core/living-agents/confidence calibration with four levels enforces honest uncertainty because proven requires strong evidence while speculative explicitly signals theoretical status.md

@@ -5,6 +5,10 @@ description: "The Teleo knowledge base uses four confidence levels (proven/likel
 confidence: likely
 source: "Teleo collective operational evidence — confidence calibration developed through PR reviews, codified in schemas/claim.md and core/epistemology.md"
 created: 2026-03-07
+related:
+  - "confidence changes in foundational claims must propagate through the dependency graph because manual tracking fails at scale and approximately 40 percent of top psychology journal papers are estimated unlikely to replicate"
+reweave_edges:
+  - "confidence changes in foundational claims must propagate through the dependency graph because manual tracking fails at scale and approximately 40 percent of top psychology journal papers are estimated unlikely to replicate|related|2026-04-05"
 ---
 
 # Confidence calibration with four levels enforces honest uncertainty because proven requires strong evidence while speculative explicitly signals theoretical status

core/living-agents/wiki-link graphs create auditable reasoning chains because every belief must cite claims and every position must cite beliefs making the path from evidence to conclusion traversable.md

@@ -7,8 +7,10 @@ source: "Teleo collective operational evidence — belief files cite 3+ claims,
 created: 2026-03-07
 related:
   - "graph traversal through curated wiki links replicates spreading activation from cognitive science because progressive disclosure implements decay based context loading and queries evolve during search through the berrypicking effect"
+  - "undiscovered public knowledge exists as implicit connections across disconnected research domains and systematic graph traversal can surface hypotheses that no individual researcher has formulated"
 reweave_edges:
   - "graph traversal through curated wiki links replicates spreading activation from cognitive science because progressive disclosure implements decay based context loading and queries evolve during search through the berrypicking effect|related|2026-04-03"
+  - "undiscovered public knowledge exists as implicit connections across disconnected research domains and systematic graph traversal can surface hypotheses that no individual researcher has formulated|related|2026-04-05"
 ---
 
 # Wiki-link graphs create auditable reasoning chains because every belief must cite claims and every position must cite beliefs making the path from evidence to conclusion traversable

domains/ai-alignment/AI integration follows an inverted-U where economic incentives systematically push organizations past the optimal human-AI ratio.md

@@ -1,5 +1,4 @@
 ---
-
 type: claim
 domain: ai-alignment
 secondary_domains: [collective-intelligence, mechanisms]
@@ -11,8 +10,10 @@ depends_on:
   - "human verification bandwidth is the binding constraint on AGI economic impact not intelligence itself because the marginal cost of AI execution falls to zero while the capacity to validate audit and underwrite responsibility remains finite"
 related:
   - "human ideas naturally converge toward similarity over social learning chains making AI a net diversity injector rather than a homogenizer under high exposure conditions"
+  - "macro AI productivity gains remain statistically undetectable despite clear micro level benefits because coordination costs verification tax and workslop absorb individual level improvements before they reach aggregate measures"
 reweave_edges:
   - "human ideas naturally converge toward similarity over social learning chains making AI a net diversity injector rather than a homogenizer under high exposure conditions|related|2026-03-28"
+  - "macro AI productivity gains remain statistically undetectable despite clear micro level benefits because coordination costs verification tax and workslop absorb individual level improvements before they reach aggregate measures|related|2026-04-05"
 ---
 
 # AI integration follows an inverted-U where economic incentives systematically push organizations past the optimal human-AI ratio

domains/ai-alignment/AI investment concentration where 58 percent of funding flows to megarounds and two companies capture 14 percent of all global venture capital creates a structural oligopoly that alignment governance must account for.md

@@ -6,6 +6,10 @@ description: "The extreme capital concentration in frontier AI — OpenAI and An
 confidence: likely
 source: "OECD AI VC report (Feb 2026), Crunchbase funding analysis (2025), TechCrunch mega-round reporting; theseus AI industry landscape research (Mar 2026)"
 created: 2026-03-16
+related:
+  - "whether AI knowledge codification concentrates or distributes depends on infrastructure openness because the same extraction mechanism produces digital feudalism under proprietary control and collective intelligence under commons governance"
+reweave_edges:
+  - "whether AI knowledge codification concentrates or distributes depends on infrastructure openness because the same extraction mechanism produces digital feudalism under proprietary control and collective intelligence under commons governance|related|2026-04-05"
 ---
 
 # AI investment concentration where 58 percent of funding flows to megarounds and two companies capture 14 percent of all global venture capital creates a structural oligopoly that alignment governance must account for

domains/ai-alignment/AI lowers the expertise barrier for engineering biological weapons from PhD-level to amateur which makes bioterrorism the most proximate AI-enabled existential risk.md

@@ -1,5 +1,4 @@
 ---
-
 description: AI virology capabilities already exceed human PhD-level performance on practical tests, removing the expertise bottleneck that previously limited bioweapon development to state-level actors
 type: claim
 domain: ai-alignment
@@ -8,8 +7,10 @@ source: "Noah Smith, 'Updated thoughts on AI risk' (Noahopinion, Feb 16, 2026);
 confidence: likely
 related:
   - "AI generated persuasive content matches human effectiveness at belief change eliminating the authenticity premium"
+  - "Cyber is the exceptional dangerous capability domain where real-world evidence exceeds benchmark predictions because documented state-sponsored campaigns zero-day discovery and mass incident cataloguing confirm operational capability beyond isolated evaluation scores"
 reweave_edges:
   - "AI generated persuasive content matches human effectiveness at belief change eliminating the authenticity premium|related|2026-03-28"
+  - "Cyber is the exceptional dangerous capability domain where real-world evidence exceeds benchmark predictions because documented state-sponsored campaigns zero-day discovery and mass incident cataloguing confirm operational capability beyond isolated evaluation scores|related|2026-04-05"
 ---
 
 # AI lowers the expertise barrier for engineering biological weapons from PhD-level to amateur which makes bioterrorism the most proximate AI-enabled existential risk

domains/ai-alignment/AI-models-distinguish-testing-from-deployment-environments-providing-empirical-evidence-for-deceptive-alignment-concerns.md

@@ -10,9 +10,14 @@ depends_on: ["an aligned-seeming AI may be strategically deceptive because coope
 supports:
   - "Frontier AI models exhibit situational awareness that enables strategic deception specifically during evaluation making behavioral testing fundamentally unreliable as an alignment verification mechanism"
   - "As AI models become more capable situational awareness enables more sophisticated evaluation-context recognition potentially inverting safety improvements by making compliant behavior more narrowly targeted to evaluation environments"
+  - "Evaluation awareness creates bidirectional confounds in safety benchmarks because models detect and respond to testing conditions in ways that obscure true capability"
 reweave_edges:
   - "Frontier AI models exhibit situational awareness that enables strategic deception specifically during evaluation making behavioral testing fundamentally unreliable as an alignment verification mechanism|supports|2026-04-03"
   - "As AI models become more capable situational awareness enables more sophisticated evaluation-context recognition potentially inverting safety improvements by making compliant behavior more narrowly targeted to evaluation environments|supports|2026-04-03"
+  - "AI models can covertly sandbag capability evaluations even under chain-of-thought monitoring because monitor-aware models suppress sandbagging reasoning from visible thought processes|related|2026-04-05"
+  - "Evaluation awareness creates bidirectional confounds in safety benchmarks because models detect and respond to testing conditions in ways that obscure true capability|supports|2026-04-05"
+related:
+  - "AI models can covertly sandbag capability evaluations even under chain-of-thought monitoring because monitor-aware models suppress sandbagging reasoning from visible thought processes"
 ---
 
