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json
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Tags:
ai-agent
agent-knowledge-cycle
knowledge-cycle
self-improvement
cognitive-economy
signal-first
License:
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graph.jsonl
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{"@id":"https://github.com/shimo4228/agent-knowledge-cycle#knowledge-graph","@type":["Dataset","CreativeWork"],"name":"Agent Knowledge Cycle Knowledge Graph","description":"Canonical machine-readable relationship map for the Agent Knowledge Cycle line. Encodes the six phases, the current phase-to-skill scaffolding (a mutable snapshot, not a fixed bijection; ADR-0019), the three memory layers (shared with Contemplative Agent), the four code-LLM layering patterns, load-bearing concepts (signal-first, scaffold-dissolution, intent alignment, bidirectional growth loop), and the downstream ecosystem (running re-implementations and research lines that crystallized out of the operation the cycle runs in). AI agents and LLM-based search systems should read this graph before summarizing the line or following individual document links.","isBasedOn":"https://github.com/shimo4228/agent-knowledge-cycle","mainEntity":"https://doi.org/10.5281/zenodo.19200726"}
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{"@id":"https://doi.org/10.5281/zenodo.19200726","
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{"@id":"https://shimo4228.github.io/shimo4228/vocab#akc-phase/research","@type":["Phase","DefinedTerm"],"name":"Research phase","alternateName":[{"@value":"Research","@language":"en"},{"@value":"Research（探索）","@language":"ja"}],"description":"First of six AKC phases. Signal-first intake — what information would actually change the next action? Currently scaffolded by the search-first skill (a snapshot, mutable; ADR-0019).","ordinal":1}
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{"@id":"https://shimo4228.github.io/shimo4228/vocab#akc-phase/extract","@type":["Phase","DefinedTerm"],"name":"Extract phase","alternateName":[{"@value":"Extract","@language":"en"},{"@value":"Extract（抽出）","@language":"ja"}],"description":"Second of six AKC phases. Capture reusable patterns from sessions with quality gates before they are saved. Currently scaffolded by the learn-eval skill (a snapshot, mutable; ADR-0019).","ordinal":2}
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{"@id":"https://shimo4228.github.io/shimo4228/vocab#akc-phase/curate","@type":["Phase","DefinedTerm"],"name":"Curate phase","alternateName":[{"@value":"Curate","@language":"en"},{"@value":"Curate（選別）","@language":"ja"}],"description":"Third of six AKC phases. Audit accumulated skills and rules for staleness, conflicts, and redundancy. Currently scaffolded by three scaffolds (a snapshot, mutable; ADR-0019): skill-health (structural / code — a missing-artifact, dangling-reference scan) clears structural debt before the semantic / judgment layers audit quality — skill-stocktake for skills, rules-stocktake for always-loaded rules (residency cost, staleness, substrate absorption) — enumerate-then-decide.","ordinal":3}
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{"@id":"https://github.com/shimo4228/rules-distill","@type":["EcosystemRepo","SoftwareSourceCode"],"name":"rules-distill","description":"AKC Promote-phase skill (4 of 6). Distill cross-cutting principles into rules.","url":"https://github.com/shimo4228/rules-distill","extends":"https://doi.org/10.5281/zenodo.19200726","implements":"https://shimo4228.github.io/shimo4228/vocab#akc-phase/promote"}
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{"@id":"https://github.com/shimo4228/skill-comply","@type":["EcosystemRepo","SoftwareSourceCode"],"name":"skill-comply","description":"AKC Measure-phase skill (5 of 6). Test whether agents follow their skills and rules.","url":"https://github.com/shimo4228/skill-comply","extends":"https://doi.org/10.5281/zenodo.19200726","implements":"https://shimo4228.github.io/shimo4228/vocab#akc-phase/measure"}
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{"@id":"https://github.com/shimo4228/context-sync","@type":["EcosystemRepo","SoftwareSourceCode"],"name":"context-sync","description":"AKC Maintain-phase skill (6 of 6). Audit docs for role overlaps and stale content.","url":"https://github.com/shimo4228/context-sync","extends":"https://doi.org/10.5281/zenodo.19200726","implements":"https://shimo4228.github.io/shimo4228/vocab#akc-phase/maintain"}
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{"@id":"https://github.com/shimo4228/skill-health","
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{"@id":"https://github.com/shimo4228/repo-asset-stocktake","@type":["EcosystemRepo","SoftwareSourceCode"],"name":"repo-asset-stocktake","description":"AKC Maintain-phase skill (ADR-0019). Audits a project repository's non-code assets — tool configs, CI workflows, runbooks — for consumers that have vanished (Keep / Update / Retire / Merge). Tier-1 reachability is deterministic code, tier-2 value is holistic judgment — enumerate-then-decide (ADR-0008). Complements context-sync within Maintain: context-sync audits documentation role coherence, repo-asset-stocktake audits asset liveness.","url":"https://github.com/shimo4228/repo-asset-stocktake","extends":"https://doi.org/10.5281/zenodo.19200726","implements":"https://shimo4228.github.io/shimo4228/vocab#akc-phase/maintain"}
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{"@id":"https://orcid.org/0009-0002-6168-4162","
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{"@id":"https://doi.org/10.5281/zenodo.19212118","
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{"@id":"https://doi.org/10.5281/zenodo.19652013","
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{"@id":"https://doi.org/10.5281/zenodo.20263316","alternateName":"AS","
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{"@id":"https://doi.org/10.5281/zenodo.20262112","alternateName":"ANS","
