We present EviSearch, a multi-agent extraction system that automates the creation of ontology-aligned clinical evidence tables directly from native trial PDFs while guaranteeing per-cell provenance for audit and human verification. EviSearch pairs a PDF-query agent (which preserves rendered layout and figures) with a retrieval-guided search agent and a reconciliation module that forces page-level verification when agents disagree. The pipeline is designed for high-precision extraction across multimodal evidence sources (text, tables, figures) and for generating reviewer-actionable provenance that clinicians can inspect and correct. On a clinician-curated benchmark of oncology trial papers, EviSearch substantially improves extraction accuracy relative to strong parsed-text baselines while providing comprehensive attribution coverage. By logging reconciler decisions and reviewer edits, the system produces structured preference and supervision signals that bootstrap iterative model improvement. EviSearch is intended to accelerate living systematic review workflows, reduce manual curation burden, and provide a safe, auditable path for integrating LLM-based extraction into evidence synthesis pipelines.

翻译：我们提出EviSearch——一个多智能体提取系统，可自动从原生试验PDF直接生成符合本体的临床证据表格，同时保证每个单元格的可溯源审计性与人工验证能力。该系统通过PDF查询智能体（保留渲染布局与图表）、检索引导智能体以及冲突协调模块（在智能体产生分歧时强制进行页面级验证）协同工作。该管道专为多模态证据源（文本、表格、图表）的高精度提取设计，并能生成可供审稿人检查修正的溯源记录。在临床医生标注的肿瘤学试验论文基准测试中，EviSearch相比强解析文本基线显著提升提取准确率，同时提供全面的归因覆盖。通过记录协调器决策与审稿人修正，系统生成结构化的偏好与监督信号，用于引导模型的迭代改进。EviSearch旨在加速动态系统综述工作流、降低人工筛选负担，并为基于大语言模型的证据提取安全、可审计地集成至证据合成管道提供可行路径。