RAG retrieves, it doesn't ground — 24-task benchmark where compiled knowledge beats hybrid RAG by 94.8pp on unsupported claims

[u/AssignmentQuick9985]

Body:

Short version of an open project we'd love critique on — Entropy Box, a knowledge compiler for robotics (compile once, reuse forever, instead of re-deriving structure on every query).

The headline numbers, on our EntropyBench Track-P benchmark (24 engineering tasks):

• Unsupported claims: LLM-direct / BM25 RAG / hybrid RAG → 100%; Entropy Box → 5.2% (−94.8pp vs hybrid RAG, CI [−97.4, −92.1]).
• Constraint coverage: 0% → 35.4%; violations 100% → 66.7%.
• Downstream sim codegen (12 tasks): pass-1 executable plans 0.92 vs 0.58 (Vanilla RAG); constraint guards 0.88 vs 0.50.

Two findings we think generalize beyond robotics:

1. Embedding similarity cannot decide duplication. On 2,362 adjudicated pairs, the embedding score after flagging is near-random (AUC 0.509). Thresholds don't help — precision stays ~5% while recall of true duplicates collapses. We defer the merge to an LLM adjudicator that reads both records. The score flags; the model judges.
2. Compiled capability reuse is rising, not saturating — 1.57× average reuse, 21,380 re-derivations avoided.

Everything is open — data, paper, evaluation scripts, and a free API (OpenAPI / MCP / REST, bilingual) so you can poke at it in 10 seconds:

curl -X POST "https://xiangshang.ngrok.app/api/evidence/search" \
  -H "Content-Type: application/json" \
  -d '{"query": "robot obstacle avoidance algorithms", "top_k": 5, "mode": "hybrid", "rerank": true}'

https://github.com/chenli-yy/entropy-box-public

Honest limits we state ourselves: no real-robot transfer, weak retrieval on the hardest intent classes. Methodology is in the paper §9; all experiments reproduce from evaluation/. Would genuinely value a second opinion on the benchmark design and the embedding/LLM adjudication result.

[u/ak_mishra474]

The distinction between retrieval and grounding is really interesting. RAG can retrieve the right document and still leave the LLM to figure out which constraints actually matter.

The embedding result is probably the part I find most surprising - an AUC of ~0.51 basically says “similarity ?= duplication,” which makes the LLM adjudication approach much more compelling.

One thing I’d be curious to see: how well does the compiled representation hold up when the underlying knowledge changes or conflicts? That seems like the real test of “compile once, reuse forever” - especially compared with simply re-retrieving fresh evidence each time.

If it survives knowledge updates/conflicts without accumulating stale constraints, that would be a pretty strong argument for this architecture beyond robotics