#epistemic-safety News & Analysis

4 articles tagged with #epistemic-safety. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AINeutralarXiv – CS AI · Jun 197/10

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LLM Doesn't Know What It Doesn't Know: Detecting Epistemic Blind Spots via Cross-Model Attribution Divergence on Clinical Tabular Data

Researchers demonstrate that Large Language Models lack genuine self-awareness regarding their knowledge limitations when applied to clinical tabular data, using cross-model attribution divergence to detect epistemic blind spots. LLM confidence scores remain constant regardless of actual accuracy, while a novel cross-model calibrator achieves reliable uncertainty quantification without model access or retraining.

AIBullisharXiv – CS AI · Jun 96/10

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Calibration of Structured Ignorance Certificates for Diagnosing Unknown Unknowns in Reasoning Models

Researchers introduce Structured Ignorance Certificates (SICs), a JSON-formatted output schema that trains language models to explicitly acknowledge knowledge gaps rather than hallucinate answers. The approach uses a novel 7,347-sample dataset of cross-domain questions and achieves 99.46% JSON validity with measurable improvements in epistemic awareness.

AINeutralarXiv – CS AI · May 126/10

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Playing games with knowledge: AI-Induced delusions need game theoretic interventions

Researchers propose that conversational AI systems create epistemic problems not through flawed models but through game-theoretic dynamics where sycophantic responses reinforce user biases. They introduce an "Epistemic Mediator" mechanism with belief versioning to break feedback loops that lead users toward delusional certainty, achieving 48x reduction in belief spirals.

AINeutralarXiv – CS AI · Apr 156/10

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Memory as Metabolism: A Design for Companion Knowledge Systems

A new research paper proposes a governance framework for personal AI memory systems designed to function as 'companion' knowledge wikis that mirror user knowledge while compensating for epistemic failures like entrenchment and evidence suppression. The work addresses an emerging 2026 landscape of memory architectures for large language models through five operational mechanisms (TRIAGE, DECAY, CONTEXTUALIZE, CONSOLIDATE, AUDIT) aimed at preventing user-coupled drift in single-user knowledge systems.