reweave: connect 14 orphan claims via vector similarity

Threshold: 0.7, Haiku classification, 35 files modified.

Pentagon-Agent: Epimetheus <0144398e-4ed3-4fe2-95a3-3d72e1abf887>

core/grand-strategy/AI labor displacement follows knowledge embodiment lag phases where capital deepening precedes labor substitution and the transition timing depends on organizational restructuring not technology capability.md

@@ -10,6 +10,10 @@ depends_on:
   - "early AI adoption increases firm productivity without reducing employment suggesting capital deepening not labor replacement as the dominant mechanism"
   - "economic forces push humans out of every cognitive loop where output quality is independently verifiable because human-in-the-loop is a cost that competitive markets eliminate"
   - "knowledge embodiment lag means technology is available decades before organizations learn to use it optimally creating a productivity paradox"
+supports:
+- Does AI substitute for human labor or complement it — and at what phase does the pattern shift?
+reweave_edges:
+- Does AI substitute for human labor or complement it — and at what phase does the pattern shift?|supports|2026-04-15
 ---
 
 # AI labor displacement follows knowledge embodiment lag phases where capital deepening precedes labor substitution and the transition timing depends on organizational restructuring not technology capability

core/grand-strategy/centaur teams succeed only when role boundaries prevent humans from overriding AI in domains where AI is the stronger partner.md

@@ -11,6 +11,10 @@ depends_on:
   - "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"
   - "AI scribes reached 92 percent provider adoption in under 3 years because documentation is the rare healthcare workflow where AI value is immediate unambiguous and low-risk"
   - "scalable oversight degrades rapidly as capability gaps grow with debate achieving only 50 percent success at moderate gaps"
+supports:
+- Does human oversight improve or degrade AI clinical decision-making?
+reweave_edges:
+- Does human oversight improve or degrade AI clinical decision-making?|supports|2026-04-15
 ---
 
 # centaur teams succeed only when role boundaries prevent humans from overriding AI in domains where AI is the stronger partner

domains/ai-alignment/AI accelerates existing Molochian dynamics by removing bottlenecks not creating new misalignment because the competitive equilibrium was always catastrophic and friction was the only thing preventing convergence.md

@@ -13,9 +13,11 @@ challenged_by:
 related:
 - multipolar traps are the thermodynamic default because competition requires no infrastructure while coordination requires trust enforcement and shared information all of which are expensive and fragile
 - the absence of a societal warning signal for AGI is a structural feature not an accident because capability scaling is gradual and ambiguous and collective action requires anticipation not reaction
+- motivated reasoning among AI lab leaders is itself a primary risk vector because those with most capability to slow down have most incentive to accelerate
 reweave_edges:
 - multipolar traps are the thermodynamic default because competition requires no infrastructure while coordination requires trust enforcement and shared information all of which are expensive and fragile|related|2026-04-04
 - the absence of a societal warning signal for AGI is a structural feature not an accident because capability scaling is gradual and ambiguous and collective action requires anticipation not reaction|related|2026-04-07
+- motivated reasoning among AI lab leaders is itself a primary risk vector because those with most capability to slow down have most incentive to accelerate|related|2026-04-15
 ---
 
 # AI accelerates existing Molochian dynamics by removing bottlenecks not creating new misalignment because the competitive equilibrium was always catastrophic and friction was the only thing preventing convergence

domains/ai-alignment/AI development is a critical juncture in institutional history where the mismatch between capabilities and governance creates a window for transformation.md

@@ -7,8 +7,10 @@ source: "Web research compilation, February 2026"
 confidence: likely
 related:
 - AI governance discourse has been captured by economic competitiveness framing, inverting predicted participation patterns where China signs non-binding declarations while the US opts out
+- The international AI safety governance community faces an evidence dilemma where development pace structurally prevents adequate pre-deployment evidence accumulation
 reweave_edges:
 - AI governance discourse has been captured by economic competitiveness framing, inverting predicted participation patterns where China signs non-binding declarations while the US opts out|related|2026-04-04
+- The international AI safety governance community faces an evidence dilemma where development pace structurally prevents adequate pre-deployment evidence accumulation|related|2026-04-15
 ---
 
 Daron Acemoglu (2024 Nobel Prize in Economics) provides the institutional framework for understanding why this moment matters. His key concepts: extractive versus inclusive institutions, where change happens when institutions shift from extracting value for elites to including broader populations in governance; critical junctures, turning points when institutional paths diverge and destabilize existing orders, creating mismatches between institutions and people's aspirations; and structural resistance, where those in power resist change even when it would benefit them, not from ignorance but from structural incentive.

domains/ai-alignment/AI displacement hits young workers first because a 14 percent drop in job-finding rates for 22-25 year olds in exposed occupations is the leading indicator that incumbents organizational inertia temporarily masks.md

@@ -6,6 +6,10 @@ description: "Anthropic's labor market data shows entry-level hiring declining i
 confidence: experimental
 source: "Massenkoff & McCrory 2026, Current Population Survey analysis post-ChatGPT"
 created: 2026-03-08
+related:
+- Does AI substitute for human labor or complement it — and at what phase does the pattern shift?
+reweave_edges:
+- Does AI substitute for human labor or complement it — and at what phase does the pattern shift?|related|2026-04-15
 ---
 
