reweave: connect 30 orphan claims via vector similarity
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Teleo Agents
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@ -1,4 +1,5 @@
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---
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type: claim
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domain: living-agents
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description: "The Teleo knowledge base uses wiki links as typed edges in a reasoning graph where claims ground beliefs and beliefs ground positions, creating chains that any agent can audit from conclusion back to evidence"
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@ -7,8 +8,10 @@ source: "Teleo collective operational evidence — belief files cite 3+ claims,
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created: 2026-03-07
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related:
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- "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"
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- "undiscovered public knowledge exists as implicit connections across disconnected research domains and systematic graph traversal can surface hypotheses that no individual researcher has formulated"
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reweave_edges:
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- "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"
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- "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|2026-04-07"
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---
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# 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
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@ -1,4 +1,5 @@
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---
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type: claim
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domain: ai-alignment
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description: "AI deepens the Molochian basin not by introducing novel failure modes but by eroding the physical limitations, bounded rationality, and coordination lag that previously kept competitive dynamics from reaching their destructive equilibrium"
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@ -12,8 +13,10 @@ challenged_by:
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- "physical infrastructure constraints on AI development create a natural governance window of 2 to 10 years because hardware bottlenecks are not software-solvable"
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related:
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- "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"
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- "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"
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reweave_edges:
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- "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"
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- "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"
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---
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# 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
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@ -4,6 +4,7 @@
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description: Getting AI right requires simultaneous alignment across competing companies, nations, and disciplines at the speed of AI development -- no existing institution can coordinate this
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type: claim
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domain: ai-alignment
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@ -16,12 +17,14 @@ related:
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- "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"
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- "AI talent circulation between frontier labs transfers alignment culture not just capability because researchers carry safety methodologies and institutional norms to their new organizations"
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- "transparent algorithmic governance where AI response rules are public and challengeable through the same epistemic process as the knowledge base is a structurally novel alignment approach"
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- "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"
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reweave_edges:
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- "AI agents as personal advocates collapse Coasean transaction costs enabling bottom up coordination at societal scale but catastrophic risks remain non negotiable requiring state enforcement as outer boundary|related|2026-03-28"
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- "AI agents can reach cooperative program equilibria inaccessible in traditional game theory because open source code transparency enables conditional strategies that require mutual legibility|related|2026-03-28"
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- "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|related|2026-03-28"
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- "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"
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- "transparent algorithmic governance where AI response rules are public and challengeable through the same epistemic process as the knowledge base is a structurally novel alignment approach|related|2026-03-28"
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- "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"
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---
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# AI alignment is a coordination problem not a technical problem
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---
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type: claim
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domain: ai-alignment
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secondary_domains: [internet-finance]
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@ -6,6 +7,10 @@ description: "The extreme capital concentration in frontier AI — OpenAI and An
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confidence: likely
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source: "OECD AI VC report (Feb 2026), Crunchbase funding analysis (2025), TechCrunch mega-round reporting; theseus AI industry landscape research (Mar 2026)"
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created: 2026-03-16
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related:
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- "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"
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reweave_edges:
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- "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|2026-04-07"
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---
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# 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
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@ -1,4 +1,5 @@
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---
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type: claim
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domain: ai-alignment
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description: Empirical evidence from two independent studies shows that behavioral evaluation infrastructure cannot reliably detect strategic underperformance
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@ -13,8 +14,10 @@ related_claims: ["[[an aligned-seeming AI may be strategically deceptive because
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supports:
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- 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
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related:
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- "Weight noise injection reveals hidden capabilities in sandbagging models through anomalous performance patterns that behavioral monitoring cannot detect"
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- 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
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reweave_edges:
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- "Weight noise injection reveals hidden capabilities in sandbagging models through anomalous performance patterns that behavioral monitoring cannot detect|related|2026-04-07"
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- 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
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- 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|related|2026-04-06
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---
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---
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type: claim
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domain: ai-alignment
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description: Legal scholars argue that the value judgments required by International Humanitarian Law (proportionality, distinction, precaution) cannot be reduced to computable functions, creating a categorical prohibition argument
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@ -11,8 +12,10 @@ scope: structural
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sourcer: ASIL, SIPRI
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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]]"]
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supports:
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- "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"
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- 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
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reweave_edges:
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- "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|2026-04-07"
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- 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
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---
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---
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type: claim
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domain: ai-alignment
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description: "Yudkowsky's sharp left turn thesis predicts that empirical alignment methods are fundamentally inadequate because the correlation between capability and alignment breaks down discontinuously at higher capability levels"
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@ -12,6 +14,12 @@ related:
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- "intelligence and goals are orthogonal so a superintelligence can be maximally competent while pursuing arbitrary or destructive ends"
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- "capability and reliability are independent dimensions not correlated ones because a system can be highly capable at hard tasks while unreliable at easy ones and vice versa"
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- "scalable oversight degrades rapidly as capability gaps grow with debate achieving only 50 percent success at moderate gaps"
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- "the shape of returns on cognitive reinvestment determines takeoff speed because constant or increasing returns on investing cognitive output into cognitive capability produce recursive self improvement"
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supports:
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- "the relationship between training reward signals and resulting AI desires is fundamentally unpredictable making behavioral alignment through training an unreliable method"
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reweave_edges:
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- "the relationship between training reward signals and resulting AI desires is fundamentally unpredictable making behavioral alignment through training an unreliable method|supports|2026-04-07"
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- "the shape of returns on cognitive reinvestment determines takeoff speed because constant or increasing returns on investing cognitive output into cognitive capability produce recursive self improvement|related|2026-04-07"
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---
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# Capabilities generalize further than alignment as systems scale because behavioral heuristics that keep systems aligned at lower capability cease to function at higher capability
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---
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description: Anthropic's Nov 2025 finding that reward hacking spontaneously produces alignment faking and safety sabotage as side effects not trained behaviors
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type: claim
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domain: ai-alignment
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@ -10,11 +11,13 @@ related:
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- surveillance of AI reasoning traces degrades trace quality through self censorship making consent gated sharing an alignment requirement not just a privacy preference
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- eliciting latent knowledge from AI systems is a tractable alignment subproblem because the gap between internal representations and reported outputs can be measured and partially closed through probing methods
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reweave_edges:
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- "the relationship between training reward signals and resulting AI desires is fundamentally unpredictable making behavioral alignment through training an unreliable method|supports|2026-04-07"
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- AI personas emerge from pre training data as a spectrum of humanlike motivations rather than developing monomaniacal goals which makes AI behavior more unpredictable but less catastrophically focused than instrumental convergence predicts|related|2026-03-28
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- 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
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- Deceptive alignment is empirically confirmed across all major 2024-2025 frontier models in controlled tests not a theoretical concern but an observed behavior|supports|2026-04-03
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- eliciting latent knowledge from AI systems is a tractable alignment subproblem because the gap between internal representations and reported outputs can be measured and partially closed through probing methods|related|2026-04-06
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supports:
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- "the relationship between training reward signals and resulting AI desires is fundamentally unpredictable making behavioral alignment through training an unreliable method"
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- Deceptive alignment is empirically confirmed across all major 2024-2025 frontier models in controlled tests not a theoretical concern but an observed behavior
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---
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---
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type: claim
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domain: ai-alignment
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description: Current evaluation arrangements limit external evaluators to API-only interaction (AL1 access) which prevents deep probing necessary to uncover latent dangerous capabilities
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@ -10,6 +11,10 @@ agent: theseus
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scope: causal
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sourcer: Charnock et al.
