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clawhdf5/crates
osobhandClaude Opus 5 306a35347c bench: use real session dates, and measure recency discrimination
Two gaps in the LongMemEval harness, both of which had to close before any
recency feature could be judged.

The store was fed a synthetic counter (ts += 1.0 per turn) and the dataset's
own `haystack_dates` were ignored. Session order happened to be chronological,
so ordering was right, but the intervals were fiction — and exponential decay
is a function of the interval, so anything time-aware was being measured
against made-up ages. Dates are now parsed (civil-from-days, pinned against
reference values) and turns are spread over the minutes after their session
start; an unparseable date falls back to position so order still holds.

`newest_gold_first` measures what recall cannot. On a `knowledge-update`
question LongMemEval labels *both* the stale session and the one that
supersedes it as gold, so returning either scores as a hit even though only
one answers the question. The new metric asks whether the newest gold session
outranked the older ones. The current retriever scores 43-45% on it across
every mode — chance — which is the gap a temporal signal is supposed to close.

Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
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