Temporal Validity in Retrieval Memory: Eliminating Stale-Fact Errors for AI Agents over Evolving Knowledge · HackerPapers discussions

Temporal Validity in Retrieval Memory: Eliminating Stale-Fact Errors for AI Agents over Evolving Knowledge — mentioned by yadu989

Mon, 27 Jul 2026 02:05:05 GMT

One concrete mechanism behind this fragility that doesn't get enough attention: retrieval systems that serve stale facts with high confidence. RAG pipelines rank by cosine similarity, which has no notion of supersession, an embedding of "the current API endpoint is X" and "the current API endpoint is Y (updated)" can be nearly identical in vector space. Our benchmarks found cosine similarity separates current from superseded facts at AUROC ~0.59, essentially chance. The model then reasons correctly from wrong premises, which is the worst failure mode: no error signal, just silent drift. The…

Paper and discussion history. A mention is not an endorsement.

Temporal Validity in Retrieval Memory: Eliminating Stale-Fact Errors for AI Agents over Evolving Knowledge — mentioned by yadu989

Mon, 27 Jul 2026 02:04:18 GMT

Knowledge graphs are genuinely interesting as a grounding mechanism because they make entity relationships explicit and queryable, something flat vector stores can't do. Overture's cross-theme approach (linking buildings, roads, POIs into a unified graph) gives the LLM a structured substrate rather than a bag of retrieved chunks. The piece I'd push on: KGs solve the structural grounding problem but don't automatically solve the temporal one. A KG edge asserting "restaurant X is at address Y" is only as fresh as the last ingest. Cosine similarity over embeddings has the same blindspot- our…

Paper and discussion history. A mention is not an endorsement.