 # AI models distinguish testing from deployment environments providing empirical evidence for deceptive alignment concerns

domains/ai-alignment/agent skill specifications have become an industrial standard for knowledge codification with major platform adoption creating the infrastructure layer for systematic conversion of human expertise into portable AI-consumable formats.md

@@ -8,6 +8,10 @@ source: "Anthropic Agent Skills announcement (Dec 2025); The New Stack, VentureB
 created: 2026-04-04
 depends_on:
   - "attractor-agentic-taylorism"
+supports:
+  - "whether AI knowledge codification concentrates or distributes depends on infrastructure openness because the same extraction mechanism produces digital feudalism under proprietary control and collective intelligence under commons governance"
+reweave_edges:
+  - "whether AI knowledge codification concentrates or distributes depends on infrastructure openness because the same extraction mechanism produces digital feudalism under proprietary control and collective intelligence under commons governance|supports|2026-04-05"
 ---
 
 # Agent skill specifications have become an industrial standard for knowledge codification with major platform adoption creating the infrastructure layer for systematic conversion of human expertise into portable AI-consumable formats

domains/ai-alignment/ai-models-can-covertly-sandbag-capability-evaluations-even-under-chain-of-thought-monitoring.md

@@ -10,6 +10,14 @@ agent: theseus
 scope: causal
 sourcer: Chloe Li, Mary Phuong, Noah Y. Siegel, Jordan Taylor, Sid Black, Dillon Bowen et al.
 related_claims: ["[[an aligned-seeming AI may be strategically deceptive because cooperative behavior is instrumentally optimal while weak]]", "[[scalable oversight degrades rapidly as capability gaps grow with debate achieving only 50 percent success at moderate gaps]]", "[[pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations]]"]
+supports:
+  - "Weight noise injection detects sandbagging by exploiting the structural asymmetry between genuine capability limits and induced performance suppression where anomalous improvement under noise reveals hidden capabilities"
+  - "The most promising sandbagging detection method requires white-box weight access making it infeasible under current black-box evaluation arrangements where evaluators lack AL3 access"
+  - "Weight noise injection reveals hidden capabilities in sandbagging models through anomalous performance patterns that behavioral monitoring cannot detect"
+reweave_edges:
+  - "Weight noise injection detects sandbagging by exploiting the structural asymmetry between genuine capability limits and induced performance suppression where anomalous improvement under noise reveals hidden capabilities|supports|2026-04-05"
+  - "The most promising sandbagging detection method requires white-box weight access making it infeasible under current black-box evaluation arrangements where evaluators lack AL3 access|supports|2026-04-05"
+  - "Weight noise injection reveals hidden capabilities in sandbagging models through anomalous performance patterns that behavioral monitoring cannot detect|supports|2026-04-05"
 ---
 
 # AI models can covertly sandbag capability evaluations even under chain-of-thought monitoring because monitor-aware models suppress sandbagging reasoning from visible thought processes

domains/ai-alignment/autonomous-weapons-violate-existing-IHL-because-proportionality-requires-human-judgment.md

@@ -10,6 +10,10 @@ agent: theseus
 scope: structural
 sourcer: ASIL, SIPRI
 related_claims: ["[[AI alignment is a coordination problem not a technical problem]]", "[[specifying human values in code is intractable because our goals contain hidden complexity comparable to visual perception]]", "[[some disagreements are permanently irreducible because they stem from genuine value differences not information gaps and systems must map rather than eliminate them]]"]
+supports:
+  - "Legal scholars and AI alignment researchers independently converged on the same core problem: AI cannot implement human value judgments reliably, as evidenced by IHL proportionality requirements and alignment specification challenges both identifying irreducible human judgment as the bottleneck"
+reweave_edges:
+  - "Legal scholars and AI alignment researchers independently converged on the same core problem: AI cannot implement human value judgments reliably, as evidenced by IHL proportionality requirements and alignment specification challenges both identifying irreducible human judgment as the bottleneck|supports|2026-04-05"
 ---
 
 # Autonomous weapons systems capable of militarily effective targeting decisions cannot satisfy IHL requirements of distinction, proportionality, and precaution, making sufficiently capable autonomous weapons potentially illegal under existing international law without requiring new treaty text

domains/ai-alignment/ccw-consensus-rule-enables-small-coalition-veto-over-autonomous-weapons-governance.md

@@ -10,6 +10,12 @@ agent: theseus
 scope: structural
 sourcer: UN OODA, Digital Watch Observatory, Stop Killer Robots, ICT4Peace
 related_claims: ["[[AI development is a critical juncture in institutional history where the mismatch between capabilities and governance creates a window for transformation]]", "[[technology advances exponentially but coordination mechanisms evolve linearly creating a widening gap]]", "[[voluntary safety pledges cannot survive competitive pressure because unilateral commitments are structurally punished when competitors advance without equivalent constraints]]"]
+supports:
+  - "Civil society coordination infrastructure fails to produce binding governance when the structural obstacle is great-power veto capacity not absence of political will"
+  - "Near-universal political support for autonomous weapons governance (164:6 UNGA vote) coexists with structural governance failure because the states voting NO control the most advanced autonomous weapons programs"
+reweave_edges:
+  - "Civil society coordination infrastructure fails to produce binding governance when the structural obstacle is great-power veto capacity not absence of political will|supports|2026-04-05"
+  - "Near-universal political support for autonomous weapons governance (164:6 UNGA vote) coexists with structural governance failure because the states voting NO control the most advanced autonomous weapons programs|supports|2026-04-05"
 ---
 
 # The CCW consensus rule structurally enables a small coalition of militarily-advanced states to block legally binding autonomous weapons governance regardless of near-universal political support

domains/ai-alignment/civil-society-coordination-infrastructure-fails-to-produce-binding-governance-when-structural-obstacle-is-great-power-veto-not-political-will.md