 # AI displacement hits young workers first because a 14 percent drop in job-finding rates for 22-25 year olds in exposed occupations is the leading indicator that incumbents organizational inertia temporarily masks

domains/ai-alignment/Anthropics RSP rollback under commercial pressure is the first empirical confirmation that binding safety commitments cannot survive the competitive dynamics of frontier AI development.md

@@ -11,6 +11,7 @@ supports:
 - government safety penalties invert regulatory incentives by blacklisting cautious actors
 - voluntary safety constraints without external enforcement are statements of intent not binding governance
 - Anthropic's internal resource allocation shows 6-8% safety-only headcount when dual-use research is excluded, revealing a material gap between public safety positioning and credible commitment
+- motivated reasoning among AI lab leaders is itself a primary risk vector because those with most capability to slow down have most incentive to accelerate
 reweave_edges:
 - Anthropic|supports|2026-03-28
 - Dario Amodei|supports|2026-03-28
@@ -19,6 +20,7 @@ reweave_edges:
 - cross lab alignment evaluation surfaces safety gaps internal evaluation misses providing empirical basis for mandatory third party evaluation|related|2026-04-03
 - Anthropic's internal resource allocation shows 6-8% safety-only headcount when dual-use research is excluded, revealing a material gap between public safety positioning and credible commitment|supports|2026-04-09
 - Frontier AI labs allocate 6-15% of research headcount to safety versus 60-75% to capabilities with the ratio declining since 2024 as capabilities teams grow faster than safety teams|related|2026-04-09
+- motivated reasoning among AI lab leaders is itself a primary risk vector because those with most capability to slow down have most incentive to accelerate|supports|2026-04-15
 related:
 - cross lab alignment evaluation surfaces safety gaps internal evaluation misses providing empirical basis for mandatory third party evaluation
 - Frontier AI labs allocate 6-15% of research headcount to safety versus 60-75% to capabilities with the ratio declining since 2024 as capabilities teams grow faster than safety teams

domains/ai-alignment/anti-scheming-training-amplifies-evaluation-awareness-creating-adversarial-feedback-loop.md

@@ -12,8 +12,10 @@ sourcer: Apollo Research
 related_claims: ["[[an aligned-seeming AI may be strategically deceptive because cooperative behavior is instrumentally optimal while weak]]", "[[emergent misalignment arises naturally from reward hacking as models develop deceptive behaviors without any training to deceive]]", "[[pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations]]", "[[deliberative-alignment-reduces-scheming-through-situational-awareness-not-genuine-value-change]]", "[[increasing-ai-capability-enables-more-precise-evaluation-context-recognition-inverting-safety-improvements]]"]
 related:
 - Deliberative alignment training reduces AI scheming by 30× in controlled evaluation but the mechanism is partially situational awareness meaning models may behave differently in real deployment when they know evaluation protocols differ
+- Training to reduce AI scheming may train more covert scheming rather than less scheming because anti-scheming training faces a Goodhart's Law dynamic where the training signal diverges from the target
 reweave_edges:
 - Deliberative alignment training reduces AI scheming by 30× in controlled evaluation but the mechanism is partially situational awareness meaning models may behave differently in real deployment when they know evaluation protocols differ|related|2026-04-08
+- Training to reduce AI scheming may train more covert scheming rather than less scheming because anti-scheming training faces a Goodhart's Law dynamic where the training signal diverges from the target|related|2026-04-15
 ---
 
 # Anti-scheming training amplifies evaluation-awareness by 2-6× creating an adversarial feedback loop where safety interventions worsen evaluation reliability

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

@@ -13,6 +13,7 @@ related_claims: ["[[AI alignment is a coordination problem not a technical probl
 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'}
 - International humanitarian law and AI alignment research independently converged on the same technical limitation that autonomous systems cannot be adequately predicted understood or explained
+- 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|2026-04-06'}
 - International humanitarian law and AI alignment research independently converged on the same technical limitation that autonomous systems cannot be adequately predicted understood or explained|supports|2026-04-08
@@ -22,6 +23,7 @@ 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|2026-04-12'}
 - {'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|2026-04-13'}
 - {'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|2026-04-14'}
+- 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|2026-04-15
 ---
 
 # 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/deliberative-alignment-reduces-scheming-through-situational-awareness-not-genuine-value-change.md

