外观
论文arXiv 2611.00155今天
机器人基础模型故障归因:弥合概念解码与实际因果缺口
本文由 AI 根据公开内容整理,可能有误,请以原文为准:From Concepts to Actual Causes: Toward Closing the Concept-Malfunction Gap in Robot Foundation Models(arXiv 2611.00155)
核心内容
该立场论文聚焦机器人基础模型事后分析中的核心因果问题,即精准定位导致特定故障的内部组件及其影响程度。作者指出,尽管机械可解释性(MI)能解码策略学习到的概念,但仅知概念编码不足以识别具体事故的实际成因,这种“概念-故障缺口”阻碍了可靠的故障诊断。论文主张将故障隔离纳入实际因果性框架进行形式化分析,以弥补现有MI方法在特定实例归因上的理论与实践短板。
技术方案上,论文将故障隔离查询形式化为包含被干预内部组件、反事实值、应急条件及读出结果的元组,并确立了查询良构的必要条件。分析揭示当前MI方法因假设固定应急条件且仅读取即时效果,导致备份机制掩盖实际成因,甚至在简单案例中产生冲突判决。基于此良构条件,文章提出了四项开放研究任务,旨在构建能够穿透概念表象、精准锚定实际故障根源的新一代可解释性方法论。
关键看点
- 故障隔离形式化:将查询定义为含干预组件、反事实值、应急条件与读出结果的元组,确立良构条件以规范归因逻辑
- MI方法局限性:指出现有机械可解释性技术因固定应急条件且仅关注即时效果,导致备份机制掩盖实际故障成因
- 概念故障缺口:阐明解码模型内部概念不等于识别特定事故的实际因果,需从通用声明转向实例级故障定位
- 开放研究任务:基于故障隔离良构条件提出四项研究方向,旨在弥合概念理解与实际故障归因之间的理论与实践鸿沟
工程与产业观察
随着具身大模型部署加速,黑盒故障归因成为安全落地瓶颈。该文将实际因果性引入MI领域,为算法工程师提供了超越特征可视化的故障诊断理论范式。其提出的形式化查询框架有助于区分模型“知道什么”与“为何出错”,对提升端到端策略的可信度与调试效率具有关键指导意义。
英文摘要 (Abstract)
Post-incident analysis of a robot foundation model poses a singular causal question: which internal components caused this failure, and to what degree? Mechanistic interpretability (MI), however, establishes general causal claims by decoding the concepts a policy has learned, yet knowing which concepts are encoded is insufficient for identifying the actual cause of a specific incident. In this position paper, we analyze this concept-malfunction gap by posing fault isolation in the framework of actual causality. Specifically, we formalize a fault-isolation query as a tuple of intervened internal components, counterfactual values, contingencies, and read-outs, and lay out conditions for it to be well-specified. Through this lens, we identify shortcomings of current MI approaches: they assume fixed contingencies and read out only immediate effects, letting backups obscure actual causes. We demonstrate on a toy example that these shortcomings can lead to conflicting verdicts. Lastly, based on the well-specification conditions, we outline four open research tasks toward closing the gap.
作者:Gihyuk Ko
来源
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