Measures whether cross-database fusion reaches what a query needs, scored
on retrieval alone so no model or judge sits between the fusion and the
number.
The corpus is MTRAG ClapNQ partitioned by article title, whole titles to a
collection, so an article's passages never split and a query's gold stays
concentrated in one collection, which is the condition a per-collection
depth quota punishes. collection_of keys on sha256 rather than hash(),
which is salted per process: the partition is never stored, and scoring
recomputes it in a different process than the one that ingested.
The 148 titles holding a gold passage carry 10,723 passages between them,
so a budget near that floor leaves no cross-topic distractors and inflates
recall. The default is 40,000 and the build reports the gold/distractor
split, warning when there are none.
build_databases opens each collection by configured name with a scope of
one, since populate_db writes to a single database. The operator entry
point emits the config for the partition it just built, so a config cannot
search a differently-partitioned build.
Claude-Session: https://claude.ai/code/session_01WhudUtZm6qqiuv8Y1sbwSc
Port 11433 serves nothing; Qwen3-Reranker-4B is on 11455. Qwen3.6 spends
its budget reasoning before it answers, and its 131072 window leaves ample
input space at 32768.
IBM's MTRAG benchmark (ClapNQ domain, pinned repo SHA): retrieval with
Recall@k/nDCG@k against binary qrels, gold-prefix QA replaying reference
conversation prefixes as message history, and live-session replay
carrying the model's own answers and tool history across turns.
Corpus population gains a bounded, resumable batched ingest path.
ConversationInput case type with transcript rendering for the judge,
eligibility-aware citation scoring, refusal precision/recall via a
label-aware RefusalJudge, per-turn verdicts with judged-turn coverage,
and per-turn tool-traffic attributes counted from each turn's new
messages so the arrays survive prior-turn compaction.