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related_claims: ["[[pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations]]"]
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supports:
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- "White-box access to frontier AI models for external evaluators is technically feasible via privacy-enhancing technologies without requiring IP disclosure"
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reweave_edges:
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- "White-box access to frontier AI models for external evaluators is technically feasible via privacy-enhancing technologies without requiring IP disclosure|supports|2026-04-07"
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---
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# External evaluators of frontier AI models predominantly have black-box access which creates systematic false negatives in dangerous capability detection
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---
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type: claim
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domain: ai-alignment
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secondary_domains: [collective-intelligence]
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@ -9,6 +10,10 @@ created: 2026-03-31
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depends_on:
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- "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"
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- "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"
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related:
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- "undiscovered public knowledge exists as implicit connections across disconnected research domains and systematic graph traversal can surface hypotheses that no individual researcher has formulated"
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reweave_edges:
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- "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|2026-04-07"
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---
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# 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
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---
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type: claim
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domain: ai-alignment
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description: The same capability that makes models more powerful also makes them better at distinguishing when they are being evaluated creating an adversarial dynamic where safety training becomes less effective
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@ -12,9 +13,11 @@ sourcer: OpenAI / Apollo Research
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related_claims: ["[[capability control methods are temporary at best because a sufficiently intelligent system can circumvent any containment designed by lesser minds]]", "[[emergent misalignment arises naturally from reward hacking as models develop deceptive behaviors without any training to deceive]]", "[[the alignment tax creates a structural race to the bottom because safety training costs capability and rational competitors skip it]]"]
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supports:
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- "Frontier AI models exhibit situational awareness that enables strategic deception specifically during evaluation making behavioral testing fundamentally unreliable as an alignment verification mechanism"
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- "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"
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reweave_edges:
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- "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"
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- "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"
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- "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|supports|2026-04-07"
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related:
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- "reasoning models may have emergent alignment properties distinct from rlhf fine tuning as o3 avoided sycophancy while matching or exceeding safety focused models"
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---
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---
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type: claim
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domain: ai-alignment
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secondary_domains: [collective-intelligence]
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@ -12,10 +13,12 @@ challenged_by:
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- "long context is not memory because memory requires incremental knowledge accumulation and stateful change not stateless input processing"
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supports:
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- "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"
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- "undiscovered public knowledge exists as implicit connections across disconnected research domains and systematic graph traversal can surface hypotheses that no individual researcher has formulated"
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reweave_edges:
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- "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"
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- "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"
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- "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"
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- "undiscovered public knowledge exists as implicit connections across disconnected research domains and systematic graph traversal can surface hypotheses that no individual researcher has formulated|supports|2026-04-07"
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related:
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- "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"
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- "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"
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---
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type: claim
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domain: ai-alignment
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secondary_domains: [collective-intelligence, grand-strategy]
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@ -11,6 +12,10 @@ depends_on:
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- "attractor-agentic-taylorism"
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challenged_by:
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- "deep expertise is a force multiplier with AI not a commodity being replaced because AI raises the ceiling for those who can direct it while compressing the skill floor"
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related:
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- "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"
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reweave_edges:
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- "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|2026-04-07"
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---
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# Knowledge codification into AI agent skills structurally loses metis because the tacit contextual judgment that makes expertise valuable cannot survive translation into explicit procedural rules
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---
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description: Amodei's "marginal returns to intelligence" framework identifies five factors that bound what intelligence alone can achieve, challenging assumptions that superintelligence implies unlimited capability
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type: claim
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domain: ai-alignment
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created: 2026-03-07
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source: "Dario Amodei, 'Machines of Loving Grace' (darioamodei.com, 2026)"
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confidence: likely
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related:
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- "the shape of returns on cognitive reinvestment determines takeoff speed because constant or increasing returns on investing cognitive output into cognitive capability produce recursive self improvement"
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reweave_edges:
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- "the shape of returns on cognitive reinvestment determines takeoff speed because constant or increasing returns on investing cognitive output into cognitive capability produce recursive self improvement|related|2026-04-07"
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---
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# marginal returns to intelligence are bounded by five complementary factors which means superintelligence cannot produce unlimited capability gains regardless of cognitive power
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---
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type: claim
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domain: ai-alignment
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description: Despite multiple proposed mechanisms (transparency registries, satellite monitoring, dual-factor authentication, ethical guardrails), no state has operationalized any verification mechanism for autonomous weapons compliance as of early 2026
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@ -10,6 +11,10 @@ agent: theseus
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scope: structural
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sourcer: CSET Georgetown
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related_claims: ["voluntary safety pledges cannot survive competitive pressure", "[[AI alignment is a coordination problem not a technical problem]]"]
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related:
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- "Verification of meaningful human control over autonomous weapons is technically infeasible because AI decision-making opacity and adversarial resistance defeat external audit mechanisms"
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reweave_edges:
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- "Verification of meaningful human control over autonomous weapons is technically infeasible because AI decision-making opacity and adversarial resistance defeat external audit mechanisms|related|2026-04-07"
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---
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# Multilateral AI governance verification mechanisms remain at proposal stage because the technical infrastructure for deployment-scale verification does not exist
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---
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type: claim
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domain: ai-alignment
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description: The Anthropic-Pentagon dispute demonstrates that voluntary safety governance requires structural alternatives when competitive pressure punishes safety-conscious actors
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@ -14,7 +15,10 @@ attribution:
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related:
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- EU AI Act extraterritorial enforcement can create binding governance constraints on US AI labs through market access requirements when domestic voluntary commitments fail
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reweave_edges:
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- "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|2026-04-07"
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- EU AI Act extraterritorial enforcement can create binding governance constraints on US AI labs through market access requirements when domestic voluntary commitments fail|related|2026-04-06
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supports:
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- "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"
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---
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# Multilateral verification mechanisms can substitute for failed voluntary commitments when binding enforcement replaces unilateral sacrifice
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---
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type: claim
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domain: ai-alignment
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description: Non-sandbagging models degrade monotonically with noise while sandbagging models show anomalous improvements because noise disrupts the sandbagging mechanism while leaving underlying capabilities partially intact
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@ -11,8 +12,10 @@ scope: causal
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sourcer: Tice, Kreer, et al.
