KAIST's Chain of Retrieval Beats Standard Paper Search by 7.82%
A new framework retrieves full scientific papers by decomposing them into aspect-specific queries and iteratively expanding the search as a tree.
- Chain of Retrieval (CoR) decomposes query papers into motivation, method, and experiment queries for retrieval.
- Iterative tree expansion promotes top-ranked papers as new queries, aggregated bottom-up via Reciprocal Rank Fusion.
- Beats abstract-to-abstract retrieval by 6.37% Recall and full-to-full by 7.82% on average.
- New SciFullBench benchmark provides full-paper queries and candidates from ICLR, NeurIPS, ACL, EMNLP.
- Generalizes to patent-to-patent retrieval with off-the-shelf GPT-4o, no training needed.
- Code and dataset on GitHub; latency remains the main cost.
Finding papers related to a complete manuscript presents a different retrieval problem from keyword search. The query may span thousands of tokens across motivation, methods, experiments, and results, while relevance may depend on a single facet. KAIST researchers propose Chain of Retrieval (CoR), a framework that searches full-paper content through aspect-specific queries, recursive expansion, and bottom-up rank fusion. On the new SciFullBench benchmark, CoR reported higher recall than abstract-only and full-document baselines, with a substantial latency cost.
One vector hides the match
Many scientific retrievers represent each paper with its title and abstract, then fine-tune an embedding model on citation-linked pairs. That representation captures broad topical similarity, yet abstracts often omit the implementation details, datasets, baselines, and evaluation choices needed to find methodological or experimental neighbors.
Long-context encoders can accept more of the manuscript, although context limits may still exclude parts of long documents. A single output embedding also compresses motivation, methods, and experiments into one vector. Signals tied to a particular dataset or technique can become difficult to distinguish from the paper’s broader topic.
Four views start the search
CoR’s Exploration phase derives three focused queries from the manuscript and retains the abstract as a broader fourth view:
- Research motivation: the problem, rationale, and research objective.
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