发表机构
Yonsei University（延世大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对智能体搜索中候选集优化与文本检查耦合的问题，提出索引原生接口IndexAct，通过持久化候选集和状态反馈分离二者，在五个基准上超越基线，并提升证据覆盖率与可扩展性。
AI 中文摘要
智能体搜索的最新进展使大语言模型（LLM）智能体对语料库探索拥有了更精细的控制。然而，即使候选集的反馈足以用于下一步决策，搜索接口也常常返回匹配的段落，从而将候选集优化与源文本暴露耦合在一起。我们提出了IndexAct，一种用于索引原生语料库交互的接口，它将候选集优化与文本检查分离开来。智能体通过词法条件和基于倒排索引的集合操作来构建和操作持久化的候选集，接收可复用的状态引用和统计信息（如候选数量），而非匹配段落。这种反馈指导进一步的优化，而单独请求的段落则提供新的线索或证据，这些线索或证据可为后续对保留候选集的操作提供信息。在涵盖智能体搜索和多跳问答的五个基准上的实验表明，IndexAct在每个基准上都优于所评估的基线方法。在BrowseComp-Plus上，与基于终端的语料库接口相比，它还以更小的平均实时上下文实现了更高的证据覆盖率，并在语料库扩展时保持了答案准确性。进一步的分析表明，信息丰富的优化反馈和状态复用支持持续的证据发现，而较短的上下文或较少的搜索步骤本身并不能确保更好的性能。
英文摘要
Recent advances in agentic search have given large language model (LLM) agents finer control over corpus exploration. However, search interfaces often return matching passages even when feedback about the candidate set would suffice for the next decision, coupling candidate refinement with source-text exposure. We propose IndexAct, an interface for Index-Native Corpus Interaction that separates candidate-set refinement from text inspection. Agents construct and manipulate persistent candidate sets through lexical conditions and set operations over an inverted index, receiving reusable state references and statistics such as candidate counts rather than matching passages. This feedback guides further refinement, while separately requested passages provide new clues or evidence that can inform subsequent operations on retained candidate sets. Experiments on five benchmarks spanning agentic search and multi-hop question answering show that IndexAct outperforms the evaluated baselines on each benchmark. On BrowseComp-Plus, it also achieves higher evidence coverage with a smaller average live context than terminal-based corpus interfaces, and maintains answer accuracy as the corpus expands. Further analyses suggest that informative refinement feedback and state reuse support continued evidence discovery, while shorter contexts or fewer search steps alone do not ensure better performance.
CommentsWork in Progress
