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@@ -25,6 +25,7 @@
 {"@id":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0017-harness-alignment-and-drift.md","@type":["ADR","TechArticle"],"name":"ADR-0017: Harness Alignment and Harness Drift","description":"Establishes two terms by derivation from established literature rather than fresh coinage — the second positioning ADR after ADR-0013, applied to the software-evolution and alignment literatures. Harness alignment: the continuous, human-gated activity of keeping an agent's harness (skills, rules, prompts, documentation) aligned with the operator's evolving intent — extends intent alignment (Christiano 2018) to the configuration layer and across time (cf. Lehman's Law of Continuing Change, 1980). Harness drift: the failure mode, in lineage with architectural drift (Perry & Wolf 1992), practical drift (Snook 2000), and agent drift (arXiv:2601.04170). Contrast: autonomous, score-driven harness optimization (Meta-Harness, arXiv:2603.28052). Also records the verified vocabulary gap: the 2026 agent-drift literature does not cite the classical software-evolution lineage."}
 {"@id":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0018-record-downstream-applications-as-first-class-context.md","@type":["ADR","TechArticle"],"name":"ADR-0018: Record Downstream Applications as First-Class Context","description":"The start-of-program repository did not know its own downstream: Contemplative Agent's description was past-direction only (upstream substrate), and four DOI-registered downstream repositories were unmentioned — surfaced as observed pain when the position paper could not cite AKC as its own primary source. Records the corrective: downstream applications are first-class context. Contemplative Agent's role is two-way (substrate for ADR-0002–0005 and operational re-implementation running the six-phase cycle over its own episode logs, every promotion human-gated, demonstration ongoing); crystallized research lines are recorded with relationship facts and DOIs only, importing no downstream content (mechanism-only rule preserved). Itself the record of a Maintain-phase execution on the repository's own self-description."}
 {"@id":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0019-cycle-structure-is-provisional.md","@type":["ADR","TechArticle"],"name":"ADR-0019: The Cycle's Structure Is Provisional — Skills, Bindings, and Phases Held Lightly","description":"AKC holds its own structure provisionally: no layer of the cycle's self-description — skills, the phase-to-skill binding, or the six-phase set — is a fixed essence. Operationalizes the Emptiness axiom (contemplative-axioms, Laukkonen et al. 2025) for AKC's own frame, correcting a reification error in which the 'bijective' binding was read as an invariant that forbade a second Curate skill. Three concrete moves: (1) the binding is reworded across graph.jsonld, the README phase table, and llms.txt from 'bound bijectively to X' to 'currently scaffolded by X (a mutable snapshot)'; (2) Curate is layered — its structural / code layer is skill-health (a dangling-reference, missing-artifact scan; an ADR-0008 guard, deterministic), its semantic / judgment layer is skill-stocktake — enumerate-then-decide, skill-health published as a standalone repository and referenced like the other cycle skills, its SkillOps four-dimension rubric staying content-side; (3) the skill set both grows (skill-health joins) and dissolves (skill-comply's compliance-checking is a candidate for substrate absorption by the host harness, recorded experimental n=1, not retired). The six-phase count is de-reified but not removed — provisional is not arbitrary; the structures remain the load-bearing current articulation, held lightly. The ADR is itself provisional. Extends ADR-0009, applies ADR-0008, bounded by ADR-0011, continues ADR-0018, grounded in contemplative-axioms (Emptiness).","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0009-akc-is-a-cycle-not-a-harness.md"}
+{"@id":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0020-readme-minimal-floor.md","@type":["ADR","TechArticle"],"name":"ADR-0020: Minimal-floor README with Single-location Theme Presentation","description":"Amends ADR-0012's README-side prescription after its fix overshot: front-loading the three core themes produced a double presentation (a four-paragraph 'What is AKC?' plus a three-subsection 'Why AKC') and a skill list spread across three tables, growing the README to 302 lines / 8 tables — observed as operator pain on 2026-07-07. The corrective: README.md moves to a minimal floor — the three themes get exactly one full presentation (in 'Why AKC', fixed order: cognitive resource → intent alignment → the cycle changes the human too), the skill list collapses to the single phase→skill table, and design principles, references, the harness-engineering comparison, and adoption levels become pointers into the existing machine-readable floor (llms-full.txt, individual ADRs) rather than second copies. ADR-0012's front-load principle (theme phrases in the first thirty lines, themes before mechanism) and its llms.txt / llms-full.txt commitments remain in force unchanged; llms-full.txt gains explicit responsibility as the canonical information floor. Second correction in the same front-door alignment family: ADR-0012 corrected invisibility, ADR-0020 corrects the overshoot into repetition. Same spirit as ADR-0019 applied to the document layer — the README's shape is a provisional structure, revised on observed pain and recorded rather than silently patched.","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0012-front-load-three-core-themes.md"}