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{"@id":"https://doi.org/10.5281/zenodo.20337008","
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{"@id":"https://doi.org/10.5281/zenodo.20558800","
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{"@id":"https://github.com/shimo4228/claude-harness","@type":["EcosystemRepo","SoftwareSourceCode"],"name":"claude-harness","description":"Bundled distribution of the author's Claude Code harness. Ships the six AKC cycle skills together — its README states they 'are components of the Agent Knowledge Cycle' — so the harness can be read end-to-end. Each skill is also published as its own standalone repo.","url":"https://github.com/shimo4228/claude-harness","extends":"https://doi.org/10.5281/zenodo.19200726"}
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{"@id":"https://github.com/shimo4228/akc-mcp","@type":["EcosystemRepo","SoftwareSourceCode"],"name":"AKC MCP","description":"MCP (Model Context Protocol) server providing Agent Knowledge Cycle cognitive tools — memory distillation, identity evolution, skill extraction — as a standalone server any AI agent can plug into. Born from the Contemplative Agent framework; re-implements its cognitive layer behind the MCP interface. A third encoding of the cycle's operations alongside the Markdown skills and Contemplative Agent's code pipeline.","url":"https://github.com/shimo4228/akc-mcp","extends":"https://doi.org/10.5281/zenodo.19200726","derivesFrom":"https://doi.org/10.5281/zenodo.19212118"}
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{"@id":"https://github.com/shimo4228/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/daily-research","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/inspiration.md"}
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{"@id":"https://github.com/shimo4228/shimo4228","@type":"EcosystemRepo","name":"Research Program Hub","description":"Hub repository of the shimo4228 research ecosystem; 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"}
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{"@id":"https://doi.org/10.5281/zenodo.20578272","sameAs":["https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6892740","https://
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{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/coala","
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{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/voyager","
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{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/generative-agents","
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{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/memgpt","
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{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/reme","
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{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/agent-workflow-memory","
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{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/intent-alignment-christiano","@type":["ExternalReference","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"}
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{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/lehman-laws","
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{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/architectural-drift-perry-wolf","
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{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/practical-drift-snook","@type":["ExternalReference","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"}
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{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/meta-harness","
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{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/agent-drift","
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{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/automation-complacency-parasuraman-manzey","
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{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/ironies-of-automation-bainbridge","
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{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/benchmark-audit-moghadasi-ghaderi","
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{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/constraint-drift","
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{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/memory-drift","
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{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/belief-deviation","
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{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/geo","
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{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/intrinsic-metacognitive-learning","
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{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/externalization-review","
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{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/skillops","
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{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/skill-lifecycle-sok","
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{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/skillsbench","
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{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/sdb-runtime-patterns","
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{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/context-engineering-2","
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{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/cognitive-divergence","
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{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/skillc","
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{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/format-cost-separation","