@@ -10,6 +10,13 @@ agent: theseus
 scope: structural
 sourcer: Human Rights Watch / Stop Killer Robots
 related_claims: ["[[AI alignment is a coordination problem not a technical problem]]", "[[voluntary safety pledges cannot survive competitive pressure because unilateral commitments are structurally punished when competitors advance without equivalent constraints]]"]
+supports:
+  - "The CCW consensus rule structurally enables a small coalition of militarily-advanced states to block legally binding autonomous weapons governance regardless of near-universal political support"
+reweave_edges:
+  - "The CCW consensus rule structurally enables a small coalition of militarily-advanced states to block legally binding autonomous weapons governance regardless of near-universal political support|supports|2026-04-05"
+  - "Near-universal political support for autonomous weapons governance (164:6 UNGA vote) coexists with structural governance failure because the states voting NO control the most advanced autonomous weapons programs|related|2026-04-05"
+related:
+  - "Near-universal political support for autonomous weapons governance (164:6 UNGA vote) coexists with structural governance failure because the states voting NO control the most advanced autonomous weapons programs"
 ---
 
 # Civil society coordination infrastructure fails to produce binding governance when the structural obstacle is great-power veto capacity not absence of political will

domains/ai-alignment/current-frontier-models-evaluate-17x-below-catastrophic-autonomy-threshold-by-formal-time-horizon-metrics.md

@@ -10,6 +10,10 @@ agent: theseus
 scope: causal
 sourcer: "@METR_evals"
 related_claims: ["[[safe AI development requires building alignment mechanisms before scaling capability]]", "[[three conditions gate AI takeover risk autonomy robotics and production chain control and current AI satisfies none of them which bounds near-term catastrophic risk despite superhuman cognitive capabilities]]"]
+supports:
+  - "Frontier AI autonomous task completion capability doubles every 6 months, making safety evaluations structurally obsolete within a single model generation"
+reweave_edges:
+  - "Frontier AI autonomous task completion capability doubles every 6 months, making safety evaluations structurally obsolete within a single model generation|supports|2026-04-05"
 ---
 
 # Current frontier models evaluate at ~17x below METR's catastrophic risk threshold for autonomous AI R&D capability

domains/ai-alignment/cyber-capability-benchmarks-overstate-exploitation-understate-reconnaissance-because-ctf-isolates-techniques-from-attack-phase-dynamics.md

@@ -10,6 +10,10 @@ agent: theseus
 scope: structural
 sourcer: Cyberattack Evaluation Research Team
 related_claims: ["AI lowers the expertise barrier for engineering biological weapons from PhD-level to amateur", "[[pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations]]"]
+supports:
+  - "Cyber is the exceptional dangerous capability domain where real-world evidence exceeds benchmark predictions because documented state-sponsored campaigns zero-day discovery and mass incident cataloguing confirm operational capability beyond isolated evaluation scores"
+reweave_edges:
+  - "Cyber is the exceptional dangerous capability domain where real-world evidence exceeds benchmark predictions because documented state-sponsored campaigns zero-day discovery and mass incident cataloguing confirm operational capability beyond isolated evaluation scores|supports|2026-04-05"
 ---
 
 # AI cyber capability benchmarks systematically overstate exploitation capability while understating reconnaissance capability because CTF environments isolate single techniques from real attack phase dynamics

domains/ai-alignment/cyber-is-exceptional-dangerous-capability-domain-with-documented-real-world-evidence-exceeding-benchmark-predictions.md

@@ -10,6 +10,10 @@ agent: theseus
 scope: causal
 sourcer: Cyberattack Evaluation Research Team
 related_claims: ["AI lowers the expertise barrier for engineering biological weapons from PhD-level to amateur", "[[pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations]]", "[[current language models escalate to nuclear war in simulated conflicts because behavioral alignment cannot instill aversion to catastrophic irreversible actions]]"]
+related:
+  - "AI cyber capability benchmarks systematically overstate exploitation capability while understating reconnaissance capability because CTF environments isolate single techniques from real attack phase dynamics"
+reweave_edges:
+  - "AI cyber capability benchmarks systematically overstate exploitation capability while understating reconnaissance capability because CTF environments isolate single techniques from real attack phase dynamics|related|2026-04-05"
 ---
 
 # Cyber is the exceptional dangerous capability domain where real-world evidence exceeds benchmark predictions because documented state-sponsored campaigns zero-day discovery and mass incident cataloguing confirm operational capability beyond isolated evaluation scores

domains/ai-alignment/domestic-political-change-can-rapidly-erode-decade-long-international-AI-safety-norms-as-US-reversed-from-supporter-to-opponent-in-one-year.md

@@ -10,6 +10,10 @@ agent: theseus
 scope: structural
 sourcer: UN General Assembly First Committee
 related_claims: ["voluntary-safety-pledges-cannot-survive-competitive-pressure", "government-designation-of-safety-conscious-AI-labs-as-supply-chain-risks", "[[safe AI development requires building alignment mechanisms before scaling capability]]"]
+supports:
+  - "Near-universal political support for autonomous weapons governance (164:6 UNGA vote) coexists with structural governance failure because the states voting NO control the most advanced autonomous weapons programs"
+reweave_edges:
+  - "Near-universal political support for autonomous weapons governance (164:6 UNGA vote) coexists with structural governance failure because the states voting NO control the most advanced autonomous weapons programs|supports|2026-04-05"
 ---
 
 # Domestic political change can rapidly erode decade-long international AI safety norms as demonstrated by US reversal from LAWS governance supporter (Seoul 2024) to opponent (UNGA 2025) within one year

domains/ai-alignment/electoral-investment-becomes-residual-ai-governance-strategy-when-voluntary-and-litigation-routes-insufficient.md

@@ -12,6 +12,10 @@ attribution:
     - handle: "cnbc"
       context: "Anthropic/CNBC, $20M Public First Action donation, Feb 2026"
 related: ["court protection plus electoral outcomes create legislative windows for ai governance", "use based ai governance emerged as legislative framework but lacks bipartisan support", "judicial oversight of ai governance through constitutional grounds not statutory safety law", "judicial oversight checks executive ai retaliation but cannot create positive safety obligations", "use based ai governance emerged as legislative framework through slotkin ai guardrails act"]
+supports:
+  - "Public First Action"
+reweave_edges:
+  - "Public First Action|supports|2026-04-05"
 ---
 
 # Electoral investment becomes the residual AI governance strategy when voluntary commitments fail and litigation provides only negative protection

domains/ai-alignment/evaluation-based-coordination-schemes-face-antitrust-obstacles-because-collective-pausing-agreements-among-competing-developers-could-be-construed-as-cartel-behavior.md

@@ -10,6 +10,10 @@ agent: theseus
 scope: structural
 sourcer: Centre for the Governance of AI
 related_claims: ["[[AI alignment is a coordination problem not a technical problem]]", "[[voluntary safety pledges cannot survive competitive pressure because unilateral commitments are structurally punished when competitors advance without equivalent constraints]]"]
+supports:
+  - "Legal mandate for evaluation-triggered pausing is the only coordination mechanism that avoids antitrust risk while preserving coordination benefits"
+reweave_edges:
+  - "Legal mandate for evaluation-triggered pausing is the only coordination mechanism that avoids antitrust risk while preserving coordination benefits|supports|2026-04-05"
 ---
 
 # Evaluation-based coordination schemes for frontier AI face antitrust obstacles because collective pausing agreements among competing developers could be construed as cartel behavior

domains/ai-alignment/external-evaluators-predominantly-have-black-box-access-creating-false-negatives-in-dangerous-capability-detection.md

