Structurally-Bounded Agentic Graph Exploration for Evidence-Grounded Scholarly DeepSearch
Score
76提出有界可检查的深度搜索新框架，对比黑箱智能体优势明显。
方法描述清晰，实验设计完整，有基准和成本分析。
成本仅为三分之一且结果可解释，便于实际部署。
论文编号为2608，针对当前深度搜索热点，时效性强。
来自arXiv AI/CL/LG，虽未经同行评议但学术可信度较高。
Base 84.5 × T2 Source weight ×0.9 = 76
Curation line 70Selected
Why selected
It balances cost, interpretability, and performance with strong methodological novelty and validation, though generalizability across different domain corpora remains to be tested.
AI commentary
「What stands out most is that this paper makes 'inspectability' a core design goal, not an after-the-fact explanation. For research scenarios that require strict evidence tracing, this bounded approach may be more practical than chasing all-powerful agents. If you're building literature review tools or evaluating search agent reliability, it's worth examining the pruning and ranking details closely.」
Quick overview
A new arXiv paper presents 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 benchmark over a 500K-paper arXiv corpus, Crase outperforms deep research agents built on proprietary models by up to 3x recall@50 at roughly a third of the cost.
- Crase uses one-time query plus local graph expansion to avoid open-ended search loops, enabling bounded reasoning.
- By expanding the 1.5-hop citation neighborhood and pruning entailment-unsupported edges, it ensures evidence-grounded candidates.
- On LitSearch and other benchmarks, it outperforms proprietary deep research agents by up to 3x recall@50 at roughly one-third the cost.
Full text
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