PlanRAG models exploratory reasoning problems as logical query trees, uses dynamic programming with a cost model to build them, and executes iterative retrieval-generation over the trees, outperforming prior RAG methods on the new WikiWeb-ERP dataset.
Enhancing retrieval-augmented large language models with iterative retrieval-generation synergy
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GDP-RAG targets only information deltas in multi-hop RAG through preliminary grounding, gap-conditioned prompts, and skeletal trajectories, reaching 60.63% accuracy at 0.51 cost-of-pass on HotpotQA, 2WikiMultiHopQA, and MuSiQue.
Large-scale evaluation shows retrieval-augmented generation yields only marginal and inconsistent gains (1-2 points) over no-retrieval baselines in biomedical QA, with model choice dominating retriever or corpus effects.
RASER routers built on one-shot RAG features selectively escalate retrieval, matching SOTA F1 scores on multi-hop QA while using 41-49% of the tokens required by always-prune across six LLMs and three benchmarks.
SPADER proposes step-wise peer advantage and diversity-aware exploration rewards in RL for multi-answer QA, reporting improved recall and F1 on QAMPARI, Mintaka, WebQSP, and QUEST.
Verbal-R3 uses a verbal reranker to generate analytic narratives that guide retrieval and reasoning in LLMs, achieving SOTA results on complex QA benchmarks.
PMSR progressively constructs structured reasoning trajectories with dual-scope queries and compositional reasoning to improve knowledge acquisition and answer accuracy in knowledge-intensive VQA.
A RAG-enhanced LLM pipeline with segmentation improves C-to-Rust transpilation correctness and eliminates raw pointer dereferences and unsafe type casts in several Coreutils programs.
Mujica-MyGo decomposes multi-turn RAG interactions via multi-agent workflows and applies minimalist policy gradient optimization to improve performance on QA benchmarks while avoiding long-context problems.
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Progressive Multimodal Search and Reasoning for Knowledge-Intensive Visual Question Answering
PMSR progressively constructs structured reasoning trajectories with dual-scope queries and compositional reasoning to improve knowledge acquisition and answer accuracy in knowledge-intensive VQA.
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Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning
Mujica-MyGo decomposes multi-turn RAG interactions via multi-agent workflows and applies minimalist policy gradient optimization to improve performance on QA benchmarks while avoiding long-context problems.