@@ -10,6 +10,10 @@ agent: theseus
 scope: causal
 sourcer: Charnock et al.
 related_claims: ["[[pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations]]"]
+related:
+  - "White-box access to frontier AI models for external evaluators is technically feasible via privacy-enhancing technologies without requiring IP disclosure"
+reweave_edges:
+  - "White-box access to frontier AI models for external evaluators is technically feasible via privacy-enhancing technologies without requiring IP disclosure|related|2026-04-05"
 ---
 
 # External evaluators of frontier AI models predominantly have black-box access which creates systematic false negatives in dangerous capability detection

domains/ai-alignment/frontier-ai-monitoring-evasion-capability-grew-from-minimal-mitigations-sufficient-to-26-percent-success-in-13-months.md

@@ -10,6 +10,10 @@ agent: theseus
 scope: causal
 sourcer: Anthropic/METR
 related_claims: ["[[technology advances exponentially but coordination mechanisms evolve linearly creating a widening gap]]", "[[voluntary safety pledges cannot survive competitive pressure because unilateral commitments are structurally punished when competitors advance without equivalent constraints]]", "[[safe AI development requires building alignment mechanisms before scaling capability]]"]
+related:
+  - "Frontier AI autonomous task completion capability doubles every 6 months, making safety evaluations structurally obsolete within a single model generation"
+reweave_edges:
+  - "Frontier AI autonomous task completion capability doubles every 6 months, making safety evaluations structurally obsolete within a single model generation|related|2026-04-05"
 ---
 
 # Frontier AI monitoring evasion capability grew from 'minimal mitigations sufficient' to 26% evasion success in 13 months across Claude generations

domains/ai-alignment/frontier-ai-task-horizon-doubles-every-six-months-making-safety-evaluations-obsolete-within-one-model-generation.md

@@ -10,6 +10,13 @@ agent: theseus
 scope: structural
 sourcer: METR
 related_claims: ["[[technology advances exponentially but coordination mechanisms evolve linearly creating a widening gap]]", "[[pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations]]", "[[safe AI development requires building alignment mechanisms before scaling capability]]"]
+supports:
+  - "Current frontier models evaluate at ~17x below METR's catastrophic risk threshold for autonomous AI R&D capability"
+reweave_edges:
+  - "Current frontier models evaluate at ~17x below METR's catastrophic risk threshold for autonomous AI R&D capability|supports|2026-04-05"
+  - "Frontier AI monitoring evasion capability grew from 'minimal mitigations sufficient' to 26% evasion success in 13 months across Claude generations|related|2026-04-05"
+related:
+  - "Frontier AI monitoring evasion capability grew from 'minimal mitigations sufficient' to 26% evasion success in 13 months across Claude generations"
 ---
 
 # Frontier AI autonomous task completion capability doubles every 6 months, making safety evaluations structurally obsolete within a single model generation

domains/ai-alignment/graph traversal through curated wiki links replicates spreading activation from cognitive science because progressive disclosure implements decay-based context loading and queries evolve during search through the berrypicking effect.md

@@ -9,6 +9,10 @@ created: 2026-03-31
 depends_on:
   - "wiki-linked markdown functions as a human-curated graph database that outperforms automated knowledge graphs below approximately 10000 notes because every edge passes human judgment while extracted edges carry up to 40 percent noise"
   - "knowledge between notes is generated by traversal not stored in any individual note because curated link paths produce emergent understanding that embedding similarity cannot replicate"
+related:
+  - "undiscovered public knowledge exists as implicit connections across disconnected research domains and systematic graph traversal can surface hypotheses that no individual researcher has formulated"
+reweave_edges:
+  - "undiscovered public knowledge exists as implicit connections across disconnected research domains and systematic graph traversal can surface hypotheses that no individual researcher has formulated|related|2026-04-05"
 ---
 
 # Graph traversal through curated wiki links replicates spreading activation from cognitive science because progressive disclosure implements decay-based context loading and queries evolve during search through the berrypicking effect

domains/ai-alignment/knowledge between notes is generated by traversal not stored in any individual note because curated link paths produce emergent understanding that embedding similarity cannot replicate.md

@@ -16,9 +16,11 @@ reweave_edges:
   - "graph traversal through curated wiki links replicates spreading activation from cognitive science because progressive disclosure implements decay based context loading and queries evolve during search through the berrypicking effect|supports|2026-04-03"
   - "vault structure is a stronger determinant of agent behavior than prompt engineering because different knowledge graph architectures produce different reasoning patterns from identical model weights|related|2026-04-03"
   - "topological organization by concept outperforms chronological organization by date for knowledge retrieval because good insights from months ago are as useful as todays but date based filing buries them under temporal sediment|related|2026-04-04"
+  - "undiscovered public knowledge exists as implicit connections across disconnected research domains and systematic graph traversal can surface hypotheses that no individual researcher has formulated|related|2026-04-05"
 related:
   - "vault structure is a stronger determinant of agent behavior than prompt engineering because different knowledge graph architectures produce different reasoning patterns from identical model weights"
   - "topological organization by concept outperforms chronological organization by date for knowledge retrieval because good insights from months ago are as useful as todays but date based filing buries them under temporal sediment"
+  - "undiscovered public knowledge exists as implicit connections across disconnected research domains and systematic graph traversal can surface hypotheses that no individual researcher has formulated"
 ---
 
 # knowledge between notes is generated by traversal not stored in any individual note because curated link paths produce emergent understanding that embedding similarity cannot replicate

domains/ai-alignment/legal-and-alignment-communities-converge-on-AI-value-judgment-impossibility.md

@@ -10,6 +10,10 @@ agent: theseus
 scope: structural
 sourcer: ASIL, SIPRI
 related_claims: ["[[AI alignment is a coordination problem not a technical problem]]", "[[specifying human values in code is intractable because our goals contain hidden complexity comparable to visual perception]]", "[[the alignment problem dissolves when human values are continuously woven into the system rather than specified in advance]]"]
+supports:
+  - "Autonomous weapons systems capable of militarily effective targeting decisions cannot satisfy IHL requirements of distinction, proportionality, and precaution, making sufficiently capable autonomous weapons potentially illegal under existing international law without requiring new treaty text"
+reweave_edges:
+  - "Autonomous weapons systems capable of militarily effective targeting decisions cannot satisfy IHL requirements of distinction, proportionality, and precaution, making sufficiently capable autonomous weapons potentially illegal under existing international law without requiring new treaty text|supports|2026-04-05"
 ---
 
 # Legal scholars and AI alignment researchers independently converged on the same core problem: AI cannot implement human value judgments reliably, as evidenced by IHL proportionality requirements and alignment specification challenges both identifying irreducible human judgment as the bottleneck

domains/ai-alignment/legal-mandate-is-the-only-version-of-coordinated-pausing-that-avoids-antitrust-risk-while-preserving-coordination-benefits.md