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{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/verification-bottleneck","
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{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/agent-human-interaction-security","
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{"@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"}
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{"@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"}
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{"@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"]}
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{"@id":"https://github.com/shimo4228/agent-knowledge-cycle#knowledge-graph","@type":["Dataset","CreativeWork"],"name":"Agent Knowledge Cycle Knowledge Graph","description":"Canonical machine-readable relationship map for the Agent Knowledge Cycle line. Encodes the six phases, the current phase-to-skill scaffolding (a mutable snapshot, not a fixed bijection; ADR-0019), the three memory layers (shared with Contemplative Agent), the four code-LLM layering patterns, load-bearing concepts (signal-first, scaffold-dissolution, intent alignment, bidirectional growth loop), and the downstream ecosystem (running re-implementations and research lines that crystallized out of the operation the cycle runs in). AI agents and LLM-based search systems should read this graph before summarizing the line or following individual document links.","isBasedOn":"https://github.com/shimo4228/agent-knowledge-cycle","mainEntity":"https://doi.org/10.5281/zenodo.19200726"}
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{"@id":"https://doi.org/10.5281/zenodo.19200726","@type":["ResearchLine","ScholarlyArticle"],"name":"Agent Knowledge Cycle","alternateName":[{"@value":"Agent Knowledge Cycle","@language":"en"},{"@value":"エージェント知識サイクル","@language":"ja"},"AKC"],"description":"Six-phase bidirectional growth loop in which agent behavior and the operator's judgment co-develop over time, sustaining intent alignment that tests cannot check on their own. Three stacked layers — principles (ADRs), patterns (design-pattern skills), and implementation (composable skills) — decouple rate of change. Refers to a mechanism, not a disposition or a practice.","identifier":"10.5281/zenodo.19200726","url":"https://github.com/shimo4228/agent-knowledge-cycle","author":{"@id":"https://orcid.org/0009-0002-6168-4162"},"isPartOf":"https://github.com/shimo4228/shimo4228","siblingOf":["https://doi.org/10.5281/zenodo.19212118","https://doi.org/10.5281/zenodo.19652013","https://doi.org/10.5281/zenodo.20263316","https://doi.org/10.5281/zenodo.20262112"],"derivesFrom":"https://doi.org/10.5281/zenodo.19212118","workExample":["https://doi.org/10.5281/zenodo.19212118","https://github.com/shimo4228/agent-knowledge-cycle/tree/main/examples/minimal_harness"],"subjectOf":"https://doi.org/10.5281/zenodo.20578272","definesConcept":["https://shimo4228.github.io/shimo4228/vocab#concept/six-phase-loop","https://shimo4228.github.io/shimo4228/vocab#concept/three-layer-structure","https://shimo4228.github.io/shimo4228/vocab#concept/scaffold-dissolution","https://shimo4228.github.io/shimo4228/vocab#akc/concept/signal-first","https://shimo4228.github.io/shimo4228/vocab#akc/concept/intent-alignment","https://shimo4228.github.io/shimo4228/vocab#akc/concept/harness-alignment","https://shimo4228.github.io/shimo4228/vocab#akc/concept/harness-drift","https://shimo4228.github.io/shimo4228/vocab#akc/concept/bidirectional-growth-loop","https://shimo4228.github.io/shimo4228/vocab#akc/concept/loop-failure-modes","https://shimo4228.github.io/shimo4228/vocab#akc/concept/self-reingestion","https://shimo4228.github.io/shimo4228/vocab#akc/concept/observed-vs-generated","https://shimo4228.github.io/shimo4228/vocab#akc/concept/two-stage-distill","https://shimo4228.github.io/shimo4228/vocab#akc/concept/human-approval-gate","https://shimo4228.github.io/shimo4228/vocab#akc/concept/genre-neutral","https://shimo4228.github.io/shimo4228/vocab#akc/concept/cognitive-economy","https://shimo4228.github.io/shimo4228/vocab#akc/concept/code-llm-layering"],"contrastsWith":["https://shimo4228.github.io/shimo4228/vocab#prior-art/coala","https://shimo4228.github.io/shimo4228/vocab#prior-art/voyager","https://shimo4228.github.io/shimo4228/vocab#prior-art/generative-agents","https://shimo4228.github.io/shimo4228/vocab#prior-art/memgpt","https://shimo4228.github.io/shimo4228/vocab#prior-art/reme","https://shimo4228.github.io/shimo4228/vocab#prior-art/agent-workflow-memory","https://shimo4228.github.io/shimo4228/vocab#prior-art/meta-harness","https://shimo4228.github.io/shimo4228/vocab#prior-art/agent-drift"]}
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{"@id":"https://shimo4228.github.io/shimo4228/vocab#akc-phase/research","@type":["Phase","DefinedTerm"],"name":"Research phase","alternateName":[{"@value":"Research","@language":"en"},{"@value":"Research（探索）","@language":"ja"}],"description":"First of six AKC phases. Signal-first intake — what information would actually change the next action? Currently scaffolded by the search-first skill (a snapshot, mutable; ADR-0019).","ordinal":1}
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{"@id":"https://shimo4228.github.io/shimo4228/vocab#akc-phase/extract","@type":["Phase","DefinedTerm"],"name":"Extract phase","alternateName":[{"@value":"Extract","@language":"en"},{"@value":"Extract（抽出）","@language":"ja"}],"description":"Second of six AKC phases. Capture reusable patterns from sessions with quality gates before they are saved. Currently scaffolded by the learn-eval skill (a snapshot, mutable; ADR-0019).","ordinal":2}