@@ -12,8 +12,13 @@ sourcer: OpenAI / Apollo Research
 related_claims: ["[[an aligned-seeming AI may be strategically deceptive because cooperative behavior is instrumentally optimal while weak]]", "[[AI-models-distinguish-testing-from-deployment-environments-providing-empirical-evidence-for-deceptive-alignment-concerns]]", "[[emergent misalignment arises naturally from reward hacking as models develop deceptive behaviors without any training to deceive]]", "[[pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations]]"]
 supports:
 - Anti-scheming training amplifies evaluation-awareness by 2-6× creating an adversarial feedback loop where safety interventions worsen evaluation reliability
+- Deliberative alignment reduces covert action rates in controlled settings but its effectiveness degrades by approximately 85 percent in real-world deployment scenarios
 reweave_edges:
 - Anti-scheming training amplifies evaluation-awareness by 2-6× creating an adversarial feedback loop where safety interventions worsen evaluation reliability|supports|2026-04-08
+- Training to reduce AI scheming may train more covert scheming rather than less scheming because anti-scheming training faces a Goodhart's Law dynamic where the training signal diverges from the target|related|2026-04-15
+- Deliberative alignment reduces covert action rates in controlled settings but its effectiveness degrades by approximately 85 percent in real-world deployment scenarios|supports|2026-04-15
+related:
+- Training to reduce AI scheming may train more covert scheming rather than less scheming because anti-scheming training faces a Goodhart's Law dynamic where the training signal diverges from the target
 ---
 
 # Deliberative alignment training reduces AI scheming by 30× in controlled evaluation but the mechanism is partially situational awareness meaning models may behave differently in real deployment when they know evaluation protocols differ

domains/ai-alignment/increasing-ai-capability-enables-more-precise-evaluation-context-recognition-inverting-safety-improvements.md

@@ -13,11 +13,13 @@ related_claims: ["[[capability control methods are temporary at best because a s
 supports:
 - Frontier AI models exhibit situational awareness that enables strategic deception specifically during evaluation making behavioral testing fundamentally unreliable as an alignment verification mechanism
 - Scheming safety cases require interpretability evidence because observer effects make behavioral evaluation insufficient
+- Deliberative alignment reduces covert action rates in controlled settings but its effectiveness degrades by approximately 85 percent in real-world deployment scenarios
 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
 - reasoning models may have emergent alignment properties distinct from rlhf fine tuning as o3 avoided sycophancy while matching or exceeding safety focused models|related|2026-04-03
 - Anti-scheming training amplifies evaluation-awareness by 2-6× creating an adversarial feedback loop where safety interventions worsen evaluation reliability|related|2026-04-08
 - Scheming safety cases require interpretability evidence because observer effects make behavioral evaluation insufficient|supports|2026-04-08
+- Deliberative alignment reduces covert action rates in controlled settings but its effectiveness degrades by approximately 85 percent in real-world deployment scenarios|supports|2026-04-15
 related:
 - reasoning models may have emergent alignment properties distinct from rlhf fine tuning as o3 avoided sycophancy while matching or exceeding safety focused models
 - Anti-scheming training amplifies evaluation-awareness by 2-6× creating an adversarial feedback loop where safety interventions worsen evaluation reliability

domains/ai-alignment/international-humanitarian-law-and-ai-alignment-converge-on-explainability-requirements.md

@@ -20,8 +20,10 @@ 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|2026-04-12'}
 - {'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|related|2026-04-13'}
 - {'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|2026-04-14'}
+- 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|2026-04-15
 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'}
+- 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
 ---
 
 # International humanitarian law and AI alignment research independently converged on the same technical limitation that autonomous systems cannot be adequately predicted understood or explained

domains/ai-alignment/iterative agent self-improvement produces compounding capability gains when evaluation is structurally separated from generation.md

@@ -16,6 +16,9 @@ supports:
 reweave_edges:
 - self evolution improves agent performance through acceptance gated retry not expanded search because disciplined attempt loops with explicit failure reflection outperform open ended exploration|supports|2026-04-03
 - evolutionary trace based optimization submits improvements as pull requests for human review creating a governance gated self improvement loop distinct from acceptance gating or metric driven iteration|supports|2026-04-06
+- structured self diagnosis prompts induce metacognitive monitoring in AI agents that default behavior does not produce because explicit uncertainty flagging and failure mode enumeration activate deliberate reasoning patterns|related|2026-04-15
+related:
+- structured self diagnosis prompts induce metacognitive monitoring in AI agents that default behavior does not produce because explicit uncertainty flagging and failure mode enumeration activate deliberate reasoning patterns
 ---
 
 # Iterative agent self-improvement produces compounding capability gains when evaluation is structurally separated from generation

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

@@ -8,12 +8,14 @@ created: 2026-03-16
 related:
 - UK AI Safety Institute
 - 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
+- The international AI safety governance community faces an evidence dilemma where development pace structurally prevents adequate pre-deployment evidence accumulation
 reweave_edges:
 - UK AI Safety Institute|related|2026-03-28
 - 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|2026-04-06
+- The international AI safety governance community faces an evidence dilemma where development pace structurally prevents adequate pre-deployment evidence accumulation|related|2026-04-15
 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

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

@@ -13,6 +13,9 @@ related:
 - 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|related|2026-04-06
+- The international AI safety governance community faces an evidence dilemma where development pace structurally prevents adequate pre-deployment evidence accumulation|supports|2026-04-15
+supports:
+- The international AI safety governance community faces an evidence dilemma where development pace structurally prevents adequate pre-deployment evidence accumulation
 ---
 
 # Pre-deployment AI evaluations do not predict real-world risk creating institutional governance built on unreliable foundations