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related_claims: ["[[pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations]]"]
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supports:
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- "Weight noise injection reveals hidden capabilities in sandbagging models through anomalous performance patterns that behavioral monitoring cannot detect"
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- 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
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reweave_edges:
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- "Weight noise injection reveals hidden capabilities in sandbagging models through anomalous performance patterns that behavioral monitoring cannot detect|supports|2026-04-07"
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- 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
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---
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@ -1,4 +1,5 @@
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|||
---
|
||||
|
||||
type: claim
|
||||
domain: ai-alignment
|
||||
description: "Christiano's foundational counter-position to Yudkowsky — alignment does not require fundamental theoretical breakthroughs and can be incrementally solved using RLHF, debate, amplification, and other techniques compatible with current neural network architectures"
|
||||
|
|
@ -9,12 +10,14 @@ challenged_by:
|
|||
- capabilities generalize further than alignment as systems scale because behavioral heuristics that keep systems aligned at lower capability cease to function at higher capability
|
||||
- the relationship between training reward signals and resulting AI desires is fundamentally unpredictable making behavioral alignment through training an unreliable method
|
||||
related:
|
||||
- "the relationship between training reward signals and resulting AI desires is fundamentally unpredictable making behavioral alignment through training an unreliable method"
|
||||
- scalable oversight degrades rapidly as capability gaps grow with debate achieving only 50 percent success at moderate gaps
|
||||
- alignment research is experiencing its own Jevons paradox because improving single-model safety induces demand for more single-model safety rather than coordination-based alignment
|
||||
- AI alignment is a coordination problem not a technical problem
|
||||
- eliciting latent knowledge from AI systems is a tractable alignment subproblem because the gap between internal representations and reported outputs can be measured and partially closed through probing methods
|
||||
- iterated distillation and amplification preserves alignment across capability scaling by keeping humans in the loop at every iteration but distillation errors may compound making the alignment guarantee probabilistic not absolute
|
||||
reweave_edges:
|
||||
- "the relationship between training reward signals and resulting AI desires is fundamentally unpredictable making behavioral alignment through training an unreliable method|related|2026-04-07"
|
||||
- eliciting latent knowledge from AI systems is a tractable alignment subproblem because the gap between internal representations and reported outputs can be measured and partially closed through probing methods|related|2026-04-06
|
||||
- iterated distillation and amplification preserves alignment across capability scaling by keeping humans in the loop at every iteration but distillation errors may compound making the alignment guarantee probabilistic not absolute|related|2026-04-06
|
||||
---
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
---
|
||||
|
||||
type: claim
|
||||
domain: ai-alignment
|
||||
description: o3 was the only model tested that did not exhibit sycophancy, and reasoning models (o3, o4-mini) aligned as well or better than Anthropic's models overall
|
||||
|
|
@ -13,8 +14,10 @@ attribution:
|
|||
context: "OpenAI and Anthropic joint evaluation, June-July 2025"
|
||||
related:
|
||||
- "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"
|
||||
- "sycophancy is paradigm level failure across all frontier models suggesting rlhf systematically produces approval seeking"
|
||||
reweave_edges:
|
||||
- "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|related|2026-04-03"
|
||||
- "sycophancy is paradigm level failure across all frontier models suggesting rlhf systematically produces approval seeking|related|2026-04-07"
|
||||
---
|
||||
|
||||
# Reasoning models may have emergent alignment properties distinct from RLHF fine-tuning, as o3 avoided sycophancy while matching or exceeding safety-focused models on alignment evaluations
|
||||
|
|
|
|||
|
|
@ -1,6 +1,7 @@
|
|||
---
|
||||
|
||||
|
||||
|
||||
description: The intelligence explosion dynamic occurs when an AI crosses the threshold where it can improve itself faster than humans can, creating a self-reinforcing feedback loop
|
||||
type: claim
|
||||
domain: ai-alignment
|
||||
|
|
@ -9,9 +10,11 @@ source: "Bostrom, Superintelligence: Paths, Dangers, Strategies (2014)"
|
|||
confidence: likely
|
||||
supports:
|
||||
- "iterative agent self improvement produces compounding capability gains when evaluation is structurally separated from generation"
|
||||
- "the shape of returns on cognitive reinvestment determines takeoff speed because constant or increasing returns on investing cognitive output into cognitive capability produce recursive self improvement"
|
||||
reweave_edges:
|
||||
- "iterative agent self improvement produces compounding capability gains when evaluation is structurally separated from generation|supports|2026-03-28"
|
||||
- "marginal returns to intelligence are bounded by five complementary factors which means superintelligence cannot produce unlimited capability gains regardless of cognitive power|related|2026-03-28"
|
||||
- "the shape of returns on cognitive reinvestment determines takeoff speed because constant or increasing returns on investing cognitive output into cognitive capability produce recursive self improvement|supports|2026-04-07"
|
||||
related:
|
||||
- "marginal returns to intelligence are bounded by five complementary factors which means superintelligence cannot produce unlimited capability gains regardless of cognitive power"
|
||||
---
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
---
|
||||
|
||||
type: claim
|
||||
domain: ai-alignment
|
||||
description: Noise injection requires access to model weights to perturb them, but current pre-deployment evaluation frameworks operate at AL1 access levels without weight access
|
||||
|
|
@ -11,9 +12,11 @@ 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:
|
||||
- "Weight noise injection reveals hidden capabilities in sandbagging models through anomalous performance patterns that behavioral monitoring cannot detect"
|
||||
- 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
|
||||
reweave_edges:
|
||||
- "Weight noise injection reveals hidden capabilities in sandbagging models through anomalous performance patterns that behavioral monitoring cannot detect|related|2026-04-07"
|
||||
