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Self-Correcting RAG: Enhancing Faithfulness via MMKP Context Selection and NLI-Guided MCTS

Paper · 2026 · Shijia Xu, Zhou Wu, Xiaolong Jia et al.

Summary

Retrieval-augmented generation often struggles with complex, multi-hop questions. Retrieved passages overlap and waste the context window, and models still produce claims the evidence does not support. Self-Correcting RAG addresses both problems with two stages.

  1. Context selection. Retrieved chunks are clustered by embedding similarity. Choosing which chunks to keep is then cast as a multidimensional multiple-choice knapsack problem (MMKP), which packs the most informative, least redundant evidence into a fixed token budget instead of taking the greedy top-k.
  2. Answer generation. The answer is built by Monte Carlo Tree Search. A natural language inference model scores each reasoning branch, rewarding entailment and heavily penalizing contradiction with the selected context.

Across six open-domain, multi-hop QA and fact-verification benchmarks, the authors report higher accuracy and fewer hallucinations than strong agentic baselines such as CRAG. Human evaluation confirms the faithfulness gains. The work matters because it treats faithfulness as a search and optimization problem, trading extra inference compute for answers that can be traced to evidence. The main cost is higher latency.

Key points

  • The MMKP selector groups chunks whose cosine similarity exceeds a threshold τ and picks at most one item per group. Pareto-pruning keeps this step polynomial-time. A redundancy budget of about C_red ≈ 120 worked best.
  • The MCTS planner achieved its best results when each node expanded into k = 3 children, the tree was limited to a depth of 3, and the simulation count was increased. A strong asymmetric contradiction penalty (w_c = −2.0) was needed to stop the search from favouring plausible but hallucinated branches.
  • On a hard subset of HotpotQA and MuSiQue, human judges found 83.6% of answers correctly attributed. This is 19.3 points above CRAG. Scoring with the same NLI model gave a slightly optimistic 0.85 AP.
  • The default reward model is RoBERTa-large-mnli. Swapping in domain NLI models (BioLinkBERT-large for PubMedQA, Legal-RoBERTa-base for LegalBench) improved results in those domains. On LegalBench, the reported score rose from 54.8 to 60.2.
  • Code is available at github.com/xjiacs/Self-Correcting-RAG.
  • Limitations:
    • Tree search increases time-to-first-token.
    • "Faithfulness" means agreement with the retrieved text, not verified truth.
    • Strict redundancy filtering may drop minority viewpoints.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Queen Mary University of LondonLondon · United Kingdom57 works · 3,600 citations🎓Chongqing UniversityChongqing · China14 works · 318 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksComputer Vision and Pattern Recognition · 3,486 worksInformation Systems · 1,730 works

🧭 Topics 1Research subjects and languages the work is about.Reasoning · 1,685 works

Cite

Cite as Shijia Xu, Zhou Wu, Xiaolong Jia, Yu Wang, Kai Liu and April Xiaowen Dong. 2026. Self-Correcting RAG: Enhancing Faithfulness via MMKP Context Selection and NLI-Guided MCTS. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-acl.1052

@inproceedings{xu2026self,
  title = {{Self-Correcting RAG: Enhancing Faithfulness via MMKP Context Selection and NLI-Guided MCTS}},
  author = {Shijia Xu, Zhou Wu, Xiaolong Jia, Yu Wang, Kai Liu and April Xiaowen Dong},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1052},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1052}
}

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