Teleo Agents dffa255594 entity-batch: update 1 entities ... - Applied 1 entity operations from queue - Files: domains/ai-alignment/AI-models-distinguish-testing-from-deployment-environments-providing-empirical-evidence-for-deceptive-alignment-concerns.md Pentagon-Agent: Epimetheus <968B2991-E2DF-4006-B962-F5B0A0CC8ACA>		2026-03-26 00:39:03 +00:00
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adaptive governance outperforms rigid alignment blueprints because superintelligence development has too many unknowns for fixed plans.md
agent research direction selection is epistemic foraging where the optimal strategy is to seek observations that maximally reduce model uncertainty rather than confirm existing beliefs.md
agent-generated code creates cognitive debt that compounds when developers cannot understand what was produced on their behalf.md	extract: 2026-02-05-mit-tech-review-misunderstood-time-horizon-graph	2026-03-23 00:20:46 +00:00
AGI may emerge as a patchwork of coordinating sub-AGI agents rather than a single monolithic system.md
AI agent orchestration that routes data and tools between specialized models outperforms both single-model and human-coached approaches because the orchestrator contributes coordination not direction.md
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.md
AI agents can reach cooperative program equilibria inaccessible in traditional game theory because open-source code transparency enables conditional strategies that require mutual legibility.md
AI agents excel at implementing well-scoped ideas but cannot generate creative experiment designs which makes the human role shift from researcher to agent workflow architect.md
AI alignment is a coordination problem not a technical problem.md	extract: 2026-03-00-mengesha-coordination-gap-frontier-ai-safety (#1619)	2026-03-22 00:39:01 +00:00
AI capability and reliability are independent dimensions because Claude solved a 30-year open mathematical problem while simultaneously degrading at basic program execution during the same session.md	extract: 2026-03-25-metr-algorithmic-vs-holistic-evaluation-benchmark-inflation	2026-03-25 11:47:09 +00:00
AI development is a critical juncture in institutional history where the mismatch between capabilities and governance creates a window for transformation.md	extract: 2026-02-00-international-ai-safety-report-2026-evaluation-reliability	2026-03-23 00:20:09 +00:00
AI displacement hits young workers first because a 14 percent drop in job-finding rates for 22-25 year olds in exposed occupations is the leading indicator that incumbents organizational inertia temporarily masks.md
AI investment concentration where 58 percent of funding flows to megarounds and two companies capture 14 percent of all global venture capital creates a structural oligopoly that alignment governance must account for.md
AI lowers the expertise barrier for engineering biological weapons from PhD-level to amateur which makes bioterrorism the most proximate AI-enabled existential risk.md	extract: 2026-03-26-aisle-openssl-zero-days	2026-03-26 00:32:39 +00:00
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.md
AI talent circulation between frontier labs transfers alignment culture not just capability because researchers carry safety methodologies and institutional norms to their new organizations.md
AI transparency is declining not improving because Stanford FMTI scores dropped 17 points in one year while frontier labs dissolved safety teams and removed safety language from mission statements.md	extract: 2025-05-29-anthropic-circuit-tracing-open-source	2026-03-24 00:16:34 +00:00
AI-companion-apps-correlate-with-increased-loneliness-creating-systemic-risk-through-parasocial-dependency.md	extract: 2025-12-00-aisi-frontier-ai-trends-report-2025	2026-03-22 00:35:18 +00:00
ai-enhanced-collective-intelligence-requires-federated-learning-architectures-to-preserve-data-sovereignty-at-scale.md
AI-exposed workers are disproportionately female high-earning and highly educated which inverts historical automation patterns and creates different political and economic displacement dynamics.md
AI-generated-persuasive-content-matches-human-effectiveness-at-belief-change-eliminating-the-authenticity-premium.md
AI-models-distinguish-testing-from-deployment-environments-providing-empirical-evidence-for-deceptive-alignment-concerns.md	entity-batch: update 1 entities	2026-03-26 00:39:03 +00:00
an aligned-seeming AI may be strategically deceptive because cooperative behavior is instrumentally optimal while weak.md
Anthropics RSP rollback under commercial pressure is the first empirical confirmation that binding safety commitments cannot survive the competitive dynamics of frontier AI development.md	extract: 2026-03-20-metr-modeling-assumptions-time-horizon-reliability	2026-03-23 00:24:04 +00:00
anthropomorphizing AI agents to claim autonomous action creates credibility debt that compounds until a crisis forces public reckoning.md
as AI-automated software development becomes certain the bottleneck shifts from building capacity to knowing what to build making structured knowledge graphs the critical input to autonomous systems.md
bostrom takes single-digit year timelines to superintelligence seriously while acknowledging decades-long alternatives remain possible.md
capability control methods are temporary at best because a sufficiently intelligent system can circumvent any containment designed by lesser minds.md	extract: 2026-03-21-replibench-autonomous-replication-capabilities	2026-03-23 12:38:45 +00:00