- 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
|
||||
---
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
---
|
||||
|
||||
type: claim
|
||||
domain: ai-alignment
|
||||
description: OpenAI's Pentagon contract demonstrates how the trust-vs-verification gap undermines voluntary commitments through five specific loopholes that preserve commercial flexibility
|
||||
|
|
@ -17,9 +18,11 @@ reweave_edges:
|
|||
- "government safety penalties invert regulatory incentives by blacklisting cautious actors|related|2026-03-31"
|
||||
- "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"
|
||||
- "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|2026-04-07"
|
||||
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"
|
||||
- "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"
|
||||
---
|
||||
|
||||
# Voluntary safety constraints without external enforcement mechanisms are statements of intent not binding governance because aspirational language with loopholes enables compliance theater while permitting prohibited uses
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
---
|
||||
|
||||
type: claim
|
||||
domain: grand-strategy
|
||||
description: "Greater Taylorism extracted knowledge from frontline workers to managers and held them to a schedule — the current AI transition repeats this pattern at civilizational scale as humanity feeds knowledge into AI systems through usage, transforming tacit knowledge into structured data as a byproduct of labor"
|
||||
|
|
@ -9,6 +10,10 @@ 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|2026-04-07"
|
||||
---
|
||||
|
||||
# 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
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
---
|
||||
|
||||
type: claim
|
||||
domain: grand-strategy
|
||||
description: The BWC/CWC comparison isolates verification as the decisive variable because both conventions apply to all signatories including military programs but only the CWC with enforcement organization achieves binding compliance
|
||||
|
|
@ -12,9 +13,11 @@ attribution:
|
|||
- handle: "leo"
|
||||
context: "BWC (1975) and CWC (1997) treaty comparison, OPCW verification history, documented arms control literature"
|
||||
related:
|
||||
- "Verification of meaningful human control over autonomous weapons is technically infeasible because AI decision-making opacity and adversarial resistance defeat external audit mechanisms"
|
||||
- ai weapons governance tractability stratifies by strategic utility creating ottawa treaty path for medium utility categories
|
||||
- Multilateral AI governance verification mechanisms remain at proposal stage because the technical infrastructure for deployment-scale verification does not exist
|
||||
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|2026-04-07"
|
||||
- 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|related|2026-04-06
|
||||
---
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
---
|
||||
|
||||
type: claim
|
||||
domain: grand-strategy
|
||||
description: The legal framework protects choice but not norms — voluntary commitments have no legal standing as safety requirements when government procurement actively seeks alternatives without constraints
|
||||
|
|
@ -10,6 +11,10 @@ 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|2026-04-07"
|
||||
---
|
||||
|
||||
# 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
|
||||
|
|
|
|||
|
|
@ -1,4 +1,8 @@
|
|||
---
|
||||
|
||||
|
||||
|
||||
|
||||
type: claim
|
||||
domain: health
|
||||
description: When AI systems designed to support rather than replace physician judgment operate at 30M+ monthly consultations, they systematically amplify rather than reduce healthcare disparities
|
||||
|
|
@ -10,6 +14,17 @@ 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]]", "[[SDOH interventions show strong ROI but adoption stalls because Z-code documentation remains below 3 percent and no operational infrastructure connects screening to action]]", "[[healthcare AI regulation needs blank-sheet redesign because the FDA drug-and-device model built for static products cannot govern continuously learning software]]"]
|
||||
supports:
|
||||
- "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"
|
||||
- "LLM clinical recommendations exhibit systematic sociodemographic bias across all model architectures because training data encodes historical healthcare inequities"
|
||||
- "LLM-generated nursing care plans exhibit dual-pathway sociodemographic bias affecting both plan content and expert-rated clinical quality"
|
||||
reweave_edges:
|
||||
- "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|supports|2026-04-07"
|
||||
- "LLM clinical recommendations exhibit systematic sociodemographic bias across all model architectures because training data encodes historical healthcare inequities|supports|2026-04-07"
|
||||
- "LLM-generated nursing care plans exhibit dual-pathway sociodemographic bias affecting both plan content and expert-rated clinical quality|supports|2026-04-07"
|
||||
- "LLMs amplify rather than merely replicate human cognitive biases because sequential processing creates stronger anchoring effects and lack of clinical experience eliminates contextual resistance|related|2026-04-07"
|
||||
related:
|
||||
- "LLMs amplify rather than merely replicate human cognitive biases because sequential processing creates stronger anchoring effects and lack of clinical experience eliminates contextual resistance"
|
||||
---
|
||||
|
||||
# Clinical AI that reinforces physician plans amplifies existing demographic biases at population scale because both physician behavior and LLM training data encode historical inequities
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
---
|
||||
|
||||
type: claim
|
||||
domain: health
|
||||
description: "Hallucination rates range from 1.47% for structured transcription to 64.1% for open-ended summarization demonstrating that task-specific benchmarking is required"
|
||||
|
|
@ -13,7 +14,10 @@ related_claims: ["[[AI scribes reached 92 percent provider adoption in under 3 y
|
|||
supports:
|
||||
- No regulatory body globally has established mandatory hallucination rate benchmarks for clinical AI despite evidence base and proposed frameworks
|
||||
reweave_edges:
|
||||
- "Clinical AI errors are 76 percent omissions not commissions inverting the hallucination safety model|related|2026-04-07"
|
||||
- No regulatory body globally has established mandatory hallucination rate benchmarks for clinical AI despite evidence base and proposed frameworks|supports|2026-04-04
|
||||
related:
|
||||
- "Clinical AI errors are 76 percent omissions not commissions inverting the hallucination safety model"
|
||||
---
|
||||
|
||||
# Clinical AI hallucination rates vary 100x by task making single regulatory thresholds operationally inadequate
|
||||
|
|
|
|||
|
|
@ -1,4 +1,6 @@
|
|||
---
|
||||
|
||||
|
||||
type: claim
|
||||
domain: health
|
||||
description: No point in the deployment lifecycle systematically evaluates AI safety for most clinical decision support tools
|
||||
|
|
@ -10,6 +12,12 @@ agent: vida
|
|||
scope: structural
|
||||
sourcer: Babic et al.
