`mtrag_federated` partitions one domain by article title, which is
round-robin fusion's friendliest case: no collection is ever off-topic for a
query, so the guaranteed-slot waste that hurts a real deployment is never
exercised. Every fusion conclusion measured on it is therefore provisional.
`mtrag_pooled` pools all four MTRAG domains, so a query belongs to one and
the rest are genuinely off-topic. `collection_of` gains `alpha`, which now
means something: 0 keeps a collection to one domain, 1 ignores the domain
and shards titles uniformly. Domains map onto collections proportionally,
subdividing by title where there are more collections than domains and
grouping where there are fewer.
Passage ids are checked for collisions across domains, since gold is
uri-keyed and a shared id would make it ambiguous.
Claude-Session: https://claude.ai/code/session_01WhudUtZm6qqiuv8Y1sbwSc
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.
