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Plan*RAG: Efficient Test-Time Planning for Retrieval Augmented Generation

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arxiv 2410.20753 v2 pith:YZPKBXB7 submitted 2024-10-28 cs.CL cs.LG

Prakhar Verma , Sukruta Prakash Midigeshi , Gaurav Sinha , Arno Solin , Nagarajan Natarajan , Amit Sharma This is my paper · ORCID
classification cs.CLcs.LG
keywords planreasoninggenerationcontextexecutionexistingmethodsmulti-hop
verification ladder T0 review T1 audit T2 compute T3 formal
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We introduce Plan*RAG, a novel framework that enables structured multi-hop reasoning in retrieval-augmented generation (RAG) through test-time reasoning plan generation. While existing approaches such as ReAct maintain reasoning chains within the language model's context window, we observe that this often leads to plan fragmentation and execution failures. Our key insight is that by isolating the reasoning plan as a directed acyclic graph (DAG) outside the LM's working memory, we can enable (1) systematic exploration of reasoning paths, (2) atomic subqueries enabling precise retrievals and grounding, and (3) efficiency through parallel execution and bounded context window utilization. Moreover, Plan*RAG's modular design allows it to be integrated with existing RAG methods, thus providing a practical solution to improve current RAG systems. On standard multi-hop reasoning benchmarks, Plan*RAG consistently achieves improvements over recently proposed methods such as RQ-RAG and Self-RAG, while maintaining comparable computational costs.

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Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. KAMR: Grounding Generation via Knowledge-Aligned Multi-hop Retrieval

    cs.IR 2026-07 conditional novelty 6.0 of 10

    Partial-alignment contrastive pretraining plus anchor-then-expand graph retrieval improves multi-hop KG evidence recovery and downstream QA over strong dense and graph RAG baselines.

  2. Fishing Out Free Riders: Shapley-Based Reward Attribution for Parallel Reasoning via Reinforcement Learning

    cs.AI 2026-07 conditional novelty 6.0 of 10

    Rewarding each parallel reasoning path by Monte-Carlo-Shapley marginal contribution, scored by a generative reward model, lifts Pass@16 on AIME24/AIME25/AMC23 by 4-90% relative over Parallel-R1 with a fifth of the tra...

  3. TeaRAG: A Token-Efficient Agentic Retrieval-Augmented Generation Framework

    cs.IR 2025-11 conditional novelty 6.0 of 10

    TeaRAG shows that hybrid chunk+triplet retrieval with Personalized PageRank and an iterative process-aware DPO reward keeps QA accuracy while cutting reasoning tokens by roughly 60%.

  4. SearchOS-V1: Towards Robust Open-Domain Information-Seeking Agent Collaboration

    cs.AI 2026-07 conditional novelty 5.0 of 10

    A multi-agent web-search framework that stores progress in shared evidence, coverage, and failure state reports the best F1 scores among compared baselines on WideSearch (80.3 item F1) and GISA (76.5 set F1).

  5. ComposeRAG: A Modular and Composable RAG for Corpus-Grounded Multi-Hop Question Answering

    cs.CL 2025-05 conditional novelty 5.0 of 10

    A modular, verifier-driven RAG pipeline with iterative re-decomposition outperforms fine-tuned and agentic baselines on four multi-hop QA benchmarks.

  6. Single LLM, Multiple Roles: A Unified Retrieval-Augmented Generation Framework Using Role-Specific Token Optimization

    cs.CL 2025-05 reject novelty 5.0 of 10

    RoleRAG tunes only role-token embeddings on a frozen LLM to run six RAG sub-tasks, reporting improved QA accuracy, but with inconsistent headline numbers and no significance tests.

  7. Leveraging LLM-Assisted Query Understanding for Live Retrieval-Augmented Generation

    cs.CL 2025-06 conditional novelty 4.0 of 10

    Omni-RAG, a query-rewriting and decomposition pipeline on top of standard retrieval and reranking, achieved rank 2 in the SIGIR 2025 LiveRAG Challenge.

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