|
||||
related_claims: ["[[healthcare AI regulation needs blank-sheet redesign because the FDA drug-and-device model built for static products cannot govern continuously learning software]]", "[[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:
|
||||
- "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"
|
||||
- "FDA's MAUDE database systematically under-detects AI-attributable harm because it has no mechanism for identifying AI algorithm contributions to adverse events"
|
||||
reweave_edges:
|
||||
- "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|supports|2026-04-07"
|
||||
- "FDA's MAUDE database systematically under-detects AI-attributable harm because it has no mechanism for identifying AI algorithm contributions to adverse events|supports|2026-04-07"
|
||||
---
|
||||
|
||||
# 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
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
---
|
||||
|
||||
type: claim
|
||||
domain: health
|
||||
description: The January 2026 guidance creates a regulatory carveout for the highest-volume category of clinical AI deployment without establishing validation criteria
|
||||
|
|
@ -11,9 +12,11 @@ scope: structural
|
|||
sourcer: "Covington & Burling LLP"
|
||||
related_claims: ["[[healthcare AI regulation needs blank-sheet redesign because the FDA drug-and-device model built for static products cannot govern continuously learning software]]", "[[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]]"]
|
||||
related:
|
||||
- "FDA transparency requirements treat clinician ability to understand AI logic as sufficient oversight but automation bias research shows trained physicians defer to flawed AI even when they can understand its reasoning"
|
||||
- FDA's 2026 CDS guidance treats automation bias as a transparency problem solvable by showing clinicians the underlying logic despite research evidence that physicians defer to AI outputs even when reasoning is visible and reviewable
|
||||
- Clinical AI deregulation is occurring during active harm accumulation not after evidence of safety as demonstrated by simultaneous FDA enforcement discretion expansion and ECRI top hazard designation in January 2026
|
||||
reweave_edges:
|
||||
- "FDA transparency requirements treat clinician ability to understand AI logic as sufficient oversight but automation bias research shows trained physicians defer to flawed AI even when they can understand its reasoning|related|2026-04-07"
|
||||
- FDA's 2026 CDS guidance treats automation bias as a transparency problem solvable by showing clinicians the underlying logic despite research evidence that physicians defer to AI outputs even when reasoning is visible and reviewable|related|2026-04-03
|
||||
- Clinical AI deregulation is occurring during active harm accumulation not after evidence of safety as demonstrated by simultaneous FDA enforcement discretion expansion and ECRI top hazard designation in January 2026|related|2026-04-04
|
||||
---
|
||||
|
|
|
|||
|
|
@ -1,4 +1,6 @@
|
|||
---
|
||||
|
||||
|
||||
type: claim
|
||||
domain: health
|
||||
description: Post-market surveillance infrastructure cannot execute on AI safety mandates because the reporting system was designed for static devices not continuously learning algorithms
|
||||
|
|
@ -10,6 +12,12 @@ agent: vida
|
|||
scope: structural
|
||||
sourcer: Handley J.L., Krevat S.A., Fong A. et al.
|
||||
related_claims: ["[[healthcare AI regulation needs blank-sheet redesign because the FDA drug-and-device model built for static products cannot govern continuously learning software]]"]
|
||||
supports:
|
||||
- "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"
|
||||
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|2026-04-07"
|
||||
- "FDA's MAUDE database systematically under-detects AI-attributable harm because it has no mechanism for identifying AI algorithm contributions to adverse events|supports|2026-04-07"
|
||||
---
|
||||
|
||||
# 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
|
||||
|
|
|
|||
|
|
@ -1,4 +1,6 @@
|
|||
---
|
||||
|
||||
|
||||
type: claim
|
||||
domain: health
|
||||
description: The 943 adverse events across 823 AI/ML-cleared devices from 2010-2023 represents structural surveillance failure, not a safety record
|
||||
|
|
@ -10,6 +12,12 @@ agent: vida
|
|||
scope: structural
|
||||
sourcer: Babic et al.
|
||||
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]]", "[[healthcare AI regulation needs blank-sheet redesign because the FDA drug-and-device model built for static products cannot govern continuously learning software]]"]
|
||||
supports:
|
||||
- "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"
|
||||
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|2026-04-07"
|
||||
- "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|supports|2026-04-07"
|
||||
---
|
||||
|
||||
# FDA's MAUDE database systematically under-detects AI-attributable harm because it has no mechanism for identifying AI algorithm contributions to adverse events
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
---
|
||||
|
||||
type: claim
|
||||
domain: health
|
||||
description: The guidance frames automation bias as a behavioral issue addressable through transparency rather than a cognitive architecture problem
|
||||
|
|
@ -14,6 +15,9 @@ challenges:
|
|||
- "FDA's 2026 CDS guidance expands enforcement discretion to cover AI tools providing single clinically appropriate recommendations while leaving clinical appropriateness undefined and requiring no bias evaluation or post-market surveillance"
|
||||
reweave_edges:
|
||||
- "FDA's 2026 CDS guidance expands enforcement discretion to cover AI tools providing single clinically appropriate recommendations while leaving clinical appropriateness undefined and requiring no bias evaluation or post-market surveillance|challenges|2026-04-03"
|
||||
- "FDA transparency requirements treat clinician ability to understand AI logic as sufficient oversight but automation bias research shows trained physicians defer to flawed AI even when they can understand its reasoning|supports|2026-04-07"
|
||||
supports:
|
||||
- "FDA transparency requirements treat clinician ability to understand AI logic as sufficient oversight but automation bias research shows trained physicians defer to flawed AI even when they can understand its reasoning"
|
||||
---
|
||||
|
||||
# FDA's 2026 CDS guidance treats automation bias as a transparency problem solvable by showing clinicians the underlying logic despite research evidence that physicians defer to AI outputs even when reasoning is visible and reviewable
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
---
|
||||
|
||||
type: claim
|
||||
domain: health
|
||||
description: Systematic review of 57 studies establishes the specific SDOH mechanisms behind US hypertension treatment failure
|
||||
|
|
@ -14,8 +15,10 @@ attribution:
|
|||