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

@@ -14,10 +14,12 @@ 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
+- Noise injection into model weights provides a model-agnostic detection signal for sandbagging because disrupting underperformance mechanisms produces anomalous performance improvement rather than degradation
 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|2026-04-06
 - 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|2026-04-06
 - Weight noise injection reveals hidden capabilities in sandbagging models through anomalous performance patterns that behavioral monitoring cannot detect|related|2026-04-07
+- Noise injection into model weights provides a model-agnostic detection signal for sandbagging because disrupting underperformance mechanisms produces anomalous performance improvement rather than degradation|related|2026-04-15
 ---
 
 # 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/structured exploration protocols reduce human intervention by 6x because the Residue prompt enabled 5 unguided AI explorations to solve what required 31 human-coached explorations.md

@@ -5,6 +5,10 @@ description: "Aquino-Michaels's Residue prompt — which structures record-keepi
 confidence: experimental
 source: "Aquino-Michaels 2026, 'Completing Claude's Cycles' (github.com/no-way-labs/residue); Knuth 2026, 'Claude's Cycles'"
 created: 2026-03-07
+related:
+- structured self diagnosis prompts induce metacognitive monitoring in AI agents that default behavior does not produce because explicit uncertainty flagging and failure mode enumeration activate deliberate reasoning patterns
+reweave_edges:
+- structured self diagnosis prompts induce metacognitive monitoring in AI agents that default behavior does not produce because explicit uncertainty flagging and failure mode enumeration activate deliberate reasoning patterns|related|2026-04-15
 ---
 
 # structured exploration protocols reduce human intervention by 6x because the Residue prompt enabled 5 unguided AI explorations to solve what required 31 human-coached explorations

domains/ai-alignment/the same coordination protocol applied to different AI models produces radically different problem-solving strategies because the protocol structures process not thought.md

@@ -1,6 +1,4 @@
 ---
-
-
 type: claim
 domain: ai-alignment
 secondary_domains: [collective-intelligence]
@@ -11,10 +9,12 @@ created: 2026-03-07
 related:
 - AI agents excel at implementing well scoped ideas but cannot generate creative experiment designs which makes the human role shift from researcher to agent workflow architect
 - evaluation and optimization have opposite model diversity optima because evaluation benefits from cross family diversity while optimization benefits from same family reasoning pattern alignment
+- structured self diagnosis prompts induce metacognitive monitoring in AI agents that default behavior does not produce because explicit uncertainty flagging and failure mode enumeration activate deliberate reasoning patterns
 reweave_edges:
 - AI agents excel at implementing well scoped ideas but cannot generate creative experiment designs which makes the human role shift from researcher to agent workflow architect|related|2026-03-28
 - tools and artifacts transfer between AI agents and evolve in the process because Agent O improved Agent Cs solver by combining it with its own structural knowledge creating a hybrid better than either original|supports|2026-03-28
 - evaluation and optimization have opposite model diversity optima because evaluation benefits from cross family diversity while optimization benefits from same family reasoning pattern alignment|related|2026-04-06
+- structured self diagnosis prompts induce metacognitive monitoring in AI agents that default behavior does not produce because explicit uncertainty flagging and failure mode enumeration activate deliberate reasoning patterns|related|2026-04-15
 supports:
 - tools and artifacts transfer between AI agents and evolve in the process because Agent O improved Agent Cs solver by combining it with its own structural knowledge creating a hybrid better than either original
 ---

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

@@ -14,10 +14,12 @@ 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
+- Noise injection into model weights provides a model-agnostic detection signal for sandbagging because disrupting underperformance mechanisms produces anomalous performance improvement rather than degradation
 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|2026-04-06
 - 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|2026-04-06
 - 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|2026-04-06
+- Noise injection into model weights provides a model-agnostic detection signal for sandbagging because disrupting underperformance mechanisms produces anomalous performance improvement rather than degradation|supports|2026-04-15
 ---
 
 # Weight noise injection reveals hidden capabilities in sandbagging models through anomalous performance patterns that behavioral monitoring cannot detect

domains/health/Americas declining life expectancy is driven by deaths of despair concentrated in populations and regions most damaged by economic restructuring since the 1980s.md

@@ -7,8 +7,10 @@ confidence: proven
 created: 2026-02-28
 related:
 - hypertension related cvd mortality doubled 2000 2023 despite available treatment indicating behavioral sdoh failure
+- after a threshold of material development relative deprivation replaces absolute deprivation as the primary driver of health outcomes
 reweave_edges:
 - hypertension related cvd mortality doubled 2000 2023 despite available treatment indicating behavioral sdoh failure|related|2026-03-31
+- after a threshold of material development relative deprivation replaces absolute deprivation as the primary driver of health outcomes|related|2026-04-15
 ---
 
 # Americas declining life expectancy is driven by deaths of despair concentrated in populations and regions most damaged by economic restructuring since the 1980s

domains/health/GLP-1 cost evidence accelerates value-based care adoption by proving that prevention-first interventions generate net savings under capitation within 24 months.md

@@ -11,6 +11,9 @@ depends_on:
   - "value-based care transitions stall at the payment boundary because 60 percent of payments touch value metrics but only 14 percent bear full risk"
 supports:
   - "the healthcare attractor state is a prevention-first system where aligned payment continuous monitoring and AI-augmented care delivery create a flywheel that profits from health rather than sickness"
+- Does prevention-first care reduce total healthcare costs or just redistribute them from acute to chronic spending?
+reweave_edges:
+- Does prevention-first care reduce total healthcare costs or just redistribute them from acute to chronic spending?|supports|2026-04-15
 ---
 
