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English(EN) Uncovering Why LLMs Fail: From Internal Mechanisms to Adversarial Reasoning Trajectories

新框架CauTrace诊断LLM推理失败

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一个名为CauTrace的新诊断框架，用于分析大型语言模型（LLM）在其推理过程中失败的原因。该框架检查七个功能状态、表示变化在Transformer块中的传播以及时间状态协调，以表征推理轨迹。CauTrace在Qwen和LLaMA等模型的标准和对抗性失败上进行了评估，揭示了失败的推理表现出较弱的Attention-MLP一致性和较少的验证活动。研究还表明，CauTrace可以为模型修复和引导提供操作信号，为理解LLM失败动态提供了一种统一的方法。 AI

影响 提供了一种理解和潜在改进LLM可靠性和安全性新方法。

排序理由 该集群包含一篇详细介绍LLM新诊断框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新框架CauTrace诊断LLM推理失败

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该集群包含一篇详细介绍LLM新诊断框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Zirui Ding (Institute of Computing Technology, Chinese Academy of Sciences, University of Chinese Academy of Sciences), Xinxin Fan (University of Chinese Academy of Sciences, State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese A… ·

    揭秘大型语言模型为何失败：从内部机制到对抗性推理轨迹

    arXiv:2610.37369v1 Announce Type: new Abstract: Large language models (LLMs) are usually evaluated by final-output correctness or safety, yet an endpoint label does not reveal how a failure develops throughout the reasoning process. We therefore ask whether ordinary reasoning fai…

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