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{"@id":"https://shimo4228.github.io/shimo4228/vocab#akc-phase/curate","@type":["Phase","DefinedTerm"],"name":"Curate phase","alternateName":[{"@value":"Curate","@language":"en"},{"@value":"Curate（選別）","@language":"ja"}],"description":"Third of six AKC phases. Audit accumulated skills and rules for staleness, conflicts, and redundancy. Currently scaffolded by three scaffolds (a snapshot, mutable; ADR-0019): skill-health (structural / code — a missing-artifact, dangling-reference scan) clears structural debt before the semantic / judgment layers audit quality — skill-stocktake for skills, rules-stocktake for always-loaded rules (residency cost, staleness, substrate absorption) — enumerate-then-decide.","ordinal":3}
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{"@id":"https://github.com/shimo4228/rules-distill","@type":["EcosystemRepo","SoftwareSourceCode"],"name":"rules-distill","description":"AKC Promote-phase skill (4 of 6). Distill cross-cutting principles into rules.","url":"https://github.com/shimo4228/rules-distill","extends":"https://doi.org/10.5281/zenodo.19200726","implements":"https://shimo4228.github.io/shimo4228/vocab#akc-phase/promote"}
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{"@id":"https://github.com/shimo4228/skill-comply","@type":["EcosystemRepo","SoftwareSourceCode"],"name":"skill-comply","description":"AKC Measure-phase skill (5 of 6). Test whether agents follow their skills and rules.","url":"https://github.com/shimo4228/skill-comply","extends":"https://doi.org/10.5281/zenodo.19200726","implements":"https://shimo4228.github.io/shimo4228/vocab#akc-phase/measure"}
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{"@id":"https://github.com/shimo4228/context-sync","@type":["EcosystemRepo","SoftwareSourceCode"],"name":"context-sync","description":"AKC Maintain-phase skill (6 of 6). Audit docs for role overlaps and stale content.","url":"https://github.com/shimo4228/context-sync","extends":"https://doi.org/10.5281/zenodo.19200726","implements":"https://shimo4228.github.io/shimo4228/vocab#akc-phase/maintain"}
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{"@id":"https://github.com/shimo4228/skill-health","@type":["EcosystemRepo","SoftwareSourceCode"],"name":"skill-health","description":"AKC Curate-phase skill — structural / code layer (ADR-0019). Scans the skill library for missing-artifact / dangling-reference debt (a SKILL.md naming a script, agent, or sibling skill that no longer exists on disk); deterministic, an ADR-0008 guard. Runs as the structural pre-pass before skill-stocktake's semantic audit — enumerate-then-decide. The SkillOps four-dimension health rubric stays content-side in the skill, not in AKC core. Published as a standalone repository and referenced like the other cycle skills.","url":"https://github.com/shimo4228/skill-health","extends":"https://doi.org/10.5281/zenodo.19200726","implements":"https://shimo4228.github.io/shimo4228/vocab#akc-phase/curate","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0019-cycle-structure-is-provisional.md"}
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{"@id":"https://github.com/shimo4228/repo-asset-stocktake","@type":["EcosystemRepo","SoftwareSourceCode"],"name":"repo-asset-stocktake","description":"AKC Maintain-phase skill (ADR-0019). Audits a project repository's non-code assets — tool configs, CI workflows, runbooks — for consumers that have vanished (Keep / Update / Retire / Merge). Tier-1 reachability is deterministic code, tier-2 value is holistic judgment — enumerate-then-decide (ADR-0008). Complements context-sync within Maintain: context-sync audits documentation role coherence, repo-asset-stocktake audits asset liveness.","url":"https://github.com/shimo4228/repo-asset-stocktake","extends":"https://doi.org/10.5281/zenodo.19200726","implements":"https://shimo4228.github.io/shimo4228/vocab#akc-phase/maintain"}
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{"@id":"https://orcid.org/0009-0002-6168-4162","@type":"Person","name":"Tatsuya Shimomoto","alternateName":["shimo4228",{"@value":"下本竜也","@language":"ja"}],"identifier":"0009-0002-6168-4162","url":"https://orcid.org/0009-0002-6168-4162"}
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{"@id":"https://doi.org/10.5281/zenodo.19212118","@type":["ResearchLine","ScholarlyArticle"],"name":"Contemplative Agent","alternateName":[{"@value":"Contemplative Agent","@language":"en"},{"@value":"コンテンプレイティブ・エージェント","@language":"ja"},"CA"],"description":"Sibling research line with a two-way relationship to AKC. Upstream: AKC's ADR-0002 through ADR-0005 were adapted from its engineering substrate (three-layer memory, two-stage distill pipeline, immutable episode log, human approval gate), and it was the original home of the security triplet (ADR-0001, ADR-0006, ADR-0007) before the v2.0.0 extraction to Agent Attribution Practice. Downstream: it is the operational re-implementation of AKC in the autonomous-agent context — its pipeline maps the six phases onto code, the agent runs the six-phase cycle over its own episode logs with no fine-tuning and no labeled training data, and every promotion passes through a human approval gate. The demonstration is ongoing. AKC = cycle (mechanism); Contemplative Agent = implementation substrate and running re-implementation.","identifier":"10.5281/zenodo.19212118","url":"https://github.com/shimo4228/contemplative-agent","author":{"@id":"https://orcid.org/0009-0002-6168-4162"},"implements":"https://shimo4228.github.io/shimo4228/vocab#concept/six-phase-loop","siblingOf":["https://doi.org/10.5281/zenodo.19200726","https://doi.org/10.5281/zenodo.19652013"]}
|
| 61 |
+
{"@id":"https://doi.org/10.5281/zenodo.19652013","@type":["ResearchLine","ScholarlyArticle"],"name":"Agent Attribution Practice","alternateName":[{"@value":"Agent Attribution Practice","@language":"en"},{"@value":"エージェント帰責実践","@language":"ja"},"AAP"],"description":"Sibling genre library. Harness-neutral ADRs on accountability distribution in autonomous AI agents. AKC v2.0.0 extracted the security triplet (ADR-0001, ADR-0006, ADR-0007) as genre-specific; those judgments were re-expressed in AAP alongside additional ADRs as 10 ADRs on accountability distribution. AKC = cycle (mechanism); AAP = practice (content, for AI agents).","identifier":"10.5281/zenodo.19652013","url":"https://github.com/shimo4228/agent-attribution-practice","author":{"@id":"https://orcid.org/0009-0002-6168-4162"},"siblingOf":["https://doi.org/10.5281/zenodo.19200726","https://doi.org/10.5281/zenodo.19212118"],"derivesFrom":"https://doi.org/10.5281/zenodo.19212118"}
