LLMs routinely produce unsupported causal stories for personal sensing anomalies, and richer evidence or constrained prompts do not reliably eliminate this epistemic overreach.
A dataset for answering time-sensitive questions
5 Pith papers cite this work. Polarity classification is still indexing.
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Language models frequently violate temporal scope stability in multi-turn dialogues by drifting toward present-day assumptions even when they possess the correct facts.
Temporal knowledge drift is encoded as a geometrically orthogonal direction in LLM residual streams, independent of correctness and uncertainty.
NuggetIndex manages atomic nuggets with temporal validity and lifecycle metadata to filter outdated information before ranking, yielding 42% higher nugget recall, 9pp better temporal correctness, and 55% fewer conflicts than passage or unmanaged proposition baselines.
Claim-selective certification decomposes medical RAG responses into verifiable claims scored against retrieved evidence and mapped via an intent-aware selector to actions, reporting zero UCCR and action accuracy of 0.92 on dev and 0.90 on test.
citing papers explorer
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Causal Stories from Sensor Traces: Auditing Epistemic Overreach in LLM-Generated Personal Sensing Explanations
LLMs routinely produce unsupported causal stories for personal sensing anomalies, and richer evidence or constrained prompts do not reliably eliminate this epistemic overreach.
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Evaluating Temporal Consistency in Multi-Turn Language Models
Language models frequently violate temporal scope stability in multi-turn dialogues by drifting toward present-day assumptions even when they possess the correct facts.
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The Geometry of Forgetting: Temporal Knowledge Drift as an Independent Axis in LLM Representations
Temporal knowledge drift is encoded as a geometrically orthogonal direction in LLM residual streams, independent of correctness and uncertainty.
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NuggetIndex: Governed Atomic Retrieval for Maintainable RAG
NuggetIndex manages atomic nuggets with temporal validity and lifecycle metadata to filter outdated information before ranking, yielding 42% higher nugget recall, 9pp better temporal correctness, and 55% fewer conflicts than passage or unmanaged proposition baselines.
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Claim-Selective Certification for High-Risk Medical Retrieval-Augmented Generation
Claim-selective certification decomposes medical RAG responses into verifiable claims scored against retrieved evidence and mapped via an intent-aware selector to actions, reporting zero UCCR and action accuracy of 0.92 on dev and 0.90 on test.