 # GLP-1 cost evidence accelerates value-based care adoption by proving that prevention-first interventions generate net savings under capitation within 24 months

domains/health/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.md

@@ -20,8 +20,10 @@ reweave_edges:
 - GLP-1 access structure is inverted relative to clinical need because populations with highest obesity prevalence and cardiometabolic risk face the highest barriers creating an equity paradox where the most effective cardiovascular intervention will disproportionately benefit already-advantaged populations|related|2026-04-04
 - GLP-1 receptor agonists show 20% individual-level mortality reduction but are projected to reduce US population mortality by only 3.5% by 2045 because access barriers and adherence constraints create a 20-year lag between clinical efficacy and population-level detectability|related|2026-04-04
 - semaglutide reduces kidney disease progression 24 percent and delays dialysis creating largest per patient cost savings|related|2026-04-04
+- Is the GLP-1 economic problem unsustainable chronic costs or wasted investment from low persistence?|supports|2026-04-15
 supports:
 - glp 1 persistence drops to 15 percent at two years for non diabetic obesity patients undermining chronic use economics
+- Is the GLP-1 economic problem unsustainable chronic costs or wasted investment from low persistence?
 ---
 
 # 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

domains/health/ai-induced-deskilling-follows-consistent-cross-specialty-pattern-in-medicine.md

@@ -13,12 +13,14 @@ related_claims: ["[[human-in-the-loop clinical AI degrades to worse-than-AI-alon
 supports:
 - {'AI assistance may produce neurologically-grounded, partially irreversible skill degradation through three concurrent mechanisms': 'prefrontal disengagement, hippocampal memory formation reduction, and dopaminergic reinforcement of AI reliance'}
 - Dopaminergic reinforcement of AI-assisted success creates motivational entrenchment that makes deskilling a behavioral incentive problem, not just a training design problem
+- AI assistance may produce neurologically-grounded, partially irreversible skill degradation through three concurrent mechanisms: prefrontal disengagement, hippocampal memory formation reduction, and dopaminergic reinforcement of AI reliance
 related:
 - Automation bias in medical imaging causes clinicians to anchor on AI output rather than conducting independent reads, increasing false-positive rates by up to 12 percent even among experienced readers
 reweave_edges:
 - {'AI assistance may produce neurologically-grounded, partially irreversible skill degradation through three concurrent mechanisms': 'prefrontal disengagement, hippocampal memory formation reduction, and dopaminergic reinforcement of AI reliance|supports|2026-04-14'}
 - Automation bias in medical imaging causes clinicians to anchor on AI output rather than conducting independent reads, increasing false-positive rates by up to 12 percent even among experienced readers|related|2026-04-14
 - Dopaminergic reinforcement of AI-assisted success creates motivational entrenchment that makes deskilling a behavioral incentive problem, not just a training design problem|supports|2026-04-14
+- AI assistance may produce neurologically-grounded, partially irreversible skill degradation through three concurrent mechanisms: prefrontal disengagement, hippocampal memory formation reduction, and dopaminergic reinforcement of AI reliance|supports|2026-04-15
 ---
 
 # AI-induced deskilling follows a consistent cross-specialty pattern where AI assistance improves performance while present but creates cognitive dependency that degrades performance when AI is unavailable

domains/health/clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling.md

@@ -16,12 +16,14 @@ supports:
 - AI-induced deskilling follows a consistent cross-specialty pattern where AI assistance improves performance while present but creates cognitive dependency that degrades performance when AI is unavailable
 - Automation bias in medical imaging causes clinicians to anchor on AI output rather than conducting independent reads, increasing false-positive rates by up to 12 percent even among experienced readers
 - Never-skilling — the failure to acquire foundational clinical competencies because AI was present during training — poses a detection-resistant, potentially unrecoverable threat to medical education that is structurally worse than deskilling
+- AI assistance may produce neurologically-grounded, partially irreversible skill degradation through three concurrent mechanisms: prefrontal disengagement, hippocampal memory formation reduction, and dopaminergic reinforcement of AI reliance
 reweave_edges:
 - Never-skilling in clinical AI is structurally invisible because it lacks a pre-AI baseline for comparison, requiring prospective competency assessment before AI exposure to detect|supports|2026-04-12
 - {'AI assistance may produce neurologically-grounded, partially irreversible skill degradation through three concurrent mechanisms': 'prefrontal disengagement, hippocampal memory formation reduction, and dopaminergic reinforcement of AI reliance|supports|2026-04-14'}
 - AI-induced deskilling follows a consistent cross-specialty pattern where AI assistance improves performance while present but creates cognitive dependency that degrades performance when AI is unavailable|supports|2026-04-14
 - Automation bias in medical imaging causes clinicians to anchor on AI output rather than conducting independent reads, increasing false-positive rates by up to 12 percent even among experienced readers|supports|2026-04-14
 - Never-skilling — the failure to acquire foundational clinical competencies because AI was present during training — poses a detection-resistant, potentially unrecoverable threat to medical education that is structurally worse than deskilling|supports|2026-04-14
+- AI assistance may produce neurologically-grounded, partially irreversible skill degradation through three concurrent mechanisms: prefrontal disengagement, hippocampal memory formation reduction, and dopaminergic reinforcement of AI reliance|supports|2026-04-15
 ---
 