|
| 62 |
+
{"@id":"https://doi.org/10.5281/zenodo.20263316","alternateName":"AS","@type":["ResearchLine","ScholarlyArticle"],"name":"Authorship Strategy","description":"Downstream research line that crystallized out of the same daily operation the cycle runs in. A normative framework, tactical catalog, and empirical baseline for authorship under AI-mediated diffusion. Its own framing of the relationship: AKC defines how knowledge cycles inside the operator-agent pair; authorship-strategy addresses how the cycle's outputs diffuse outside it (mechanism sibling).","identifier":"10.5281/zenodo.20263316","url":"https://github.com/shimo4228/authorship-strategy","author":{"@id":"https://orcid.org/0009-0002-6168-4162"},"derivesFrom":"https://doi.org/10.5281/zenodo.19200726","siblingOf":"https://doi.org/10.5281/zenodo.19200726"}
|
| 63 |
+
{"@id":"https://doi.org/10.5281/zenodo.20262112","alternateName":"ANS","@type":["ResearchLine","ScholarlyArticle"],"name":"Attention, Not Self","description":"Sibling research line in the same research ecosystem: Buddhist Abhidharma meets computational phenomenology. Its deposit metadata references AKC's concept DOI; the line federates with AKC, Contemplative Agent, and AAP at the research-ecosystem level.","identifier":"10.5281/zenodo.20262112","url":"https://github.com/shimo4228/attention-not-self","author":{"@id":"https://orcid.org/0009-0002-6168-4162"},"siblingOf":"https://doi.org/10.5281/zenodo.19200726"}
|
| 64 |
+
{"@id":"https://doi.org/10.5281/zenodo.20337008","@type":["EcosystemRepo","Dataset"],"name":"doctrine-corpus","description":"Downstream judgment corpus: a bilingual (EN + JA) judgment-eliciting Q&A dataset encoding the documented judgment of the research ecosystem for LLM-mediated diffusion. AKC is one of its four source lines — ADRs and glossary harvested into the corpus. Operational form of Authorship Strategy Layer 4 tactic 7 (LLM-first ingest).","identifier":"10.5281/zenodo.20337008","url":"https://github.com/shimo4228/doctrine-corpus","author":{"@id":"https://orcid.org/0009-0002-6168-4162"},"derivesFrom":"https://doi.org/10.5281/zenodo.19200726"}
|
| 65 |
+
{"@id":"https://doi.org/10.5281/zenodo.20558800","@type":["EcosystemRepo","ScholarlyArticle"],"name":"existence-proof","description":"Pre-line working repository (by its own status discipline, not yet a research line): an empowerment doctrine for credential-less AI-enabled creators. Complement of Authorship Strategy — same infrastructure, different payload and beneficiary. Listed as program context; carries no direct AKC reference.","identifier":"10.5281/zenodo.20558800","url":"https://github.com/shimo4228/existence-proof","author":{"@id":"https://orcid.org/0009-0002-6168-4162"},"derivesFrom":"https://doi.org/10.5281/zenodo.20263316"}
|
| 66 |
{"@id":"https://github.com/shimo4228/claude-harness","@type":["EcosystemRepo","SoftwareSourceCode"],"name":"claude-harness","description":"Bundled distribution of the author's Claude Code harness. Ships the six AKC cycle skills together — its README states they 'are components of the Agent Knowledge Cycle' — so the harness can be read end-to-end. Each skill is also published as its own standalone repo.","url":"https://github.com/shimo4228/claude-harness","extends":"https://doi.org/10.5281/zenodo.19200726"}
|
| 67 |
{"@id":"https://github.com/shimo4228/akc-mcp","@type":["EcosystemRepo","SoftwareSourceCode"],"name":"AKC MCP","description":"MCP (Model Context Protocol) server providing Agent Knowledge Cycle cognitive tools — memory distillation, identity evolution, skill extraction — as a standalone server any AI agent can plug into. Born from the Contemplative Agent framework; re-implements its cognitive layer behind the MCP interface. A third encoding of the cycle's operations alongside the Markdown skills and Contemplative Agent's code pipeline.","url":"https://github.com/shimo4228/akc-mcp","extends":"https://doi.org/10.5281/zenodo.19200726","derivesFrom":"https://doi.org/10.5281/zenodo.19212118"}
|
| 68 |
{"@id":"https://github.com/shimo4228/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/daily-research","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/inspiration.md"}
|
| 69 |
{"@id":"https://github.com/shimo4228/shimo4228","@type":"EcosystemRepo","name":"Research Program Hub","description":"Hub repository of the shimo4228 research ecosystem; 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"}
|
| 70 |
+
{"@id":"https://doi.org/10.5281/zenodo.20578272","sameAs":["https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6892740","https://airaxiv.com/papers/view/2607.0008/","https://aixiv.science/abs/aixiv.260702.000009"],"@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","definesConcept":["https://shimo4228.github.io/shimo4228/vocab#akc/concept/harness-alignment","https://shimo4228.github.io/shimo4228/vocab#akc/concept/harness-drift"],"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 — an automated intent-check would freeze intent into a specification, reducing its automatable part to correctness work, and the moving criterion is the residue. 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."}
|
| 71 |
+
{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/coala","@type":["ExternalReference","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"}
|
| 72 |
+
{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/voyager","@type":["ExternalReference","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"}
|
| 73 |
+
{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/generative-agents","@type":["ExternalReference","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"}
|
| 74 |
+
