RuFlo Files

What ships — batched per "no constant releases"

Four independent threads:

1. 3rd BEIR dataset (ArguAna) — strengthens 2-dataset → 3-dataset story
2. BGE-large NFCorpus ceiling test — answered (no lift on this hardware)
3. ruvector@0.2.27 Tier-0 wiring — kills the silent-fallback bug at source
4. 4 user bugs from #2246 — 3 fixed, 1 forwarded

3-dataset BEIR results

Dataset	nDCG@10	Pipeline	Rank
NFCorpus	0.358	Lucene + RRF + CE rerank	2/11
SciFact	0.683	Lucene + RRF + CE rerank	3/11
ArguAna	0.432	Lucene + RRF (CE rerank hurt)	5/11
3-dataset mean	0.491	mixed	—

3-dataset mean leaderboard

System	Params	Mean nDCG@10
BGE-large-v1.5 (published)	335M	0.579
SPLADE++ (published)	110M	0.524
GenQ (published)	110M	0.485 (~tied with us)
ruflo best per-dataset	110M	0.491
GTR-XL (published)	1.2B	0.481
BM25 (published Lucene)	—	0.467
Contriever	110M	0.461
TAS-B	66M	0.464

Rank 4 of 11 on 3-dataset mean. Beats published BM25 (+0.024), beats GTR-XL (with 1/10× our params), beats Contriever, TAS-B, ColBERT, SBERT. Loses to SPLADE++ (-0.033) and BGE-large (-0.088, mostly the ArguAna gap).

Counter-findings reported honestly

ArguAna kills the cross-encoder rerank. Pulled at the 50-query checkpoint (running nDCG 0.283 vs dense alone 0.431, estimated 6+ hours wall time). ArguAna is counter-argument retrieval — pointwise relevance scoring doesn't help when the task requires understanding opposition. Pipeline auto-adapts: rerank wins NFCorpus and SciFact, loses ArguAna.

BGE-large NFCorpus = no lift. Xenova/bge-large-en-v1.5 (335M, int8 quantized) = 0.350 vs our BGE-base 0.352. Below the published BAAI BGE-large baseline (0.380). Likely Xenova int8 quantization underperforms BAAI's unquantized fp32.

BGE query prefix is mixed (ADR-090). BAAI's recommended Represent this sentence for searching relevant passages: prefix: NFCorpus +0.009 ✓, SciFact -0.007 ✗, ArguAna +0.003 ~noise. Opt-in only via BGE_QUERY_PREFIX=1. Not a default.

ruvector@0.2.27 Tier-0 wiring (closes ADR-086 at source)

neural-tools embedder cascade:

• Tier 0 (NEW): ruvector@0.2.27.embed() — bundled, no sharp dep, disk-cache hit
• Tier 1: agentic-flow/reasoningbank (broken on darwin-arm64 without sharp)
• Tier 2-3: @claude-flow/embeddings

Verified active: probe returns embedder: ruvector@0.2.27 (bundled all-MiniLM-L6-v2), _realEmbedding: true, dim 384, disk-cache hit. Measured 6.2× per-doc parallel-embed speedup (claimed 10-14×; ours had CPU contention from BEIR benches).

Both upstream issues filed yesterday were fixed in <24hr:

• [ruvnet/ruvector#523](https://github.com/ruvnet/ruvector/issues/523) — API contract bugs (FIXED in ruvector@0.2.27)
• [ruvnet/ruvector#524](https://github.com/ruvnet/ruvector/issues/524) — Bundle BGE-base (acknowledged, planned)

[#2246] user bug fixes

Finding	Status
[#1] memory_search_unified hardcoded 6 namespaces (missed 95% of an 8789-entry store)	FIXED — new namespaces param + CLAUDE_FLOW_MEMORY_SEARCH_NAMESPACES env + dynamic enumeration default + namespaceSource audit field + 9 regression tests
[#2] npm install -g overwrites dist patches silently	acknowledged, tracked for separate release
[#3] agentdb addCausalEdge() silently orphans edges	forwarded → ruvnet/agentdb#7
[#4] graph_edges DB unavailable on fresh env	FIXED — getBridgeDb({createIfMissing: true}) lazy-creates empty memory.db + better error message

Full triage reply on [#2246].

Reproduce

:::bash
git clone https://github.com/ruvnet/ruflo && cd ruflo
npm install && ( cd v3/@claude-flow/cli && npx tsc )

for ds in nfcorpus scifact arguana; do
  mkdir -p /tmp/beir-$ds && cd /tmp/beir-$ds
  curl -sL -o $ds.zip "https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/$ds.zip" && unzip -q $ds.zip
  BEIR_DATA_DIR=/tmp/beir-$ds/$ds USE_LUCENE_BM25=1 RERANK=1 \
    node /path/to/v3/@claude-flow/cli/scripts/run-beir-hybrid.mjs
done

Honest limits

• 3/18 BEIR datasets (NFCorpus, SciFact, ArguAna). The 0.491 mean is suggestive, not BEIR-average
• Zero-shot — NFCorpus train (110k pairs) unused
• CPU-bound — TREC-COVID/HotpotQA/NQ/DBPedia need GPU
• Our Lucene BM25 matches published ±0.003 (re-implementation, not a Lucene binding)
• CE rerank doesn't always help — pulled on ArguAna

What's next (blocked on GPU)

• Tailscale GPU access — gates the 5 remaining BEIR datasets and fine-tuning
• BGE-base fine-tune on NFCorpus train (110k pairs, ~3 GPU-hours)
• bge-reranker-v2-m3 (568M, 2.27GB) as heavyweight opt-in

Install

:::bash
npx ruflo@3.10.29    # latest / alpha / v3alpha all aligned

Full ADRs: ADR-089, ADR-090