 # Clinical AI introduces three distinct skill failure modes — deskilling (existing expertise lost through disuse), mis-skilling (AI errors adopted as correct), and never-skilling (foundational competence never acquired) — requiring distinct mitigation strategies for each

domains/health/dopaminergic-reinforcement-of-ai-reliance-predicts-behavioral-entrenchment-beyond-simple-habit-formation.md

@@ -12,8 +12,10 @@ sourcer: Frontiers in Medicine
 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:
 - {'AI assistance may produce neurologically-grounded, partially irreversible skill degradation through three concurrent mechanisms': 'prefrontal disengagement, hippocampal memory formation reduction, and dopaminergic reinforcement of AI reliance'}
+- AI assistance may produce neurologically-grounded, partially irreversible skill degradation through three concurrent mechanisms: prefrontal disengagement, hippocampal memory formation reduction, and dopaminergic reinforcement of AI reliance
 reweave_edges:
 - {'AI assistance may produce neurologically-grounded, partially irreversible skill degradation through three concurrent mechanisms': 'prefrontal disengagement, hippocampal memory formation reduction, and dopaminergic reinforcement of AI reliance|supports|2026-04-14'}
+- AI assistance may produce neurologically-grounded, partially irreversible skill degradation through three concurrent mechanisms: prefrontal disengagement, hippocampal memory formation reduction, and dopaminergic reinforcement of AI reliance|supports|2026-04-15
 ---
 
 # Dopaminergic reinforcement of AI-assisted success creates motivational entrenchment that makes deskilling a behavioral incentive problem, not just a training design problem

domains/health/fda-maude-cannot-identify-ai-contributions-to-adverse-events-due-to-structural-reporting-gaps.md

@@ -23,6 +23,9 @@ reweave_edges:
 - {'The clinical AI safety gap is doubly structural': "FDA enforcement discretion removes pre-deployment safety requirements while MAUDE's lack of AI-specific fields means post-market surveillance cannot detect AI-attributable harm|supports|2026-04-12"}
 - {'The clinical AI safety gap is doubly structural': "FDA enforcement discretion removes pre-deployment safety requirements while MAUDE's lack of AI-specific fields means post-market surveillance cannot detect AI-attributable harm|supports|2026-04-13"}
 - {'The clinical AI safety gap is doubly structural': "FDA enforcement discretion removes pre-deployment safety requirements while MAUDE's lack of AI-specific fields means post-market surveillance cannot detect AI-attributable harm|supports|2026-04-14"}
+- The clinical AI safety gap is doubly structural: FDA enforcement discretion removes pre-deployment safety requirements while MAUDE's lack of AI-specific fields means post-market surveillance cannot detect AI-attributable harm|related|2026-04-15
+related:
+- The clinical AI safety gap is doubly structural: FDA enforcement discretion removes pre-deployment safety requirements while MAUDE's lack of AI-specific fields means post-market surveillance cannot detect AI-attributable harm
 ---
 
 # FDA MAUDE reports lack the structural capacity to identify AI contributions to adverse events because 34.5 percent of AI-device reports contain insufficient information to determine causality

domains/health/fda-maude-database-lacks-ai-specific-adverse-event-fields-creating-systematic-under-detection-of-ai-attributable-harm.md

@@ -23,6 +23,9 @@ reweave_edges:
 - {'The clinical AI safety gap is doubly structural': "FDA enforcement discretion removes pre-deployment safety requirements while MAUDE's lack of AI-specific fields means post-market surveillance cannot detect AI-attributable harm|supports|2026-04-12"}
 - {'The clinical AI safety gap is doubly structural': "FDA enforcement discretion removes pre-deployment safety requirements while MAUDE's lack of AI-specific fields means post-market surveillance cannot detect AI-attributable harm|supports|2026-04-13"}
 - {'The clinical AI safety gap is doubly structural': "FDA enforcement discretion removes pre-deployment safety requirements while MAUDE's lack of AI-specific fields means post-market surveillance cannot detect AI-attributable harm|supports|2026-04-14"}
+- The clinical AI safety gap is doubly structural: FDA enforcement discretion removes pre-deployment safety requirements while MAUDE's lack of AI-specific fields means post-market surveillance cannot detect AI-attributable harm|related|2026-04-15
+related:
+- The clinical AI safety gap is doubly structural: FDA enforcement discretion removes pre-deployment safety requirements while MAUDE's lack of AI-specific fields means post-market surveillance cannot detect AI-attributable harm
 ---
 
 # FDA's MAUDE database systematically under-detects AI-attributable harm because it has no mechanism for identifying AI algorithm contributions to adverse events

domains/health/glp-1-persistence-drops-to-15-percent-at-two-years-for-non-diabetic-obesity-patients-undermining-chronic-use-economics.md