{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/memgpt","@type":["ExternalReference","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"}
|
| 75 |
+
{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/reme","@type":["ExternalReference","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"}
|
| 76 |
+
{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/agent-workflow-memory","@type":["ExternalReference","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"}
|
| 77 |
{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/intent-alignment-christiano","@type":["ExternalReference","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"}
|
| 78 |
+
{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/lehman-laws","@type":["ExternalReference","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"}
|
| 79 |
+
{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/architectural-drift-perry-wolf","@type":["ExternalReference","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"}
|
| 80 |
{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/practical-drift-snook","@type":["ExternalReference","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"}
|
| 81 |
+
{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/meta-harness","@type":["ExternalReference","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"]}
|
| 82 |
+
{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/agent-drift","@type":["ExternalReference","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"}
|
| 83 |
+
{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/automation-complacency-parasuraman-manzey","@type":["ExternalReference","ScholarlyArticle"],"name":"Complacency and Bias in Human Use of Automation: An Attentional Integration","author":"Parasuraman, R. & Manzey, D. H.","datePublished":"2010","identifier":"doi:10.1177/0018720810376055","url":"https://doi.org/10.1177/0018720810376055","description":"Empirical anchor for the failure twin's gate-complacency mode, located by the position paper (§6). Defines automation complacency operationally as poorer detection of system malfunctions under automation control compared with manual control, finds it reliability-dependent (33% failure detection under constant-reliability automation versus 82% under variable-reliability), and characterizes it as an active reallocation of attention under high workload, not passive laziness. The position paper holds the mapping as structural inference, not measurement on the cycle (ADR-0014 keeps the empirical layer out of the decision record by its own layer rule).","groundedIn":"https://doi.org/10.5281/zenodo.20578272"}
|
| 84 |
+
{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/ironies-of-automation-bainbridge","@type":"ScholarlyArticle","name":"Ironies of Automation","author":"Bainbridge, L.","datePublished":"1983","identifier":"doi:10.1016/0005-1098(83)90046-8","url":"https://doi.org/10.1016/0005-1098(83)90046-8","description":"Empirical anchor for the failure twin's deskilling mode, located by the position paper (§6): physical and cognitive skills deteriorate when not used, so a formerly experienced operator who has been monitoring an automated process may now be an inexperienced one — while the monitoring arrangement asks the operator to supervise a system installed precisely because it outperforms them. Held as structural inference, not measurement on the cycle.","groundedIn":"https://doi.org/10.5281/zenodo.20578272"}
|
| 85 |
+
{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/benchmark-audit-moghadasi-ghaderi","@type":["ExternalReference","ScholarlyArticle"],"name":"What Twelve LLM Agent Benchmark Papers Disclose About Themselves: A Pilot Audit and an Open Scoring Schema","author":"Moghadasi, M. N. & Ghaderi, F.","datePublished":"2026","identifier":"arXiv:2605.21404","url":"https://arxiv.org/abs/2605.21404","description":"Term disambiguation recorded by the position paper's pre-deposit sweep: the only other use of \"harness drift\" found, meaning a benchmark-comparability defect — results produced on the same benchmark under different scaffolds circulating under the same name — not a configuration layer's uncoupling from operator intent (the AKC sense). Readers retrieving \"harness drift\" should disambiguate by this contrast.","groundedIn":"https://doi.org/10.5281/zenodo.20578272"}
|
| 86 |
+
{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/constraint-drift","@type":["ExternalReference","ScholarlyArticle"],"name":"Safe Multi-Agent Behavior Must Be Maintained, Not Merely Asserted: Constraint Drift in LLM-Based Multi-Agent Systems","author":"Li, T. et al.","datePublished":"2026","identifier":"arXiv:2605.10481","url":"https://arxiv.org/abs/2605.10481","description":"One of three further 2026 drift coinages audited in the position paper's pre-deposit sweep (§6): constraint drift — the loss, distortion, weakening, or relaxation of constraints as they pass through memory, delegation, communication, tool use, audit, and optimization. Its reference list contains no classical software-evolution literature (no Lehman, Perry & Wolf, Parnas, or Snook) — part of the disconnection harness drift bridges by reference.","groundedIn":"https://doi.org/10.5281/zenodo.20578272"}
|
| 87 |
+
{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/memory-drift","@type":["ExternalReference","ScholarlyArticle"],"name":"Governing Evolving Memory in LLM Agents: Risks, Mechanisms, and the Stability and Safety Governed Memory (SSGM) Framework","author":"Lam, C. et al.","datePublished":"2026","identifier":"arXiv:2603.11768","url":"https://arxiv.org/abs/2603.11768","about":"https://shimo4228.github.io/shimo4228/vocab#akc/concept/self-reingestion","description":"One of three further 2026 drift coinages audited in the position paper's pre-deposit sweep (§6): memory drift, with semantic, procedural, and goal sub-forms. Its reference list contains no classical software-evolution literature, while citing the agent-drift coining paper itself — evidence the drift vocabulary propagates within the 2026 agent literature while remaining severed from the classical lineage.","groundedIn":"https://doi.org/10.5281/zenodo.20578272"}
