2wikimultihopqa-20784650·3 events·first seen Aliases: 2WikiMultiHopQA
A new arXiv preprint proposes two adapter families — MaLoRA (token-level dynamic scaling via Mamba recurrence) and MaRA (context-level segment retrieval via cross-segment state tracking) — as improvements over standard LoRA for language model reasoning. Evaluated on three frozen backbones (Qwen-2.5-7B, Llama-3.1-8B, Gemma-2-9B) and two multi-hop QA benchmarks (MuSiQue, 2WikiMultihopQA), the methods yield average gains of +6.8 F1 (+10.5% relative) over LoRA, with up to +18.2% relative improvement on the hardest configuration. Token-level gains also transfer to RULER QA-2 under length-stress conditions.
DynaKRAG is a new framework that formulates multi-hop retrieval-augmented generation as a state-conditioned control problem over atomic evidence operations (iterative retrieval, query reformulation, sufficiency judging, etc.), using a learned controller to select among valid operations at each step. Evaluated with Qwen2.5-7B-Instruct, it achieves F1 scores of 0.5998 on HotpotQA, 0.5340 on 2WikiMultiHopQA, and 0.3061 on MuSiQue, outperforming the strongest baselines on all three benchmarks. Ablations show that replacing the learned controller with a uniform policy costs 3.96–5.78 F1 points, and that additional retrieval is not uniformly beneficial.
A new arXiv preprint introduces HKVM-RAG, an evidence-organization layer for multi-hop RAG that uses weighted hyperedges as retrieval keys while retaining passage text as answer values. Under a fixed-substrate protocol controlling for tuple cache, reader, and evaluation budget, the hypergraph key-value approach improves over KG-PPR by +3.4 F1 on 2WikiMultiHopQA and +3.6 F1 on MuSiQue. A dense-aware controller combining frozen ColBERTv2 with HKVM features reaches 88.8, 65.1, and 85.8 F1 on three benchmarks, outperforming ColBERTv2 alone by 5–11 F1 points. The work positions hypergraph organization as a reusable evidence-control mechanism rather than a dense-retrieval replacement.