Structurally-bounded Agentic Graph Exploration for Evidence-Grounded Scholarly DeepSearch

Abstract:We present Crase, a bounded and inspectable alternative to deep research agents for scholarly search. Instead of an open-ended search loop, Crase queries a search engine once for seed papers, expands them along their 1.5-hop citation neighborhood, prunes citation edges whose claims lack entailment support, and ranks the remaining papers with a recency-aware random walk. This makes the candidate set, the reason each paper is kept, and the stopping condition explicit and fixed before inference. On LitSearch and one further benchmarks over a 500K-paper arXiv corpus, Crase outperforms deep research agents built on proprietary models by up to 3$\times$ recall@50 at roughly a third of the cost.

Computation and Language (cs.CL); Information Retrieval (cs.IR)

arXiv:2608.24809 [cs.CL]

(or arXiv:2608.24809v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.24809 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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