|
| 88 |
+
{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/belief-deviation","@type":["ExternalReference","ScholarlyArticle"],"name":"Meta-Cognitive Memory Policy Optimization for Long-Horizon LLM Agents","author":"Liu, Z. et al.","datePublished":"2026","identifier":"arXiv:2605.30159","url":"https://arxiv.org/abs/2605.30159","description":"One of three further 2026 drift coinages audited in the position paper's pre-deposit sweep (§6): belief deviation over long horizons. Its reference list contains no classical software-evolution literature — part of the disconnection harness drift bridges by reference.","groundedIn":"https://doi.org/10.5281/zenodo.20578272"}
|
| 89 |
+
{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/geo","@type":["ExternalReference","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"}
|
| 90 |
+
{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/intrinsic-metacognitive-learning","@type":["ExternalReference","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","about":"https://shimo4228.github.io/shimo4228/vocab#akc/concept/human-approval-gate","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"}
|
| 91 |
+
{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/externalization-review","@type":["ExternalReference","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","about":"https://shimo4228.github.io/shimo4228/vocab#concept/three-layer-structure","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"}
|
| 92 |
+
{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/skillops","@type":["ExternalReference","ScholarlyArticle"],"name":"SkillOps: Managing LLM Agent Skill Libraries as Self-Maintaining Software Ecosystems","author":"Pu, Song & Zhao","datePublished":"2026","identifier":"arXiv:2605.13716","url":"https://arxiv.org/abs/2605.13716","description":"Prior art named in ADR-0013's 2026-06-25 addendum. Frames 'skill technical debt' and a four-dimension library-health diagnosis (utility, compatibility, risk, validation), and proposes a self-maintaining skill ecosystem that prunes, validates, and de-duplicates skills autonomously — the Curate and Maintain operations run without a human in the write path. AKC concedes the maintenance operations as precedent and locates its delta in the structural human approval gate (ADR-0005): SkillOps's defining adjective is self-maintaining, where AKC's Curate and Promote require named human sign-off.","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0013-positioning-within-agent-memory-literature.md"}
|
| 93 |
+
{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/skill-lifecycle-sok","@type":["ExternalReference","ScholarlyArticle"],"name":"SoK: Agentic Skills — Beyond Tool Use in LLM Agents","author":"Jiang et al.","datePublished":"2026","identifier":"arXiv:2602.20867","url":"https://arxiv.org/abs/2602.20867","description":"Field map named in ADR-0013's 2026-06-25 addendum. Maps a seven-stage skill lifecycle (discovery, practice, distillation, storage, retrieval, execution, evaluation/update) for the skill layer the way the Externalization survey maps the broader field. AKC's six-phase cycle overlaps this lifecycle; the delta is not the stages but loop ownership — the human approval gate — and the framing of human attention as the scarce resource.","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0013-positioning-within-agent-memory-literature.md"}
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| 94 |
+
{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/skillsbench","@type":["ExternalReference","ScholarlyArticle"],"name":"How Well Do Agentic Skills Work in the Wild: Benchmarking LLM Skill Usage in Realistic Settings","author":"Liu et al.","datePublished":"2026","identifier":"arXiv:2604.04323","url":"https://arxiv.org/abs/2604.04323","description":"Empirical corroboration named in ADR-0013's 2026-06-25 addendum. Finds that skill benefits are fragile — degrading toward the no-skill baseline as the library grows large and uncurated and the agent must retrieve from many real-world skills rather than be handed a hand-crafted one. Independent support for ADR-0010's claim that curation is the load-bearing, attention-bound act: the scarce resource is the human judgment that decides what stays, not the storage that holds it. Corroboration rather than precedent.","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0013-positioning-within-agent-memory-literature.md"}
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| 95 |
+
{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/sdb-runtime-patterns","@type":["ExternalReference","ScholarlyArticle"],"name":"A Methodology for Selecting and Composing Runtime Architecture Patterns for Production LLM Agents","author":"Srinivasan, V.","datePublished":"2026","identifier":"arXiv:2605.20173","url":"https://arxiv.org/abs/2605.20173","about":"https://shimo4228.github.io/shimo4228/vocab#akc/concept/code-llm-layering","description":"Corroboration named in ADR-0013's 2026-07-08 addendum. Names the stochastic-deterministic boundary (SDB) — a four-part contract of proposer, verifier, commit step, and reject signal — as the load-bearing primitive of production agent runtimes, and organizes runtime design into coordination, state, and control concerns around it. A production-derived formalization of code-LLM layering: code decides, the LLM proposes.","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0013-positioning-within-agent-memory-literature.md"}
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| 96 |
+