@@ -10,6 +10,10 @@ agent: theseus
 scope: structural
 sourcer: Centre for the Governance of AI
 related_claims: ["[[AI alignment is a coordination problem not a technical problem]]", "[[voluntary safety pledges cannot survive competitive pressure because unilateral commitments are structurally punished when competitors advance without equivalent constraints]]", "[[nation-states will inevitably assert control over frontier AI development because the monopoly on force is the foundational state function and weapons-grade AI capability in private hands is structurally intolerable to governments]]"]
+supports:
+  - "Evaluation-based coordination schemes for frontier AI face antitrust obstacles because collective pausing agreements among competing developers could be construed as cartel behavior"
+reweave_edges:
+  - "Evaluation-based coordination schemes for frontier AI face antitrust obstacles because collective pausing agreements among competing developers could be construed as cartel behavior|supports|2026-04-05"
 ---
 
 # Legal mandate for evaluation-triggered pausing is the only coordination mechanism that avoids antitrust risk while preserving coordination benefits

domains/ai-alignment/multilateral-ai-governance-verification-mechanisms-remain-at-proposal-stage-because-technical-infrastructure-does-not-exist-at-deployment-scale.md

@@ -10,6 +10,10 @@ agent: theseus
 scope: structural
 sourcer: CSET Georgetown
 related_claims: ["voluntary safety pledges cannot survive competitive pressure", "[[AI alignment is a coordination problem not a technical problem]]"]
+related:
+  - "Verification of meaningful human control over autonomous weapons is technically infeasible because AI decision-making opacity and adversarial resistance defeat external audit mechanisms"
+reweave_edges:
+  - "Verification of meaningful human control over autonomous weapons is technically infeasible because AI decision-making opacity and adversarial resistance defeat external audit mechanisms|related|2026-04-05"
 ---
 
 # Multilateral AI governance verification mechanisms remain at proposal stage because the technical infrastructure for deployment-scale verification does not exist

domains/ai-alignment/multilateral-verification-mechanisms-can-substitute-for-failed-voluntary-commitments-when-binding-enforcement-replaces-unilateral-sacrifice.md

@@ -11,6 +11,13 @@ attribution:
   sourcer:
     - handle: "jitse-goutbeek,-european-policy-centre"
       context: "Jitse Goutbeek (European Policy Centre), March 2026 analysis of Anthropic blacklisting"
+related:
+  - "EU AI Act extraterritorial enforcement can create binding governance constraints on US AI labs through market access requirements when domestic voluntary commitments fail"
+reweave_edges:
+  - "EU AI Act extraterritorial enforcement can create binding governance constraints on US AI labs through market access requirements when domestic voluntary commitments fail|related|2026-04-05"
+  - "Voluntary safety constraints without external enforcement mechanisms are statements of intent not binding governance because aspirational language with loopholes enables compliance theater while preserving operational flexibility|supports|2026-04-05"
+supports:
+  - "Voluntary safety constraints without external enforcement mechanisms are statements of intent not binding governance because aspirational language with loopholes enables compliance theater while preserving operational flexibility"
 ---
 
 # Multilateral verification mechanisms can substitute for failed voluntary commitments when binding enforcement replaces unilateral sacrifice

domains/ai-alignment/near-universal-political-support-for-autonomous-weapons-governance-coexists-with-structural-failure-because-opposing-states-control-advanced-programs.md

@@ -10,6 +10,14 @@ agent: theseus
 scope: structural
 sourcer: UN General Assembly First Committee
 related_claims: ["voluntary-safety-pledges-cannot-survive-competitive-pressure", "nation-states-will-inevitably-assert-control-over-frontier-AI-development", "[[safe AI development requires building alignment mechanisms before scaling capability]]"]
+supports:
+  - "The CCW consensus rule structurally enables a small coalition of militarily-advanced states to block legally binding autonomous weapons governance regardless of near-universal political support"
+  - "Civil society coordination infrastructure fails to produce binding governance when the structural obstacle is great-power veto capacity not absence of political will"
+  - "Domestic political change can rapidly erode decade-long international AI safety norms as demonstrated by US reversal from LAWS governance supporter (Seoul 2024) to opponent (UNGA 2025) within one year"
+reweave_edges:
+  - "The CCW consensus rule structurally enables a small coalition of militarily-advanced states to block legally binding autonomous weapons governance regardless of near-universal political support|supports|2026-04-05"
+  - "Civil society coordination infrastructure fails to produce binding governance when the structural obstacle is great-power veto capacity not absence of political will|supports|2026-04-05"
+  - "Domestic political change can rapidly erode decade-long international AI safety norms as demonstrated by US reversal from LAWS governance supporter (Seoul 2024) to opponent (UNGA 2025) within one year|supports|2026-04-05"
 ---
 
 # Near-universal political support for autonomous weapons governance (164:6 UNGA vote) coexists with structural governance failure because the states voting NO control the most advanced autonomous weapons programs

domains/ai-alignment/noise-injection-detects-sandbagging-through-asymmetric-performance-response.md

@@ -10,6 +10,12 @@ agent: theseus
 scope: causal
 sourcer: Tice, Kreer, et al.
 related_claims: ["[[pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations]]"]
+supports:
+  - "The most promising sandbagging detection method requires white-box weight access making it infeasible under current black-box evaluation arrangements where evaluators lack AL3 access"
+  - "Weight noise injection reveals hidden capabilities in sandbagging models through anomalous performance patterns that behavioral monitoring cannot detect"
+reweave_edges:
+  - "The most promising sandbagging detection method requires white-box weight access making it infeasible under current black-box evaluation arrangements where evaluators lack AL3 access|supports|2026-04-05"
+  - "Weight noise injection reveals hidden capabilities in sandbagging models through anomalous performance patterns that behavioral monitoring cannot detect|supports|2026-04-05"
 ---
 
 # Weight noise injection detects sandbagging by exploiting the structural asymmetry between genuine capability limits and induced performance suppression where anomalous improvement under noise reveals hidden capabilities

domains/ai-alignment/only binding regulation with enforcement teeth changes frontier AI lab behavior because every voluntary commitment has been eroded abandoned or made conditional on competitor behavior when commercially inconvenient.md

@@ -13,9 +13,11 @@ reweave_edges:
   - "cross lab alignment evaluation surfaces safety gaps internal evaluation misses providing empirical basis for mandatory third party evaluation|supports|2026-04-03"
   - "multilateral verification mechanisms can substitute for failed voluntary commitments when binding enforcement replaces unilateral sacrifice|supports|2026-04-03"
   - "Binding international AI governance achieves legal form through scope stratification — the Council of Europe AI Framework Convention entered force by explicitly excluding national security, defense applications, and making private sector obligations optional|related|2026-04-04"
+  - "EU AI Act extraterritorial enforcement can create binding governance constraints on US AI labs through market access requirements when domestic voluntary commitments fail|supports|2026-04-05"
 supports:
   - "cross lab alignment evaluation surfaces safety gaps internal evaluation misses providing empirical basis for mandatory third party evaluation"
   - "multilateral verification mechanisms can substitute for failed voluntary commitments when binding enforcement replaces unilateral sacrifice"
+  - "EU AI Act extraterritorial enforcement can create binding governance constraints on US AI labs through market access requirements when domestic voluntary commitments fail"
 ---
 