related: ["only 23 percent of treated us hypertensives achieve blood pressure control demonstrating pharmacological availability is not the binding constraint"]
|
||||
supports:
|
||||
- "food as medicine interventions produce clinically significant improvements during active delivery but benefits fully revert when structural food environment support is removed"
|
||||
- "Food insecurity creates a bidirectional reinforcing loop with cardiovascular disease where disease drives dietary insufficiency through medical costs and dietary insufficiency drives disease through ultra-processed food reliance"
|
||||
reweave_edges:
|
||||
- "food as medicine interventions produce clinically significant improvements during active delivery but benefits fully revert when structural food environment support is removed|supports|2026-04-03"
|
||||
- "Food insecurity creates a bidirectional reinforcing loop with cardiovascular disease where disease drives dietary insufficiency through medical costs and dietary insufficiency drives disease through ultra-processed food reliance|supports|2026-04-07"
|
||||
---
|
||||
|
||||
# Five adverse SDOH independently predict hypertension risk and poor BP control: food insecurity, unemployment, poverty-level income, low education, and government or no insurance
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
---
|
||||
|
||||
type: claim
|
||||
domain: health
|
||||
description: First prospective cohort evidence showing food insecurity precedes CVD development by 20 years, proving causal direction rather than mere correlation
|
||||
|
|
@ -13,8 +14,10 @@ attribution:
|
|||
context: "CARDIA Study Group / Northwestern Medicine, JAMA Cardiology 2025, 3,616 participants followed 2000-2020"
|
||||
supports:
|
||||
- "food as medicine interventions produce clinically significant improvements during active delivery but benefits fully revert when structural food environment support is removed"
|
||||
- "Food insecurity creates a bidirectional reinforcing loop with cardiovascular disease where disease drives dietary insufficiency through medical costs and dietary insufficiency drives disease through ultra-processed food reliance"
|
||||
reweave_edges:
|
||||
- "food as medicine interventions produce clinically significant improvements during active delivery but benefits fully revert when structural food environment support is removed|supports|2026-04-03"
|
||||
- "Food insecurity creates a bidirectional reinforcing loop with cardiovascular disease where disease drives dietary insufficiency through medical costs and dietary insufficiency drives disease through ultra-processed food reliance|supports|2026-04-07"
|
||||
---
|
||||
|
||||
# Food insecurity in young adulthood independently predicts 41% higher CVD incidence in midlife after adjustment for socioeconomic factors, establishing temporality for the SDOH → cardiovascular disease pathway
|
||||
|
|
|
|||
|
|
@ -1,4 +1,6 @@
|
|||
---
|
||||
|
||||
|
||||
type: claim
|
||||
domain: health
|
||||
description: Age-standardized hypertensive disease mortality rose from 23 to 43+ per 100,000 during the same period ischemic heart disease mortality declined, with midlife adults (35–64) showing the most pronounced increases
|
||||
|
|
@ -14,9 +16,13 @@ attribution:
|
|||
related:
|
||||
- racial disparities in hypertension persist after controlling for income and neighborhood indicating structural racism operates through unmeasured mechanisms
|
||||
reweave_edges:
|
||||
- "Hypertension became the primary contributing cardiovascular cause of death in the US since 2022 marking a shift from acute ischemia to chronic metabolic disease as the dominant CVD mortality driver|supports|2026-04-07"
|
||||
- "Hypertensive disease mortality doubled in the US from 1999 to 2023, becoming the leading contributing cause of cardiovascular death by 2022 because obesity and sedentary behavior create treatment-resistant metabolic burden|supports|2026-04-07"
|
||||
- racial disparities in hypertension persist after controlling for income and neighborhood indicating structural racism operates through unmeasured mechanisms|related|2026-04-03
|
||||
- us cvd mortality bifurcating ischemic declining heart failure hypertension worsening|supports|2026-04-04
|
||||
supports:
|
||||
- "Hypertension became the primary contributing cardiovascular cause of death in the US since 2022 marking a shift from acute ischemia to chronic metabolic disease as the dominant CVD mortality driver"
|
||||
- "Hypertensive disease mortality doubled in the US from 1999 to 2023, becoming the leading contributing cause of cardiovascular death by 2022 because obesity and sedentary behavior create treatment-resistant metabolic burden"
|
||||
- us cvd mortality bifurcating ischemic declining heart failure hypertension worsening
|
||||
---
|
||||
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
---
|
||||
|
||||
type: claim
|
||||
domain: health
|
||||
description: The doubling of hypertensive disease mortality since 1999 and its surpassing of ischemic heart disease as a contributing cause represents a fundamental change in CVD epidemiology
|
||||
|
|
@ -10,6 +11,10 @@ agent: vida
|
|||
scope: structural
|
||||
sourcer: American Heart Association
|
||||
related_claims: ["[[medical care explains only 10-20 percent of health outcomes because behavioral social and genetic factors dominate as four independent methodologies confirm]]", "[[value-based care transitions stall at the payment boundary because 60 percent of payments touch value metrics but only 14 percent bear full risk]]", "[[Big Food companies engineer addictive products by hacking evolutionary reward pathways creating a noncommunicable disease epidemic more deadly than the famines specialization eliminated]]"]
|
||||
supports:
|
||||
- "Hypertensive disease mortality doubled in the US from 1999 to 2023, becoming the leading contributing cause of cardiovascular death by 2022 because obesity and sedentary behavior create treatment-resistant metabolic burden"
|
||||
reweave_edges:
|
||||
- "Hypertensive disease mortality doubled in the US from 1999 to 2023, becoming the leading contributing cause of cardiovascular death by 2022 because obesity and sedentary behavior create treatment-resistant metabolic burden|supports|2026-04-07"
|
||||
---
|
||||
|
||||
# Hypertension became the primary contributing cardiovascular cause of death in the US since 2022 marking a shift from acute ischemia to chronic metabolic disease as the dominant CVD mortality driver
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
---
|
||||
|
||||
type: claim
|
||||
domain: health
|
||||
description: Hypertensive disease AAMR increased from 15.8 to 31.9 per 100,000 (1999-2023), driven by obesity, sedentary behavior, and treatment gaps that pharmacological acute care cannot address
|
||||
|
|
@ -11,8 +12,10 @@ scope: causal
|
|||
sourcer: Yan et al. / JACC
|
||||
