XDomainBench：诊断高维科学知识组合中的推理崩溃
XDomainBench: Diagnosing Reasoning Collapse in High-Dimensional Scientific Knowledge Composition
AI总结：
XDomainBench通过系统压力测试科学推理，揭示LLM在跨学科知识组合中的推理崩溃问题，发现领域组合导致直接难度增加和轨迹模式引发的误差累积是根本原因。
摘要：
大型语言模型（LLMs）越来越多地用于知识合成，但其在科学知识中的组合泛化能力仍不明确。现有基准主要关注单轮受限场景，无法捕捉现实交互科学工作流暴露的能力边界。为此，我们引入XDomainBench，一个交互跨学科科学推理的诊断基准。我们正式化组合顺序和混合结构，从单学科到跨学科进行系统压力测试，涵盖20个领域和4种任务类别，共8598个交互会话，8种真实轨迹模式覆盖难度和领域混合动态，模拟真实AI4S场景。大规模评估揭示随着组合顺序增加，LLM出现系统性推理崩溃，根源在于：（i）领域组合导致的直接难度增加；（ii）间接交互放大失败，轨迹模式触发误差累积、推理中断和领域混淆，最终导致会话崩溃。
英文摘要：
Large Language Models (LLMs) are increasingly deployed for knowledge synthesis, yet their capacity for compositional generalization in scientific knowledge remains under-characterized. Existing benchmarks primarily focus on single-turn restricted scenarios, failing to capture the capability boundaries exposed by real-world interactive scientific workflows. To address this, we introduce XDomainBench, a diagnostic benchmark for interactive interdisciplinary scientific reasoning. We formalize the composition order and mixture structure to enable systematic stress-testing from single-discipline to inter-disciplinary, comprising 8,598 interactive sessions across 20 domains and 4 task categories, with 8 realistic trajectory patterns covering difficulty and domain-mixture dynamics, simulating real AI4S scenarios. Large-scale evaluation of LLMs reveals a systematic reasoning collapse as composition order increases, stemming from two root causes: (i) direct difficulty increases induced by domain composition, and (ii) indirect interaction-amplified failures where trajectory patterns trigger error accumulation, reasoning breaks, and domain confusion, ultimately leading to session collapse.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