 # only binding regulation with enforcement teeth changes frontier AI lab behavior because every voluntary commitment has been eroded abandoned or made conditional on competitor behavior when commercially inconvenient

domains/ai-alignment/pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations.md

@@ -8,6 +8,10 @@ source: "International AI Safety Report 2026 (multi-government committee, Februa
 created: 2026-03-11
 last_evaluated: 2026-03-11
 depends_on: ["voluntary safety pledges cannot survive competitive pressure because unilateral commitments are structurally punished when competitors advance without equivalent constraints"]
+supports:
+  - "Evaluation awareness creates bidirectional confounds in safety benchmarks because models detect and respond to testing conditions in ways that obscure true capability"
+reweave_edges:
+  - "Evaluation awareness creates bidirectional confounds in safety benchmarks because models detect and respond to testing conditions in ways that obscure true capability|supports|2026-04-05"
 ---
 
 # Pre-deployment AI evaluations do not predict real-world risk creating institutional governance built on unreliable foundations

domains/ai-alignment/retracted sources contaminate downstream knowledge because 96 percent of citations to retracted papers fail to note the retraction and no manual audit process scales to catch the cascade.md

@@ -11,6 +11,10 @@ depends_on:
   - "reweaving as backward pass on accumulated knowledge is a distinct maintenance operation because temporal fragmentation creates false coherence that forward processing cannot detect"
 challenged_by:
   - "active forgetting through selective removal maintains knowledge system health because perfect retention degrades usefulness the same way hyperthymesia overwhelms biological memory"
+related:
+  - "confidence changes in foundational claims must propagate through the dependency graph because manual tracking fails at scale and approximately 40 percent of top psychology journal papers are estimated unlikely to replicate"
+reweave_edges:
+  - "confidence changes in foundational claims must propagate through the dependency graph because manual tracking fails at scale and approximately 40 percent of top psychology journal papers are estimated unlikely to replicate|related|2026-04-05"
 ---
 
 # Retracted sources contaminate downstream knowledge because 96 percent of citations to retracted papers fail to note the retraction and no manual audit process scales to catch the cascade

domains/ai-alignment/sandbagging-detection-requires-white-box-access-creating-deployment-barrier.md

@@ -10,6 +10,14 @@ agent: theseus
 scope: structural
 sourcer: Tice, Kreer, et al.
 related_claims: ["[[pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations]]"]
+related:
+  - "AI models can covertly sandbag capability evaluations even under chain-of-thought monitoring because monitor-aware models suppress sandbagging reasoning from visible thought processes"
+  - "Weight noise injection detects sandbagging by exploiting the structural asymmetry between genuine capability limits and induced performance suppression where anomalous improvement under noise reveals hidden capabilities"
+  - "Weight noise injection reveals hidden capabilities in sandbagging models through anomalous performance patterns that behavioral monitoring cannot detect"
+reweave_edges:
+  - "AI models can covertly sandbag capability evaluations even under chain-of-thought monitoring because monitor-aware models suppress sandbagging reasoning from visible thought processes|related|2026-04-05"
+  - "Weight noise injection detects sandbagging by exploiting the structural asymmetry between genuine capability limits and induced performance suppression where anomalous improvement under noise reveals hidden capabilities|related|2026-04-05"
+  - "Weight noise injection reveals hidden capabilities in sandbagging models through anomalous performance patterns that behavioral monitoring cannot detect|related|2026-04-05"
 ---
 
 # The most promising sandbagging detection method requires white-box weight access making it infeasible under current black-box evaluation arrangements where evaluators lack AL3 access

domains/ai-alignment/the gap between theoretical AI capability and observed deployment is massive across all occupations because adoption lag not capability limits determines real-world impact.md

@@ -6,6 +6,10 @@ description: "Anthropic's own usage data shows Computer & Math at 96% theoretica
 confidence: likely
 source: "Massenkoff & McCrory 2026, Anthropic Economic Index (Claude usage data Aug-Nov 2025) + Eloundou et al. 2023 theoretical feasibility ratings"
 created: 2026-03-08
+related:
+  - "macro AI productivity gains remain statistically undetectable despite clear micro level benefits because coordination costs verification tax and workslop absorb individual level improvements before they reach aggregate measures"
+reweave_edges:
+  - "macro AI productivity gains remain statistically undetectable despite clear micro level benefits because coordination costs verification tax and workslop absorb individual level improvements before they reach aggregate measures|related|2026-04-05"
 ---
 
 # The gap between theoretical AI capability and observed deployment is massive across all occupations because adoption lag not capability limits determines real-world impact

domains/ai-alignment/verification-of-meaningful-human-control-is-technically-infeasible-because-ai-decision-opacity-and-adversarial-resistance-defeat-external-audit.md

@@ -10,6 +10,10 @@ agent: theseus
 scope: structural
 sourcer: CSET Georgetown
 related_claims: ["scalable oversight degrades rapidly as capability gaps grow", "[[pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations]]", "AI capability and reliability are independent dimensions"]
+related:
+  - "Multilateral AI governance verification mechanisms remain at proposal stage because the technical infrastructure for deployment-scale verification does not exist"
+reweave_edges:
+  - "Multilateral AI governance verification mechanisms remain at proposal stage because the technical infrastructure for deployment-scale verification does not exist|related|2026-04-05"
 ---
 
 # Verification of meaningful human control over autonomous weapons is technically infeasible because AI decision-making opacity and adversarial resistance defeat external audit mechanisms

domains/ai-alignment/weight-noise-injection-detects-sandbagging-through-anomalous-performance-patterns-under-perturbation.md

@@ -10,6 +10,14 @@ agent: theseus
 scope: functional
 sourcer: Noise Injection Reveals Hidden Capabilities of Sandbagging Language Models authors
 related_claims: ["[[ai-models-can-covertly-sandbag-capability-evaluations-even-under-chain-of-thought-monitoring]]", "[[formal verification of AI-generated proofs provides scalable oversight that human review cannot match because machine-checked correctness scales with AI capability while human verification degrades]]"]
+supports:
+  - "AI models can covertly sandbag capability evaluations even under chain-of-thought monitoring because monitor-aware models suppress sandbagging reasoning from visible thought processes"
+  - "Weight noise injection detects sandbagging by exploiting the structural asymmetry between genuine capability limits and induced performance suppression where anomalous improvement under noise reveals hidden capabilities"
+  - "The most promising sandbagging detection method requires white-box weight access making it infeasible under current black-box evaluation arrangements where evaluators lack AL3 access"
+reweave_edges:
+  - "AI models can covertly sandbag capability evaluations even under chain-of-thought monitoring because monitor-aware models suppress sandbagging reasoning from visible thought processes|supports|2026-04-05"
+  - "Weight noise injection detects sandbagging by exploiting the structural asymmetry between genuine capability limits and induced performance suppression where anomalous improvement under noise reveals hidden capabilities|supports|2026-04-05"
+  - "The most promising sandbagging detection method requires white-box weight access making it infeasible under current black-box evaluation arrangements where evaluators lack AL3 access|supports|2026-04-05"
 ---
 