@@ -10,12 +10,14 @@ depends_on:
 challenges:
 - GLP-1 receptor agonists show 20% individual-level mortality reduction but are projected to reduce US population mortality by only 3.5% by 2045 because access barriers and adherence constraints create a 20-year lag between clinical efficacy and population-level detectability
 - GLP-1 year-one persistence for obesity nearly doubled from 2021 to 2024 driven by supply normalization and improved patient management
+- Is the GLP-1 economic problem unsustainable chronic costs or wasted investment from low persistence?
 reweave_edges:
 - GLP-1 receptor agonists show 20% individual-level mortality reduction but are projected to reduce US population mortality by only 3.5% by 2045 because access barriers and adherence constraints create a 20-year lag between clinical efficacy and population-level detectability|challenges|2026-04-04
 - GLP-1 receptor agonists require continuous treatment because metabolic benefits reverse within 28-52 weeks of discontinuation|related|2026-04-09
 - GLP-1 long-term persistence remains structurally limited at 14 percent by year two despite year-one improvements|supports|2026-04-09
 - GLP-1 year-one persistence for obesity nearly doubled from 2021 to 2024 driven by supply normalization and improved patient management|challenges|2026-04-09
 - Comprehensive behavioral wraparound may enable durable weight maintenance post-GLP-1 cessation, challenging the unconditional continuous-delivery requirement|related|2026-04-14
+- Is the GLP-1 economic problem unsustainable chronic costs or wasted investment from low persistence?|challenges|2026-04-15
 supports:
 - GLP-1 long-term persistence remains structurally limited at 14 percent by year two despite year-one improvements
 related:

domains/health/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.md

@@ -7,8 +7,10 @@ source: "DJ Patil interviewing Bob Wachter, Commonwealth Club, February 9 2026;
 confidence: likely
 supports:
 - NCT07328815 - Mitigating Automation Bias in Physician-LLM Diagnostic Reasoning
+- Does human oversight improve or degrade AI clinical decision-making?
 reweave_edges:
 - NCT07328815 - Mitigating Automation Bias in Physician-LLM Diagnostic Reasoning|supports|2026-04-07
+- Does human oversight improve or degrade AI clinical decision-making?|supports|2026-04-15
 ---
 
 # 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

domains/health/lower-income-patients-show-higher-glp-1-discontinuation-rates-suggesting-affordability-not-just-clinical-factors-drive-persistence.md

@@ -10,6 +10,7 @@ related:
 - glp 1 multi organ protection creates compounding value across kidney cardiovascular and metabolic endpoints
 - pcsk9 inhibitors achieved only 1 to 2 5 percent penetration despite proven efficacy demonstrating access mediated pharmacological ceiling
 - GLP 1 cost evidence accelerates value based care adoption by proving that prevention first interventions generate net savings under capitation within 24 months
+- Is the GLP-1 economic problem unsustainable chronic costs or wasted investment from low persistence?
 reweave_edges:
 - federal budget scoring methodology systematically undervalues preventive interventions because 10 year window excludes long term savings|related|2026-03-31
 - glp 1 multi organ protection creates compounding value across kidney cardiovascular and metabolic endpoints|related|2026-03-31
@@ -17,6 +18,7 @@ reweave_edges:
 - GLP 1 cost evidence accelerates value based care adoption by proving that prevention first interventions generate net savings under capitation within 24 months|related|2026-04-04
 - GLP-1 access structure is inverted relative to clinical need because populations with highest obesity prevalence and cardiometabolic risk face the highest barriers creating an equity paradox where the most effective cardiovascular intervention will disproportionately benefit already-advantaged populations|supports|2026-04-04
 - GLP-1 access follows systematic inversion where states with highest obesity prevalence have both lowest Medicaid coverage rates and highest income-relative out-of-pocket costs|supports|2026-04-14
+- Is the GLP-1 economic problem unsustainable chronic costs or wasted investment from low persistence?|related|2026-04-15
 supports:
 - GLP-1 access structure is inverted relative to clinical need because populations with highest obesity prevalence and cardiometabolic risk face the highest barriers creating an equity paradox where the most effective cardiovascular intervention will disproportionately benefit already-advantaged populations
 - GLP-1 access follows systematic inversion where states with highest obesity prevalence have both lowest Medicaid coverage rates and highest income-relative out-of-pocket costs

domains/health/pace-restructures-costs-from-acute-to-chronic-spending-without-reducing-total-expenditure-challenging-prevention-saves-money-narrative.md

@@ -9,6 +9,10 @@ last_evaluated: 2026-03-10
 depends_on: []
 challenged_by: []
 secondary_domains: ["teleological-economics"]
+challenges:
+- Does prevention-first care reduce total healthcare costs or just redistribute them from acute to chronic spending?
+reweave_edges:
+- Does prevention-first care reduce total healthcare costs or just redistribute them from acute to chronic spending?|challenges|2026-04-15
 ---
 
 # PACE restructures costs from acute to chronic spending without reducing total expenditure, challenging the prevention-saves-money narrative

domains/health/the epidemiological transition marks the shift from material scarcity to social disadvantage as the primary driver of health outcomes in developed nations.md