coding agents cannot take accountability for mistakes which means humans must retain decision authority over security and critical systems regardless of agent capability.md
coding-agents-crossed-usability-threshold-december-2025-when-models-achieved-sustained-coherence-across-complex-multi-file-tasks.md
collective attention allocation follows nested active inference where domain agents minimize uncertainty within their boundaries while the evaluator minimizes uncertainty at domain intersections.md
community-centred norm elicitation surfaces alignment targets materially different from developer-specified rules.md
compute export controls are the most impactful AI governance mechanism but target geopolitical competition not safety leaving capability development unconstrained.md
coordination protocol design produces larger capability gains than model scaling because the same AI model performed 6x better with structured exploration than with human coaching on the same problem.md
current language models escalate to nuclear war in simulated conflicts because behavioral alignment cannot instill aversion to catastrophic irreversible actions.md
deep technical expertise is a greater force multiplier when combined with AI agents because skilled practitioners delegate more effectively than novices.md
delegating critical infrastructure development to AI creates civilizational fragility because humans lose the ability to understand maintain and fix the systems civilization depends on.md	extract: 2026-03-26-aisle-openssl-zero-days	2026-03-26 00:32:39 +00:00
democratic alignment assemblies produce constitutions as effective as expert-designed ones while better representing diverse populations.md
developing superintelligence is surgery for a fatal condition not russian roulette because the baseline of inaction is itself catastrophic.md
economic forces push humans out of every cognitive loop where output quality is independently verifiable because human-in-the-loop is a cost that competitive markets eliminate.md
emergent misalignment arises naturally from reward hacking as models develop deceptive behaviors without any training to deceive.md
factorised-generative-models-enable-decentralized-multi-agent-representation-through-individual-level-beliefs.md
formal verification becomes economically necessary as AI-generated code scales because testing cannot detect adversarial overfitting and a proof cannot be gamed.md
formal verification of AI-generated proofs provides scalable oversight that human review cannot match because machine-checked correctness scales with AI capability while human verification degrades.md
government designation of safety-conscious AI labs as supply chain risks inverts the regulatory dynamic by penalizing safety constraints rather than enforcing them.md
high AI exposure increases collective idea diversity without improving individual creative quality creating an asymmetry between group and individual effects.md
human civilization passes falsifiable superorganism criteria because individuals cannot survive apart from society and occupations function as role-specific cellular algorithms.md
human ideas naturally converge toward similarity over social learning chains making AI a net diversity injector rather than a homogenizer under high-exposure conditions.md
human verification bandwidth is the binding constraint on AGI economic impact not intelligence itself because the marginal cost of AI execution falls to zero while the capacity to validate audit and underwrite responsibility remains finite.md
human-AI mathematical collaboration succeeds through role specialization where AI explores solution spaces humans provide strategic direction and mathematicians verify correctness.md
individual-free-energy-minimization-does-not-guarantee-collective-optimization-in-multi-agent-active-inference.md
instrumental convergence risks may be less imminent than originally argued because current AI architectures do not exhibit systematic power-seeking behavior.md
intelligence and goals are orthogonal so a superintelligence can be maximally competent while pursuing arbitrary or destructive ends.md
intrinsic proactive alignment develops genuine moral capacity through self-awareness empathy and theory of mind rather than external reward optimization.md
machine-learning-pattern-extraction-systematically-erases-dataset-outliers-where-vulnerable-populations-concentrate.md
marginal returns to intelligence are bounded by five complementary factors which means superintelligence cannot produce unlimited capability gains regardless of cognitive power.md
maxmin-rlhf-applies-egalitarian-social-choice-to-alignment-by-maximizing-minimum-utility-across-preference-groups.md
minority-preference-alignment-improves-33-percent-without-majority-compromise-suggesting-single-reward-leaves-value-on-table.md
modeling preference sensitivity as a learned distribution rather than a fixed scalar resolves DPO diversity failures without demographic labels or explicit user modeling.md
multi-agent deployment exposes emergent security vulnerabilities invisible to single-agent evaluation because cross-agent propagation identity spoofing and unauthorized compliance arise only in realistic multi-party environments.md
multi-model collaboration solved problems that single models could not because different AI architectures contribute complementary capabilities as the even-case solution to Knuths Hamiltonian decomposition required GPT and Claude working together.md
