5arXiv cs.CL (Computation and Language)·
DynaKRAG: Learnable state-conditioned control policy for multi-hop RAG evidence acquisition
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.
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HKVM-RAG: Hypergraph key-value separation improves multi-hop retrieval-augmented generation
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.
UMG-RAG: Training-free hybrid retrieval with uncertainty-aware granularity fusion for long-document RAG
Researchers propose Uncertainty-aware Multi-Granularity RAG (UMG-RAG), a training-free hybrid retrieval framework that addresses the tension between large and fine-grained retrieval chunks in RAG pipelines. The system converts dense and sparse retriever scores across multiple chunk granularities into evidence distributions, estimates reliability via entropy, and fuses candidates using query-specific confidence signals. A variant called UMGP-RAG uses fine-grained hits to locate evidence while returning broader parent chunks for coherence. Experiments on QA benchmarks show improved generation quality with no changes to the underlying retriever or generator.
RAG-based policy learning formalized under causal inference framework with regret bounds
A new arXiv preprint proposes one-step and two-step methods for policy learning using retrieval-augmented generation, formulated under the potential outcome framework from causal inference. The two-step method connects action-specific vector search to nearest-neighbor matching, with regret decomposed into candidate-generation and within-candidate choice components. Theoretical bounds on regret are derived using prediction-error guarantees for nearest-neighbor estimators and transformers. The work bridges RAG methodology with formal causal inference theory, offering a principled treatment of action selection in RAG-based systems.
RAPS-DA: Regime-aware peer specialization for robust RAG under knowledge conflicts
A new arXiv preprint introduces RAPS-DA, a training framework for making RAG systems more robust when retrieved context conflicts with a model's parametric knowledge. The approach divides conflicts into three reliability regimes (Grounding, Arbitration, Resistance) and trains separate peer specialist models per regime from a shared base, using reverse-KL supervision and a dual-layer token selector to filter uninformative training signals. Peer specialists exist only during training, so the deployed student model requires no additional components at inference time. Experiments across five conflict scenarios and two out-of-distribution benchmarks show RAPS-DA outperforms prompting, decoding, fine-tuning, RL, and single-teacher baselines.
RASER: Recoverability-Aware Selective Escalation Router for Multi-Hop Question Answering
RASER introduces a family of lightweight routers that decide whether to escalate retrieval complexity for multi-hop QA without making additional LLM calls. Built on top of one-shot RAG using six derived features, RASER-2 and RASER-3 route queries to progressively more expensive retrieval strategies (PRUNE, IRCoT) only when needed. Across six LLMs and three benchmarks, the routers match SOTA F1 while consuming only 41-49% of the tokens required by always-escalating baselines.
RA-RFT: Retrieval-Augmented Reinforcement Fine-Tuning teaches LLMs to reason by analogy
Researchers propose Retrieval-Augmented Reinforcement Fine-Tuning (RA-RFT), a post-training framework that trains a retriever to rank contexts by expected reasoning benefit rather than semantic similarity, then fine-tunes a policy model via reinforcement learning using retrieved analogous demonstrations. The key insight is that reasoning-relevant retrieval surfaces complementary solution strategies rather than superficially similar problems. On mathematical reasoning benchmarks, RA-RFT improves AIME 2025 average@32 accuracy by 7.1 and 2.8 points over GRPO for Qwen3-1.7B and Qwen3-4B respectively, suggesting reasoning-aware retrieval is orthogonal to reward design and training curriculum improvements.
DocTrace: Structure-Aware On-Demand Hypergraph Memory for Long-Document QA
Researchers introduce DocTrace, a multi-agent RAG framework for long-document question answering that uses query-triggered knowledge organization rather than costly query-agnostic preprocessing. The system combines a lightweight document structural tree index, on-demand hypergraph working memory, and a graph-structured experience memory that stores successful reasoning plans for reuse. Evaluated on four long-document QA datasets, DocTrace outperforms the strongest baseline (ComoRAG) by up to 8.85% F1 and 4.40% EM while reducing computational cost by 53.32%.
MIRAGE: Training-free defense for RAG systems against misinformation in retrieved documents
Researchers introduce MIRAGE, a training-free, model-agnostic defense mechanism for long-form Retrieval-Augmented Generation systems exposed to polluted retrieval corpora. The system builds an NLI-based cross-document claim graph and uses a Defended-Claims Gate to either condition generation on consistently supported evidence or fall back to parametric answering. The authors also release a pollution benchmark protocol spanning four perturbation families and demonstrate that MIRAGE restores factuality across four long-form QA benchmarks where vanilla RAG degrades significantly. Code and datasets are publicly released.