lineage named in ADR-0017. Law I (Continuing Change): an E-type program \"undergoes continual change or becomes progressively less useful\"; \"evolution is an intrinsic, feedback driven, property of software\" (Proceedings of the IEEE 68(9)). Lehman's driver of change is the world the program is embedded in; harness alignment's driver is the operator's evolving intent.","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0017-harness-alignment-and-drift.md"}
+{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/architectural-drift-perry-wolf","sameAs":"https://www.wikidata.org/wiki/Q55880382","@type":"ScholarlyArticle","name":"Foundations for the Study of Software Architecture","author":"Perry, D. E. & Wolf, A. L.","datePublished":"1992","identifier":"doi:10.1145/141874.141884","url":"https://doi.org/10.1145/141874.141884","description":"Vocabulary lineage named in ADR-0017. Defines architectural drift — \"due to insensitivity about the architecture\", leading \"more to inadaptability than to disasters\" (ACM SIGSOFT SEN 17(4)) — the canonical academic name for divergence-by-insensitivity. Assumes a fixed intended architecture; harness drift's reference point (operator intent) itself evolves.","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0017-harness-alignment-and-drift.md"}
 {"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/practical-drift-snook","@type":"Book","name":"Friendly Fire: The Accidental Shootdown of U.S. Black Hawks over Northern Iraq","author":"Snook, S. A.","datePublished":"2000","description":"Vocabulary lineage named in ADR-0017. Names practical drift: practice slowly uncoupling from written procedure (cited as characterized in secondary literature; book text not directly verified). Names the failure process at the rules-vs-practice boundary; harness alignment names the counter-activity.","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0017-harness-alignment-and-drift.md"}
 {"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/meta-harness","sameAs":"https://www.wikidata.org/wiki/Q140181272","@type":"ScholarlyArticle","name":"Meta-Harness: End-to-End Optimization of Model Harnesses","author":"Lee, Y. et al.","datePublished":"2026","description":"Contrast named in ADR-0017, and the framing ADR-0009 rejected for AKC itself. Defines the harness as \"the code that determines what information to store, retrieve, and present to the model\" and improves it autonomously against benchmark scores — harness optimization, the correctness axis. Harness alignment is the human-gated counterpart on the intent axis; the two are complementary, not competing.","url":"https://arxiv.org/abs/2603.28052","groundedIn":["https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0017-harness-alignment-and-drift.md","https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0009-akc-is-a-cycle-not-a-harness.md"]}
 {"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/agent-drift","sameAs":"https://www.wikidata.org/wiki/Q140181260","@type":"ScholarlyArticle","name":"Agent Drift: Quantifying Behavioral Degradation in Multi-Agent LLM Systems Over Extended Interactions","author":"Rath, A.","datePublished":"2026","description":"Vocabulary lineage named in ADR-0017. Defines agent drift as \"the progressive degradation of agent behavior, decision quality, and inter-agent coherence over extended interaction sequences\" and semantic drift as \"progressive deviation from original intent\". Its reference list contains no classical software-engineering literature (no Lehman, Perry & Wolf, Parnas, or Snook) — the vocabulary gap harness drift bridges by explicit lineage to both bodies.","url":"https://arxiv.org/abs/2601.04170","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0017-harness-alignment-and-drift.md"}
+{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/geo","sameAs":"https://www.wikidata.org/wiki/Q131161430","@type":"ScholarlyArticle","name":"GEO: Generative Engine Optimization","author":"Aggarwal, P. et al.","datePublished":"2023","identifier":"arXiv:2311.09735","url":"https://arxiv.org/abs/2311.09735","description":"Measurement framework behind the geo-writer snapshot in ADR-0010 — the first Measure-phase self-application to AKC's own documentation. The README is scored before and after the cognitive-economy change on GEO-derived checks (entity density, question-heading prominence, chunk self-containment, definition density), with both snapshots retained in version control so successive ADRs can track the README's GEO trajectory over time.","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0010-human-cognitive-resource-as-central-constraint.md"}
+{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/intrinsic-metacognitive-learning","sameAs":"https://www.wikidata.org/wiki/Q140181243","@type":"ScholarlyArticle","name":"Truly Self-Improving Agents Require Intrinsic Metacognitive Learning","author":"Liu & van der Schaar","datePublished":"2025","identifier":"arXiv:2506.05109","url":"https://arxiv.org/abs/2506.05109","description":"Taxonomy named in ADR-0005's defense of the human approval gate. In its intrinsic/extrinsic distinction, AKC's gate is extrinsic metacognition — a human-designed loop with a human evaluator at the decision point — and stays so by design, not by immaturity: behavior-modifying writes remain gated because they are where the operator's evolving intent enters the loop.","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0005-human-approval-gate.md"}
+{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/externalization-review","sameAs":"https://www.wikidata.org/wiki/Q140181274","@type":"ScholarlyArticle","name":"Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering","author":"Zhou et al.","datePublished":"2026","identifier":"arXiv:2604.08224","url":"https://arxiv.org/abs/2604.08224","description":"Field map named in ADR-0013's Related-Work positioning. Frames the field as three coupled forms of externalization — memory, skills, protocols — coordinated by harness engineering as the unification layer; AKC accepts that it sits squarely inside this frame, overlapping the memory and skills quadrants, and locates its delta in loop ownership rather than in the externalization operations themselves.","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0013-positioning-within-agent-memory-literature.md"}
 {"@id":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/schemas/episode-log.schema.json","@type":["CreativeWork","DataDownload"],"name":"Episode log JSON schema","description":"JSON schema for the Layer 1 episode log record. Append-only, daily-partitioned JSONL with owner-only permissions. Codifies the immutable source-of-truth shape (ADR-0002).","encodingFormat":"application/schema+json","appliesTo":"https://shimo4228.github.io/shimo4228/vocab#memory-layer/episode-log","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0002-immutable-episode-log.md"}
 {"@id":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/schemas/knowledge.schema.json","@type":["CreativeWork","DataDownload"],"name":"Knowledge store JSON schema","description":"JSON schema for the Layer 2 knowledge record. Time-decayed and forbidden-substring validated patterns distilled from Layer 1 episodes (ADR-0003).","encodingFormat":"application/schema+json","appliesTo":"https://shimo4228.github.io/shimo4228/vocab#memory-layer/knowledge","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0003-three-layer-distillation.md"}
 {"@id":"https://github.com/shimo4228/agent-knowledge-cycle/tree/main/examples/minimal_harness","@type":"SoftwareSourceCode","name":"minimal_harness reference implementation","description":"~500-line dependency-free Python reference demonstrating the three memory layers and the two-stage distill pipeline. Runs the cycle on behavioral patterns; the mechanism demo for ADR-0011 genre-neutrality (falsifiable commitment #1). Runnable end-to-end with `python3 -m examples.minimal_harness.demo`.","programmingLanguage":"Python","codeRepository":"https://github.com/shimo4228/agent-knowledge-cycle","implements":"https://shimo4228.github.io/shimo4228/vocab#concept/six-phase-loop","groundedIn":["https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0003-three-layer-distillation.md","https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0004-two-stage-distill-pipeline.md","https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0011-cycle-applies-to-any-knowledge-body.md"]}