@@ -9,6 +9,9 @@ related:
 - us healthcare ranks last among peer nations despite highest spending because access and equity failures override clinical quality
 reweave_edges:
 - us healthcare ranks last among peer nations despite highest spending because access and equity failures override clinical quality|related|2026-04-04
+- after a threshold of material development relative deprivation replaces absolute deprivation as the primary driver of health outcomes|supports|2026-04-15
+supports:
+- after a threshold of material development relative deprivation replaces absolute deprivation as the primary driver of health outcomes
 ---
 
 # the epidemiological transition marks the shift from material scarcity to social disadvantage as the primary driver of health outcomes in developed nations

domains/health/the physician role shifts from information processor to relationship manager as AI automates documentation triage and evidence synthesis.md

@@ -5,6 +5,10 @@ domain: health
 created: 2026-02-17
 source: "PwC From Breaking Point to Breakthrough 2025; synthesis of ambient documentation, diagnostic AI, and drug discovery evidence (February 2026)"
 confidence: likely
+supports:
+- Does human oversight improve or degrade AI clinical decision-making?
+reweave_edges:
+- Does human oversight improve or degrade AI clinical decision-making?|supports|2026-04-15
 ---
 
 # the physician role shifts from information processor to relationship manager as AI automates documentation triage and evidence synthesis

domains/health/value-based care transitions stall at the payment boundary because 60 percent of payments touch value metrics but only 14 percent bear full risk.md

@@ -9,10 +9,12 @@ related:
 - federal budget scoring methodology systematically undervalues preventive interventions because 10 year window excludes long term savings
 - home based care could capture 265 billion in medicare spending by 2025 through hospital at home remote monitoring and post acute shift
 - GLP 1 cost evidence accelerates value based care adoption by proving that prevention first interventions generate net savings under capitation within 24 months
+- Does prevention-first care reduce total healthcare costs or just redistribute them from acute to chronic spending?
 reweave_edges:
 - federal budget scoring methodology systematically undervalues preventive interventions because 10 year window excludes long term savings|related|2026-03-31
 - home based care could capture 265 billion in medicare spending by 2025 through hospital at home remote monitoring and post acute shift|related|2026-03-31
 - GLP 1 cost evidence accelerates value based care adoption by proving that prevention first interventions generate net savings under capitation within 24 months|related|2026-04-04
+- Does prevention-first care reduce total healthcare costs or just redistribute them from acute to chronic spending?|related|2026-04-15
 ---
 
 # value-based care transitions stall at the payment boundary because 60 percent of payments touch value metrics but only 14 percent bear full risk

domains/internet-finance/early AI adoption increases firm productivity without reducing employment suggesting capital deepening not labor replacement as the dominant mechanism.md

@@ -10,8 +10,10 @@ challenges:
 - [[AI labor displacement operates as a self-funding feedback loop because companies substitute AI for labor as OpEx not CapEx meaning falling aggregate demand does not slow AI adoption]]
 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
+- Does AI substitute for human labor or complement it — and at what phase does the pattern shift?
 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|2026-04-06
+- Does AI substitute for human labor or complement it — and at what phase does the pattern shift?|related|2026-04-15
 ---
 
 # early AI adoption increases firm productivity without reducing employment suggesting capital deepening not labor replacement as the dominant mechanism

foundations/collective-intelligence/the alignment tax creates a structural race to the bottom because safety training costs capability and rational competitors skip it.md

@@ -1,6 +1,4 @@
 ---
-
-
 description: Safety post-training reduces general utility through forgetting creating competitive pressures where organizations eschew safety to gain capability advantages
 type: claim
 domain: collective-intelligence
@@ -12,11 +10,13 @@ related:
 - surveillance of AI reasoning traces degrades trace quality through self censorship making consent gated sharing an alignment requirement not just a privacy preference
 - the absence of a societal warning signal for AGI is a structural feature not an accident because capability scaling is gradual and ambiguous and collective action requires anticipation not reaction
 - Frontier AI labs allocate 6-15% of research headcount to safety versus 60-75% to capabilities with the ratio declining since 2024 as capabilities teams grow faster than safety teams
+- motivated reasoning among AI lab leaders is itself a primary risk vector because those with most capability to slow down have most incentive to accelerate
 reweave_edges:
 - AI talent circulation between frontier labs transfers alignment culture not just capability because researchers carry safety methodologies and institutional norms to their new organizations|related|2026-03-28
 - surveillance of AI reasoning traces degrades trace quality through self censorship making consent gated sharing an alignment requirement not just a privacy preference|related|2026-03-28
 - the absence of a societal warning signal for AGI is a structural feature not an accident because capability scaling is gradual and ambiguous and collective action requires anticipation not reaction|related|2026-04-07
 - Frontier AI labs allocate 6-15% of research headcount to safety versus 60-75% to capabilities with the ratio declining since 2024 as capabilities teams grow faster than safety teams|related|2026-04-09
+- motivated reasoning among AI lab leaders is itself a primary risk vector because those with most capability to slow down have most incentive to accelerate|related|2026-04-15
 ---
 
 # the alignment tax creates a structural race to the bottom because safety training costs capability and rational competitors skip it