Most multi-hop RAG goes stale the moment your data changes, what about a training-free approach that skips the graph rebuild?

Most methods that get strong multi-hop answers (GraphRAG, HippoRAG, RAPTOR, trained retrievers) build a knowledge graph or fine-tune a retriever over the corpus. That's fine until the data changes — then you re-extract / rebuild / retrain before the new facts are usable. For a corpus that updates daily, that's a real cost.

MOTHRAG does the multi-hop reasoning at query time over a plain dense index instead. An update is just embed + append (one embedding call) — no graph reconstruction, no retraining — so it stays current as the corpus changes.

And dropping the graph doesn't cost accuracy. F1, Llama-3.3-70B reader, n=1000 each:

Competitor rows reproduced from HippoRAG2 (ICML 2025), Table 2. MOTHRAG is within ~0.7 avg F1 of the GPU-bound research frontier (a fine-tuned, GPU-served stack — not shown). (Fair note: graph-RAG systems like GraphRAG shine on small curated / sensemaking corpora — this is multi-hop factoid QA over changing data, a different regime.)

Deterministic by design: instead of a free-form agent loop it runs a small ensemble of reasoning arms (direct read, decomposition, an iterative grounding-driven arm) under a deterministic arbitrator, over a bridge retrieval substrate with multi-hop chain filtering. Every answer is proof-tree-structured, so you can audit why it answered. Measured ≈$0.018/query, ~44% cheaper at matched accuracy.

Open source, ~1 week old — genuinely after feedback and failure cases:

• pip install mothrag
• Code: https://github.com/juliangeymonat-jpg/mothrag
• Paper: https://doi.org/10.5281/zenodo.20668567
• Live demo (BYO free key): https://huggingface.co/spaces/JUBOX99/mothrag-demo