SLIDERS：基于自主数据库调和的证据合成方法，用于系统性综述

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Abstract:Systematic reviews requires comprehensive evidence synthesis from large document corpora to answer targeted research questions and are foundational in finance, social sciences, and other technical fields. Manual construction of evidence tables is labor-intensive, and recent LLM-based assistants relying on embedding or keyword search often fail to meet the coverage standards of systematic reviews. We introduce SLIDERS, a novel LLM-based methodology for automatically assembling evidence tables tailored to research questions for systematic reviews. SLIDERS extracts full-text excerpts that serve as direct evidence or as provenance for structured data. Core to SLIDERS is an autonomous evidence reconciliation coding agent that iteratively writes SQL to analyze and reconcile extracted evidence, bringing together information fragmented across documents, resolving inconsistencies and removing duplicates across records. We evaluate SLIDERS on BrowseComp+, achieving 90% accuracy, and show that it outperforms all baselines on additional benchmarks within the LLM's context window. To assess scalability, we introduce two new evidence-synthesis benchmarks, WikiCeleb100 (3.9M tokens) and FinQ100 (36M tokens), which include follow-up questions beyond the original research query. Although SLIDERS synthesizes evidence knowing only the original question, it answers 78.9% and 55.2% of all questions correctly on WikiCeleb100 and FinQ100, respectively

Comments:	62 pages (10 main), NeurIPS 2026
Subjects:	Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as:	arXiv:2604.22294 [cs.CL]
	(or arXiv:2604.22294v3 [cs.CL] for this version)
	https://doi.org/10.48550/arXiv.2604.22294 arXiv-issued DOI via DataCite

Submission history

From: Harshit Joshi [view email]
[v1] Fri, 24 Apr 2026 07:16:44 UTC (1,284 KB)
[v2] Thu, 9 Jul 2026 20:36:49 UTC (708 KB)
[v3] Wed, 11 Nov 2026 09:03:00 UTC (648 KB)