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 Our benchmarks found cosine similarity separates current from superseded facts at AUROC ~0.59, essentially chance.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 fix we've been exploring is treating retrieval as a bi-temporal ledger problem rather than a similarity problem — explicit (subject, relation, object) supersession so a newer fact structurally invalidates the older one, not just outranks it probabilistically. That gets stale-fact error close to zero on evolving-fact benchmarks where RAG scores 0.20–0.47.
Paper with the full benchmark methodology if anyone wants to dig in: https://arxiv.org/abs/2606.26511 (disclosure: I'm one of the authors).
Curious whether others have hit this in production. does your team have a way to detect when the retrieval layer is confidently serving outdated context, or do you mostly find out from user reports?
The fix we've been exploring is treating retrieval as a bi-temporal ledger problem rather than a similarity problem — explicit (subject, relation, object) supersession so a newer fact structurally invalidates the older one, not just outranks it probabilistically. That gets stale-fact error close to zero on evolving-fact benchmarks where RAG scores 0.20–0.47.
Paper with the full benchmark methodology if anyone wants to dig in: https://arxiv.org/abs/2606.26511 (disclosure: I'm one of the authors).
Curious whether others have hit this in production. does your team have a way to detect when the retrieval layer is confidently serving outdated context, or do you mostly find out from user reports?
Always happy to talk about local-first AI architecture.
Email: [email protected]