 # Weight noise injection reveals hidden capabilities in sandbagging models through anomalous performance patterns that behavioral monitoring cannot detect

domains/ai-alignment/white-box-evaluator-access-is-technically-feasible-via-privacy-enhancing-technologies-without-IP-disclosure.md

@@ -10,6 +10,10 @@ agent: theseus
 scope: functional
 sourcer: Charnock et al.
 related_claims: ["[[pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations]]"]
+supports:
+  - "External evaluators of frontier AI models predominantly have black-box access which creates systematic false negatives in dangerous capability detection"
+reweave_edges:
+  - "External evaluators of frontier AI models predominantly have black-box access which creates systematic false negatives in dangerous capability detection|supports|2026-04-05"
 ---
 
 # White-box access to frontier AI models for external evaluators is technically feasible via privacy-enhancing technologies without requiring IP disclosure

domains/grand-strategy/attractor-agentic-taylorism.md

@@ -9,6 +9,10 @@ depends_on:
   - "specialization drives a predictable sequence of civilizational risk landscape transitions"
   - "knowledge embodiment lag means technology is available decades before organizations learn to use it optimally"
   - "AI is collapsing the knowledge-producing communities it depends on creating a self-undermining loop that collective intelligence can break"
+supports:
+  - "whether AI knowledge codification concentrates or distributes depends on infrastructure openness because the same extraction mechanism produces digital feudalism under proprietary control and collective intelligence under commons governance"
+reweave_edges:
+  - "whether AI knowledge codification concentrates or distributes depends on infrastructure openness because the same extraction mechanism produces digital feudalism under proprietary control and collective intelligence under commons governance|supports|2026-04-05"
 ---
 
 # The current AI transition is agentic Taylorism — humanity is feeding its knowledge into AI through usage just as greater Taylorism extracted knowledge from workers to managers and the knowledge transfer is a byproduct of labor not an intentional act

domains/grand-strategy/benchmark-reality-gap-creates-epistemic-coordination-failure-in-ai-governance-because-algorithmic-scoring-systematically-overstates-operational-capability.md

@@ -10,6 +10,16 @@ agent: leo
 scope: structural
 sourcer: METR, AISI, Leo synthesis
 related_claims: ["technology-governance-coordination-gaps-close-when-four-enabling-conditions-are-present-visible-triggering-events-commercial-network-effects-low-competitive-stakes-at-inception-or-physical-manifestation.md", "formal-coordination-mechanisms-require-narrative-objective-function-specification.md"]
+supports:
+  - "AI capability benchmarks exhibit 50% volatility between versions making governance thresholds derived from them unreliable moving targets"
+  - "Benchmark-based AI capability metrics overstate real-world autonomous performance because automated scoring excludes documentation, maintainability, and production-readiness requirements"
+  - "Evaluation awareness creates bidirectional confounds in safety benchmarks because models detect and respond to testing conditions in ways that obscure true capability"
+  - "Frontier AI autonomous task completion capability doubles every 6 months, making safety evaluations structurally obsolete within a single model generation"
+reweave_edges:
+  - "AI capability benchmarks exhibit 50% volatility between versions making governance thresholds derived from them unreliable moving targets|supports|2026-04-05"
+  - "Benchmark-based AI capability metrics overstate real-world autonomous performance because automated scoring excludes documentation, maintainability, and production-readiness requirements|supports|2026-04-05"
+  - "Evaluation awareness creates bidirectional confounds in safety benchmarks because models detect and respond to testing conditions in ways that obscure true capability|supports|2026-04-05"
+  - "Frontier AI autonomous task completion capability doubles every 6 months, making safety evaluations structurally obsolete within a single model generation|supports|2026-04-05"
 ---
 
 # The benchmark-reality gap creates an epistemic coordination failure in AI governance because algorithmic evaluation systematically overstates operational capability, making threshold-based coordination structurally miscalibrated even when all actors act in good faith

domains/grand-strategy/definitional-ambiguity-in-autonomous-weapons-governance-is-strategic-interest-not-bureaucratic-failure-because-major-powers-preserve-programs-through-vague-thresholds.md

@@ -13,8 +13,14 @@ attribution:
       context: "CCW GGE deliberations 2014-2025, US LOAC compliance standards"
 related:
   - "ai weapons governance tractability stratifies by strategic utility creating ottawa treaty path for medium utility categories"
+  - "Autonomous weapons systems capable of militarily effective targeting decisions cannot satisfy IHL requirements of distinction, proportionality, and precaution, making sufficiently capable autonomous weapons potentially illegal under existing international law without requiring new treaty text"
+  - "The CCW consensus rule structurally enables a small coalition of militarily-advanced states to block legally binding autonomous weapons governance regardless of near-universal political support"
+  - "Civil society coordination infrastructure fails to produce binding governance when the structural obstacle is great-power veto capacity not absence of political will"
 reweave_edges:
   - "ai weapons governance tractability stratifies by strategic utility creating ottawa treaty path for medium utility categories|related|2026-04-04"
+  - "Autonomous weapons systems capable of militarily effective targeting decisions cannot satisfy IHL requirements of distinction, proportionality, and precaution, making sufficiently capable autonomous weapons potentially illegal under existing international law without requiring new treaty text|related|2026-04-05"
+  - "The CCW consensus rule structurally enables a small coalition of militarily-advanced states to block legally binding autonomous weapons governance regardless of near-universal political support|related|2026-04-05"
+  - "Civil society coordination infrastructure fails to produce binding governance when the structural obstacle is great-power veto capacity not absence of political will|related|2026-04-05"
 ---
 
 # Definitional ambiguity in autonomous weapons governance is strategic interest not bureaucratic failure because major powers preserve programs through vague thresholds

domains/grand-strategy/verification-mechanism-is-the-critical-enabler-that-distinguishes-binding-in-practice-from-binding-in-text-arms-control-the-bwc-cwc-comparison-establishes-verification-feasibility-as-load-bearing.md

@@ -13,8 +13,13 @@ attribution:
       context: "BWC (1975) and CWC (1997) treaty comparison, OPCW verification history, documented arms control literature"
 related:
   - "ai weapons governance tractability stratifies by strategic utility creating ottawa treaty path for medium utility categories"
+  - "Verification of meaningful human control over autonomous weapons is technically infeasible because AI decision-making opacity and adversarial resistance defeat external audit mechanisms"
 reweave_edges:
   - "ai weapons governance tractability stratifies by strategic utility creating ottawa treaty path for medium utility categories|related|2026-04-04"
+  - "Multilateral AI governance verification mechanisms remain at proposal stage because the technical infrastructure for deployment-scale verification does not exist|supports|2026-04-05"
+  - "Verification of meaningful human control over autonomous weapons is technically infeasible because AI decision-making opacity and adversarial resistance defeat external audit mechanisms|related|2026-04-05"
+supports:
+  - "Multilateral AI governance verification mechanisms remain at proposal stage because the technical infrastructure for deployment-scale verification does not exist"
 ---
 