 {"@id":"https://shimo4228.github.io/shimo4228/vocab#concept/six-phase-loop","@type":["Concept","DefinedTerm"],"name":"six-phase loop","description":"Bidirectional growth loop running Research, Extract, Curate, Promote, Measure, Maintain. Each phase is currently scaffolded by composable skills — a mutable snapshot, not a fixed bijection (ADR-0019); the cycle stays stable even as individual skills are added, layered, or dissolved.","groundedIn":"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#concept/three-layer-structure","@type":["Concept","DefinedTerm"],"name":"three-layer structure","description":"AKC's stacked architecture: principles (ADRs), patterns (design-pattern skills), implementation (composable skills). Separating layers decouples rate of change.","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0003-three-layer-distillation.md"}
 {"@id":"https://shimo4228.github.io/shimo4228/vocab#concept/scaffold-dissolution","@type":["Concept","DefinedTerm"],"name":"scaffold dissolution","description":"Property that explicit AKC skills can be dropped once the cycle has been internalized. The skills are scaffolding, not the goal. Dissolution is the intended end state, not a fallback.","groundedIn":"https://github.com/shimo4228/agent-knowledge-cycle/blob/main/docs/adr/0009-akc-is-a-cycle-not-a-harness.md"}
@@ -81,14 +82,21 @@
 {"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/ironies-of-automation-bainbridge","sameAs":"https://www.wikidata.org/wiki/Q62065802","@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"}
 {"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/benchmark-audit-moghadasi-ghaderi","sameAs":"https://www.wikidata.org/wiki/Q140181281","@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"}
 {"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/constraint-drift","sameAs":"https://www.wikidata.org/wiki/Q140181279","@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"}
-{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/memory-drift","sameAs":"https://www.wikidata.org/wiki/Q140181270","@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","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"}
+{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/memory-drift","sameAs":"https://www.wikidata.org/wiki/Q140181270","@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"}
 {"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/belief-deviation","sameAs":"https://www.wikidata.org/wiki/Q140181284","@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"}
 {"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/geo","sameAs":"https://www.wikidata.org/wiki/Q131161430","@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"}
-{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/intrinsic-metacognitive-learning","sameAs":"https://www.wikidata.org/wiki/Q140181243","@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","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":["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","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://shimo4228.github.io/shimo4228/vocab#prior-art/intrinsic-metacognitive-learning","sameAs":"https://www.wikidata.org/wiki/Q140181243","@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"}
+{"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/externalization-review","sameAs":"https://www.wikidata.org/wiki/Q140181274","@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"}
 {"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/skillops","sameAs":"https://www.wikidata.org/wiki/Q140352501","@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"}
 {"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/skill-lifecycle-sok","sameAs":"https://www.wikidata.org/wiki/Q140352503","@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"}
 {"@id":"https://shimo4228.github.io/shimo4228/vocab#prior-art/skillsbench","sameAs":"https://www.wikidata.org/wiki/Q140352504","@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"}
+{"@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"}
+{"@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"}
+{"@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"}
+{"@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"}
+{"@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"}
+{"@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"}
+{"@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"}
 {"@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"]}