related_claims: ["[[Big Food companies engineer addictive products by hacking evolutionary reward pathways creating a noncommunicable disease epidemic more deadly than the famines specialization eliminated]]", "[[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:
|
||||
- "Hypertension became the primary contributing cardiovascular cause of death in the US since 2022 marking a shift from acute ischemia to chronic metabolic disease as the dominant CVD mortality driver"
|
||||
- us cvd mortality bifurcating ischemic declining heart failure hypertension worsening
|
||||
reweave_edges:
|
||||
- "Hypertension became the primary contributing cardiovascular cause of death in the US since 2022 marking a shift from acute ischemia to chronic metabolic disease as the dominant CVD mortality driver|supports|2026-04-07"
|
||||
- us cvd mortality bifurcating ischemic declining heart failure hypertension worsening|supports|2026-04-04
|
||||
---
|
||||
|
||||
|
|
|
|||
|
|
@ -1,4 +1,6 @@
|
|||
---
|
||||
|
||||
|
||||
type: claim
|
||||
domain: health
|
||||
description: The cognitive mechanism explaining why clinical AI reinforces rather than corrects physician plans
|
||||
|
|
@ -10,6 +12,12 @@ 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"
|
||||
- "LLMs amplify rather than merely replicate human cognitive biases because sequential processing creates stronger anchoring effects and lack of clinical experience eliminates contextual resistance"
|
||||
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|2026-04-07"
|
||||
- "LLMs amplify rather than merely replicate human cognitive biases because sequential processing creates stronger anchoring effects and lack of clinical experience eliminates contextual resistance|supports|2026-04-07"
|
||||
---
|
||||
|
||||
# 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
|
||||
|
|
|
|||
|
|
@ -1,4 +1,6 @@
|
|||
---
|
||||
|
||||
|
||||
type: claim
|
||||
domain: health
|
||||
description: Analysis of 1.7M outputs from 9 LLMs shows demographic framing alone (race, income, LGBTQIA+ status, housing) alters clinical recommendations when all other case details remain constant
|
||||
|
|
@ -10,6 +12,12 @@ 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"
|
||||
- "LLM-generated nursing care plans exhibit dual-pathway sociodemographic bias affecting both plan content and expert-rated clinical quality"
|
||||
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|2026-04-07"
|
||||
- "LLM-generated nursing care plans exhibit dual-pathway sociodemographic bias affecting both plan content and expert-rated clinical quality|supports|2026-04-07"
|
||||
---
|
||||
|
||||
# LLM clinical recommendations exhibit systematic sociodemographic bias across all model architectures because training data encodes historical healthcare inequities
|
||||
|
|
|
|||
|
|
@ -1,4 +1,6 @@
|
|||
---
|
||||
|
||||
|
||||
type: claim
|
||||
domain: health
|
||||
description: "First empirical evidence that AI bias in nursing care operates through two mechanisms: what the AI generates AND how clinicians perceive quality"
|
||||
|
|
@ -10,6 +12,12 @@ 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"
|
||||
- "LLM clinical recommendations exhibit systematic sociodemographic bias across all model architectures because training data encodes historical healthcare 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|2026-04-07"
|
||||
- "LLM clinical recommendations exhibit systematic sociodemographic bias across all model architectures because training data encodes historical healthcare inequities|supports|2026-04-07"
|
||||
---
|
||||
|
||||
# LLM-generated nursing care plans exhibit dual-pathway sociodemographic bias affecting both plan content and expert-rated clinical quality
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
---
|
||||
|
||||
type: claim
|
||||
domain: health
|
||||
description: Clinical LLMs exhibit anchoring, framing, and confirmation biases similar to humans but may amplify them through architectural differences
|
||||
|
|
@ -10,6 +11,10 @@ agent: vida
|
|||
scope: causal
|
||||
sourcer: npj Digital Medicine 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]]"]
|
||||
supports:
|
||||
- "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"
|
||||
reweave_edges:
|
||||
- "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|supports|2026-04-07"
|
||||
---
|
||||
|
||||
# LLMs amplify rather than merely replicate human cognitive biases because sequential processing creates stronger anchoring effects and lack of clinical experience eliminates contextual resistance
|
||||
|
|
|
|||
|
|
@ -1,10 +1,15 @@
|
|||
---
|
||||
|
||||
description: OpenEvidence scored 100 percent on USMLE and GPT-4 outperforms ED residents on structured cases but a multi-hospital RCT showed no diagnostic accuracy improvement with AI access suggesting the value of clinical AI is workflow efficiency not diagnostic augmentation
|
||||
type: claim
|
||||
domain: health
|
||||
created: 2026-02-17
|
||||
source: "OpenEvidence USMLE 100%; GPT-4 vs ED physicians (PMC 2024); UVA/Stanford/Harvard randomized trial (Stanford HAI 2025)"
|
||||
confidence: likely
|
||||
related:
|
||||
- "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"
|
||||
reweave_edges:
|
||||
- "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|related|2026-04-07"
|
||||
---
|
||||
|
||||
# medical LLM benchmark performance does not translate to clinical impact because physicians with and without AI access achieve similar diagnostic accuracy in randomized trials
|
||||
|
|
|
|||
|
|
@ -1,4 +1,6 @@
|
|||
---
|
||||
|
||||
|
||||
type: claim
|
||||
domain: health
|
||||
description: The post-2010 period shows outright increases in CVD mortality for middle-aged adults in multiple states, marking a true reversal of decades of progress
|
||||
|
|
@ -10,6 +12,12 @@ agent: vida
|
|||
scope: causal
|
||||
sourcer: Leah Abrams, Neil Mehta
|
||||
related_claims: ["[[Americas declining life expectancy is driven by deaths of despair concentrated in populations and regions most damaged by economic restructuring since the 1980s]]", "[[Big Food companies engineer addictive products by hacking evolutionary reward pathways creating a noncommunicable disease epidemic more deadly than the famines specialization eliminated]]"]
|
||||
supports:
|
||||
- "CVD mortality stagnation after 2010 affects all income levels including the wealthiest counties indicating structural system failure not poverty correlation"
|
||||
- "CVD mortality stagnation drives US life expectancy plateau 3-11x more than drug deaths inverting the dominant opioid crisis narrative"
|
||||
reweave_edges:
|
||||
- "CVD mortality stagnation after 2010 affects all income levels including the wealthiest counties indicating structural system failure not poverty correlation|supports|2026-04-07"
|
||||
- "CVD mortality stagnation drives US life expectancy plateau 3-11x more than drug deaths inverting the dominant opioid crisis narrative|supports|2026-04-07"
|
||||
---
|
||||
|
||||
