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Test-time Corpus Feedback: From Retrieval to RAG

Paper · 2026 · Mandeep Rathee, Venktesh V, Sean MacAvaney et al.

Summary

Most retrieval-augmented generation (RAG) pipelines fetch documents once with a fixed retriever, such as BM25 or a dense dual encoder, and then generate an answer. This one-shot design struggles on tasks that need iterative evidence gathering, such as multi-hop QA, claim verification and procedural questions.

This survey, by Rathee, V, MacAvaney and Anand (Findings of EACL 2026), reviews methods that adapt retrieval at inference time. They do so using automatic signals grounded in the corpus, which the authors call test-time corpus feedback. Unlike classic Rocchio-style relevance feedback, these signals come from rankers, verifiers or LLM confidence rather than from explicit user judgments.

The authors organise the literature by where feedback is applied: the query, the retrieved document pool, or the generation loop. They also map the relevant benchmarks and evaluation metrics. As a survey, the paper reports no new experiments. Its contribution is a structured taxonomy that connects IR and NLP work and treats retrieval as a dynamic, learnable part of RAG. This framing is useful when designing retrieval for harder or lower-resource settings.

Key points

  • Query-level feedback covers expansion, rewriting, query-vector adaptation and decomposition. It is grouped into three families:
    • lexical PRF (Rocchio, Relevance Modeling, LCE);
    • semantic PRF (ANCE PRF, ColBERT PRF);
    • generative feedback (GRF, GAR, LameR, MILL, DeepRetrieval).
  • Retrieval-level feedback uses rankers or corpus graphs to revise the document pool. Examples are GraphAR, LADR, QUAM, LexBoost, SlideGAR, ReFIT and TOUR.
  • Generation-time feedback decides when and what to retrieve. It is grouped into four families:
    • rule-based methods (SKR, IRCoT, Adaptive RAG);
    • signal-triggered methods (FLARE, DRAGIN, SeaKR, CRAG);
    • prompt-based methods (Self-Ask, ReAct, MCTS-RAG);
    • learned or agentic methods (Self-RAG, Search-R1, R1-Searcher, ZeroSearch).
  • Benchmarks discussed include FEVER, HoVeR, QuanTemp and AveriTeC for fact verification, plus BRIGHT and BrowseComp-Plus for reasoning and Deep Research tasks.
  • Open problems and scope:
    • Open problems are computational cost, noisy corpus feedback, missing feedback-aware decision policies, and weak evaluation of multi-round feedback.
    • The survey excludes training-time feedback, corpus-free self-refinement, multimodal RAG and knowledge graphs.

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

🎓Stockholm UniversityStockholm · Sweden21 works · 1,832 citations🎓University of GlasgowGlasgow · United Kingdom11 works · 635 citations🎓Delft University of TechnologyDelft · Netherlands6 works · 290 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksInformation Systems · 1,437 works

🧭 Topics 1Research subjects and languages the work is about.Information retrieval & search · 473 works

Cite

Cite as Mandeep Rathee, Venktesh V, Sean MacAvaney and Avishek Anand. 2026. Test-time Corpus Feedback: From Retrieval to RAG. Findings of the Association for Computational Linguistics: EACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-eacl.298

@inproceedings{rathee2026test,
  title = {{Test-time Corpus Feedback: From Retrieval to RAG}},
  author = {Mandeep Rathee, Venktesh V, Sean MacAvaney and Avishek Anand},
  booktitle = {Findings of the Association for Computational Linguistics: EACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-eacl.298},
  url = {https://doi.org/10.18653/v1/2026.findings-eacl.298}
}

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