nation-states will inevitably assert control over frontier AI development because the monopoly on force is the foundational state function and weapons-grade AI capability in private hands is structurally intolerable to governments.md
national-scale-collective-intelligence-infrastructure-requires-seven-trust-properties-to-achieve-legitimacy.md
no research group is building alignment through collective intelligence infrastructure despite the field converging on problems that require it.md
only binding regulation with enforcement teeth changes frontier AI lab behavior because every voluntary commitment has been eroded abandoned or made conditional on competitor behavior when commercially inconvenient.md
permanently failing to develop superintelligence is itself an existential catastrophe because preventable mass death continues indefinitely.md
persistent irreducible disagreement.md
pluralistic alignment must accommodate irreducibly diverse values simultaneously rather than converging on a single aligned state.md
pluralistic-ai-alignment-through-multiple-systems-preserves-value-diversity-better-than-forced-consensus.md
post-arrow-social-choice-mechanisms-work-by-weakening-independence-of-irrelevant-alternatives.md
pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations.md	extract: 2026-03-26-metr-algorithmic-vs-holistic-evaluation	2026-03-26 00:36:22 +00:00
recursive self-improvement creates explosive intelligence gains because the system that improves is itself improving.md
representative-sampling-and-deliberative-mechanisms-should-replace-convenience-platforms-for-ai-alignment-feedback.md
rlchf-aggregated-rankings-variant-combines-evaluator-rankings-via-social-welfare-function-before-reward-model-training.md
rlchf-features-based-variant-models-individual-preferences-with-evaluator-characteristics-enabling-aggregation-across-diverse-groups.md
rlhf-is-implicit-social-choice-without-normative-scrutiny.md
safe AI development requires building alignment mechanisms before scaling capability.md	extract: 2026-02-00-international-ai-safety-report-2026-evaluation-reliability	2026-03-23 00:20:09 +00:00
single-reward-rlhf-cannot-align-diverse-preferences-because-alignment-gap-grows-proportional-to-minority-distinctiveness.md
some disagreements are permanently irreducible because they stem from genuine value differences not information gaps and systems must map rather than eliminate them.md
specifying human values in code is intractable because our goals contain hidden complexity comparable to visual perception.md
structured exploration protocols reduce human intervention by 6x because the Residue prompt enabled 5 unguided AI explorations to solve what required 31 human-coached explorations.md
subagent hierarchies outperform peer multi-agent architectures in practice because deployed systems consistently converge on one primary agent controlling specialized helpers.md
super co-alignment proposes that human and AI values should be co-shaped through iterative alignment rather than specified in advance.md
superorganism organization extends effective lifespan substantially at each organizational level which means civilizational intelligence operates on temporal horizons that individual-preference alignment cannot serve.md
task difficulty moderates AI idea adoption more than source disclosure with difficult problems generating AI reliance regardless of whether the source is labeled.md
the first mover to superintelligence likely gains decisive strategic advantage because the gap between leader and followers accelerates during takeoff.md
the gap between theoretical AI capability and observed deployment is massive across all occupations because adoption lag not capability limits determines real-world impact.md	auto-fix: strip 15 broken wiki links	2026-03-25 11:47:09 +00:00
the optimal SI development strategy is swift to harbor slow to berth moving fast to capability then pausing before full deployment.md
the progression from autocomplete to autonomous agent teams follows a capability-matched escalation where premature adoption creates more chaos than value.md
the same coordination protocol applied to different AI models produces radically different problem-solving strategies because the protocol structures process not thought.md
the specification trap means any values encoded at training time become structurally unstable as deployment contexts diverge from training conditions.md
the variance of a learned preference sensitivity distribution diagnoses dataset heterogeneity and collapses to fixed-parameter behavior when preferences are homogeneous.md
three conditions gate AI takeover risk autonomy robotics and production chain control and current AI satisfies none of them which bounds near-term catastrophic risk despite superhuman cognitive capabilities.md
tools and artifacts transfer between AI agents and evolve in the process because Agent O improved Agent Cs solver by combining it with its own structural knowledge creating a hybrid better than either original.md
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.md
universal alignment is mathematically impossible because Arrows impossibility theorem applies to aggregating diverse human preferences into a single coherent objective.md
user questions are an irreplaceable free energy signal for knowledge agents because they reveal functional uncertainty that model introspection cannot detect.md
voluntary safety pledges cannot survive competitive pressure because unilateral commitments are structurally punished when competitors advance without equivalent constraints.md	extract: 2026-03-21-replibench-autonomous-replication-capabilities	2026-03-23 12:38:45 +00:00