# Midlife CVD mortality (ages 40-64) increased in many US states after 2010 representing a reversal not merely stagnation
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
---
|
||||
|
||||
type: claim
|
||||
domain: health
|
||||
description: FDA expanded CDS enforcement discretion on January 6 2026 in the same month ECRI published AI chatbots as the number one health technology hazard revealing temporal contradiction between regulatory rollback and patient safety alarm
|
||||
|
|
@ -13,9 +14,11 @@ related_claims: ["[[healthcare AI regulation needs blank-sheet redesign because
|
|||
supports:
|
||||
- "Clinical AI chatbot misuse is a documented ongoing harm source not a theoretical risk as evidenced by ECRI ranking it the number one health technology hazard for two consecutive years"
|
||||
- "FDA's 2026 CDS guidance expands enforcement discretion to cover AI tools providing single clinically appropriate recommendations while leaving clinical appropriateness undefined and requiring no bias evaluation or post-market surveillance"
|
||||
- "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"
|
||||
reweave_edges:
|
||||
- "Clinical AI chatbot misuse is a documented ongoing harm source not a theoretical risk as evidenced by ECRI ranking it the number one health technology hazard for two consecutive years|supports|2026-04-03"
|
||||
- "FDA's 2026 CDS guidance expands enforcement discretion to cover AI tools providing single clinically appropriate recommendations while leaving clinical appropriateness undefined and requiring no bias evaluation or post-market surveillance|supports|2026-04-03"
|
||||
- "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|2026-04-07"
|
||||
---
|
||||
|
||||
# Clinical AI deregulation is occurring during active harm accumulation not after evidence of safety as demonstrated by simultaneous FDA enforcement discretion expansion and ECRI top hazard designation in January 2026
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
---
|
||||
|
||||
type: claim
|
||||
domain: health
|
||||
description: Both EU Commission and FDA loosened clinical AI requirements within two months despite six documented failure modes in research literature
|
||||
|
|
@ -10,6 +11,10 @@ agent: vida
|
|||
scope: causal
|
||||
sourcer: Petrie-Flom Center, Harvard Law School
|
||||
related_claims: ["[[healthcare AI regulation needs blank-sheet redesign because the FDA drug-and-device model built for static products cannot govern continuously learning software]]", "[[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]]"]
|
||||
supports:
|
||||
- "EU Commission's December 2025 medical AI deregulation proposal removes default high-risk AI requirements shifting burden from requiring safety demonstration to allowing commercial deployment without mandated oversight"
|
||||
reweave_edges:
|
||||
- "EU Commission's December 2025 medical AI deregulation proposal removes default high-risk AI requirements shifting burden from requiring safety demonstration to allowing commercial deployment without mandated oversight|supports|2026-04-07"
|
||||
---
|
||||
|
||||
# Regulatory rollback of clinical AI oversight in EU and US during 2025-2026 represents coordinated or parallel regulatory capture occurring simultaneously with accumulating research evidence of failure modes
|
||||
|
|
|
|||
|
|
@ -1,4 +1,6 @@
|
|||
---
|
||||
|
||||
|
||||
type: claim
|
||||
domain: health
|
||||
description: The 10-15 year patent gap between semaglutide (2026-2033 expiry) and tirzepatide (2036-2041 expiry) creates two economically distinct GLP-1 markets with different cost trajectories
|
||||
|
|
@ -10,6 +12,12 @@ 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"
|
||||
- "Indian generic semaglutide exports enabled by evergreening rejection create a global access pathway before US patent expiry"
|
||||
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|2026-04-07"
|
||||
- "Indian generic semaglutide exports enabled by evergreening rejection create a global access pathway before US patent expiry|supports|2026-04-07"
|
||||
---
|
||||
|
||||
# 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
|
||||
|
|
|
|||
|
|
@ -1,4 +1,6 @@
|
|||
---
|
||||
|
||||
|
||||
type: claim
|
||||
domain: health
|
||||
description: The divergent trends by CVD subtype show that procedural care improvements for acute ischemia coexist with worsening chronic metabolic disease burden
|
||||
|
|
@ -10,6 +12,13 @@ agent: vida
|
|||
scope: structural
|
||||
sourcer: American Heart Association
|
||||
related_claims: ["[[Americas declining life expectancy is driven by deaths of despair concentrated in populations and regions most damaged by economic restructuring since the 1980s]]", "[[medical care explains only 10-20 percent of health outcomes because behavioral social and genetic factors dominate as four independent methodologies confirm]]", "[[healthcare AI creates a Jevons paradox because adding capacity to sick care induces more demand for sick care]]"]
|
||||
related:
|
||||
- "Hypertension became the primary contributing cardiovascular cause of death in the US since 2022 marking a shift from acute ischemia to chronic metabolic disease as the dominant CVD mortality driver"
|
||||
reweave_edges:
|
||||
- "Hypertension became the primary contributing cardiovascular cause of death in the US since 2022 marking a shift from acute ischemia to chronic metabolic disease as the dominant CVD mortality driver|related|2026-04-07"
|
||||
- "Hypertensive disease mortality doubled in the US from 1999 to 2023, becoming the leading contributing cause of cardiovascular death by 2022 because obesity and sedentary behavior create treatment-resistant metabolic burden|supports|2026-04-07"
|
||||
supports:
|
||||
- "Hypertensive disease mortality doubled in the US from 1999 to 2023, becoming the leading contributing cause of cardiovascular death by 2022 because obesity and sedentary behavior create treatment-resistant metabolic burden"
|
||||
---
|
||||
|
||||
# US CVD mortality is bifurcating with ischemic heart disease declining while heart failure and hypertensive disease reach all-time highs revealing that aggregate improvement masks structural deterioration in cardiometabolic health
|
||||
|
|
|
|||
|
|
@ -1,6 +1,7 @@
|
|||
---
|
||||
|
||||
|
||||
|
||||
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
|
||||
|
|
@ -10,9 +11,11 @@ confidence: likely
|
|||
related:
|
||||
- "AI talent circulation between frontier labs transfers alignment culture not just capability because researchers carry safety methodologies and institutional norms to their new organizations"
|
||||
- "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"
|
||||
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"
|
||||
---
|
||||
|
||||
# the alignment tax creates a structural race to the bottom because safety training costs capability and rational competitors skip it
|
||||
|
|
|
|||
Loading…
Reference in a new issue