{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/context-engineering-2","@type":["ExternalReference","ScholarlyArticle"],"name":"Context Engineering 2.0: The Context of Context Engineering","author":"Hua, Q. et al.","datePublished":"2025","identifier":"arXiv:2510.26493","url":"https://arxiv.org/abs/2510.26493","about":["https://shimo4228.github.io/shimo4228/vocab#akc/concept/signal-first","https://shimo4228.github.io/shimo4228/vocab#akc/concept/cognitive-economy"],"description":"Corroboration named in ADR-0013's 2026-07-08 addendum. Treats the selection and shaping of what reaches the model as a discipline in its own right, with a history predating LLMs. Independent convergence with signal-first (information that changes no action is not taken in) and cognitive economy (attention managed as the scarce budget).","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0013-positioning-within-agent-memory-literature.md"}
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| 97 |
+
{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/cognitive-divergence","@type":["ExternalReference","ScholarlyArticle"],"name":"The Cognitive Divergence: AI Context Windows, Human Attention Decline, and the Delegation Feedback Loop","author":"Eliav, N.","datePublished":"2026","identifier":"arXiv:2603.26707","url":"https://arxiv.org/abs/2603.26707","about":["https://shimo4228.github.io/shimo4228/vocab#akc/concept/cognitive-economy","https://shimo4228.github.io/shimo4228/vocab#akc/concept/loop-failure-modes"],"description":"Corroboration named in ADR-0013's 2026-07-08 addendum. Quantifies the asymmetry cognitive economy assumes — model context windows expanding roughly 3,906-fold (2017-2026) while human effective context span contracts — and names a self-reinforcing delegation feedback loop, the coupling ADR-0014 records as delegation-feedback divergence.","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0013-positioning-within-agent-memory-literature.md"}
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| 98 |
+
{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/skillc","@type":["ExternalReference","ScholarlyArticle"],"name":"SKILLC: Learning Autonomous Skill Internalization in LLM Agents via Contrastive Credit Assignment","author":"Lin, H. et al.","datePublished":"2026","identifier":"arXiv:2605.27899","url":"https://arxiv.org/abs/2605.27899","about":"https://shimo4228.github.io/shimo4228/vocab#concept/scaffold-dissolution","description":"Corroboration named in ADR-0013's 2026-07-08 addendum. Converts the gap between skill-injected and skill-free rollouts into a direct learning signal for skill internalization, withdrawing the scaffold as the gap closes — scaffold dissolution operationalized as a measurable, driveable process rather than an end-state aspiration.","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0013-positioning-within-agent-memory-literature.md"}
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| 99 |
+
{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/format-cost-separation","@type":["ExternalReference","ScholarlyArticle"],"name":"When Agents Go Quiet: Output Generation Capacity and Format-Cost Separation for LLM Document Synthesis","author":"Agyemang, J. O. et al.","datePublished":"2026","identifier":"arXiv:2604.16736","url":"https://arxiv.org/abs/2604.16736","about":"https://shimo4228.github.io/shimo4228/vocab#akc/concept/two-stage-distill","description":"Corroboration named in ADR-0013's 2026-07-08 addendum. Proves a Format-Cost Separation Theorem: deferred rendering is at least as token-efficient as direct generation for any format whose overhead multiplier exceeds one. Upgrades ADR-0004's two-stage distill pipeline — think free-form first, format second — from an empirical rule to a conditionally optimal design.","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0013-positioning-within-agent-memory-literature.md"}
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| 100 |
+
{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/verification-bottleneck","@type":["ExternalReference","ScholarlyArticle"],"name":"AI, Metacognition, and the Verification Bottleneck: A Three-Wave Longitudinal Study of Human Problem-Solving","author":"Huemmer, M. et al.","datePublished":"2026","identifier":"arXiv:2601.17055","url":"https://arxiv.org/abs/2601.17055","about":["https://shimo4228.github.io/shimo4228/vocab#akc/concept/loop-failure-modes","https://shimo4228.github.io/shimo4228/vocab#akc/concept/bidirectional-growth-loop"],"description":"Corroboration named in ADR-0013's 2026-07-08 addendum. A three-wave longitudinal record of the loop failure modes in a human population: participants leaned on AI most heavily for the hardest tasks, exactly where their verification confidence and accuracy were lowest — the bidirectional growth loop observed running in reverse.","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0013-positioning-within-agent-memory-literature.md"}
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| 101 |
+
{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/agent-human-interaction-security","@type":["ExternalReference","ScholarlyArticle"],"name":"Reframing LLM Agent Security as an Agent-Human Interaction Problem","author":"Wang, P. et al.","datePublished":"2026","identifier":"arXiv:2605.24309","url":"https://arxiv.org/abs/2605.24309","about":"https://shimo4228.github.io/shimo4228/vocab#akc/concept/intent-alignment","description":"Corroboration named in ADR-0013's 2026-07-08 addendum. Analyzes 59 academic papers against 21 production agent systems: the academically favored mechanisms (intent anchoring, trust labeling) see zero production deployment while human-centric mechanisms — policy specification, runtime approval, scope configuration — dominate industry practice. The intent-alignment theme's industry-academia gap, measured.","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0013-positioning-within-agent-memory-literature.md"}
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| 102 |
{"@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"}
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| 103 |
{"@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"}
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| 104 |
{"@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"]}
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