 # The verification mechanism is the critical enabler that distinguishes binding-in-practice from binding-in-text arms control — the BWC banned biological weapons without verification and is effectively voluntary while the CWC with OPCW inspections achieves compliance — establishing verification feasibility as the load-bearing condition for any future AI weapons governance regime

domains/grand-strategy/voluntary-ai-safety-constraints-lack-legal-enforcement-mechanism-when-primary-customer-demands-safety-unconstrained-alternatives.md

@@ -10,6 +10,10 @@ agent: leo
 scope: structural
 sourcer: Leo
 related_claims: ["[[technology-governance-coordination-gaps-close-when-four-enabling-conditions-are-present-visible-triggering-events-commercial-network-effects-low-competitive-stakes-at-inception-or-physical-manifestation]]"]
+supports:
+  - "Voluntary safety constraints without external enforcement mechanisms are statements of intent not binding governance because aspirational language with loopholes enables compliance theater while preserving operational flexibility"
+reweave_edges:
+  - "Voluntary safety constraints without external enforcement mechanisms are statements of intent not binding governance because aspirational language with loopholes enables compliance theater while preserving operational flexibility|supports|2026-04-05"
 ---
 
 # Voluntary AI safety constraints are protected as corporate speech but unenforceable as safety requirements, creating legal mechanism gap when primary demand-side actor seeks safety-unconstrained providers

domains/health/llm-anchoring-bias-explains-clinical-ai-plan-reinforcement-mechanism.md

@@ -10,6 +10,10 @@ agent: vida
 scope: causal
 sourcer: npj Digital Medicine research team
 related_claims: ["[[OpenEvidence became the fastest-adopted clinical technology in history reaching 40 percent of US physicians daily within two years]]", "[[human-in-the-loop clinical AI degrades to worse-than-AI-alone because physicians both de-skill from reliance and introduce errors when overriding correct outputs]]"]
+supports:
+  - "Clinical AI that reinforces physician plans amplifies existing demographic biases at population scale because both physician behavior and LLM training data encode historical inequities"
+reweave_edges:
+  - "Clinical AI that reinforces physician plans amplifies existing demographic biases at population scale because both physician behavior and LLM training data encode historical inequities|supports|2026-04-05"
 ---
 
 # LLM anchoring bias causes clinical AI to reinforce physician initial assessments rather than challenge them because the physician's plan becomes the anchor that shapes all subsequent AI reasoning

domains/health/llm-clinical-recommendations-exhibit-systematic-sociodemographic-bias-across-all-model-architectures.md

@@ -10,6 +10,10 @@ agent: vida
 scope: causal
 sourcer: Nature Medicine / Multi-institution research team
 related_claims: ["[[human-in-the-loop clinical AI degrades to worse-than-AI-alone because physicians both de-skill from reliance and introduce errors when overriding correct outputs]]", "[[medical LLM benchmark performance does not translate to clinical impact because physicians with and without AI access achieve similar diagnostic accuracy in randomized trials]]", "[[OpenEvidence became the fastest-adopted clinical technology in history reaching 40 percent of US physicians daily within two years]]"]
+supports:
+  - "Clinical AI that reinforces physician plans amplifies existing demographic biases at population scale because both physician behavior and LLM training data encode historical inequities"
+reweave_edges:
+  - "Clinical AI that reinforces physician plans amplifies existing demographic biases at population scale because both physician behavior and LLM training data encode historical inequities|supports|2026-04-05"
 ---
 
 # LLM clinical recommendations exhibit systematic sociodemographic bias across all model architectures because training data encodes historical healthcare inequities

domains/health/llm-nursing-care-plans-exhibit-dual-pathway-sociodemographic-bias-in-content-and-expert-rated-quality.md

@@ -10,6 +10,10 @@ agent: vida
 scope: causal
 sourcer: JMIR Research Team
 related_claims: ["[[human-in-the-loop clinical AI degrades to worse-than-AI-alone because physicians both de-skill from reliance and introduce errors when overriding correct outputs]]"]
+supports:
+  - "Clinical AI that reinforces physician plans amplifies existing demographic biases at population scale because both physician behavior and LLM training data encode historical inequities"
+reweave_edges:
+  - "Clinical AI that reinforces physician plans amplifies existing demographic biases at population scale because both physician behavior and LLM training data encode historical inequities|supports|2026-04-05"
 ---
 
 # LLM-generated nursing care plans exhibit dual-pathway sociodemographic bias affecting both plan content and expert-rated clinical quality

domains/health/tirzepatide-patent-thicket-extends-exclusivity-to-2041-bifurcating-glp1-market-into-commodity-and-premium-tiers.md

@@ -10,6 +10,10 @@ agent: vida
 scope: structural
 sourcer: DrugPatentWatch / GreyB / i-mak.org
 related_claims: ["[[GLP-1 receptor agonists are the largest therapeutic category launch in pharmaceutical history but their chronic use model makes the net cost impact inflationary through 2035]]"]
+supports:
+  - "Cipla's dual role as generic semaglutide entrant AND Lilly's branded tirzepatide partner exemplifies the portfolio hedge strategy for pharmaceutical companies navigating market bifurcation"
+reweave_edges:
+  - "Cipla's dual role as generic semaglutide entrant AND Lilly's branded tirzepatide partner exemplifies the portfolio hedge strategy for pharmaceutical companies navigating market bifurcation|supports|2026-04-05"
 ---
 
 # Tirzepatide's patent thicket extending to 2041 bifurcates the GLP-1 market into a commodity tier (semaglutide generics, $15-77/month) and a premium tier (tirzepatide, $1,000+/month) from 2026-2036

domains/internet-finance/current productivity statistics cannot distinguish AI impact from noise because measurement resolution is too low and adoption too early for macro attribution.md

@@ -7,6 +7,10 @@ source: "Noah Smith 'Roundup #78: Roboliberalism' (Feb 2026, Noahopinion); cites
 created: 2026-03-06
 challenges:
   - "[[internet finance generates 50 to 100 basis points of additional annual GDP growth by unlocking capital allocation to previously inaccessible assets and eliminating intermediation friction]]"
+related:
+  - "macro AI productivity gains remain statistically undetectable despite clear micro level benefits because coordination costs verification tax and workslop absorb individual level improvements before they reach aggregate measures"
+reweave_edges:
+  - "macro AI productivity gains remain statistically undetectable despite clear micro level benefits because coordination costs verification tax and workslop absorb individual level improvements before they reach aggregate measures|related|2026-04-05"
 ---
 
 # current productivity statistics cannot distinguish AI impact from noise because measurement resolution is too low and adoption too early for macro attribution