English(EN) Stochastic CHAOS: Why Deterministic Inference Kills, and Distributional Variability Is the Heartbeat of Artifical Cognition

论文认为确定性LLM推理会扼杀认知和安全

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

一篇新论文反对在大型语言模型中使用确定性推理，认为它会阻碍模型对不确定性的建模能力，抑制涌现能力，并削弱安全对齐。作者提出了“Stochastic CHAOS”，将分布变异性视为一种可控信号。他们证明了确定性推理会低估模型的能力和脆弱性，掩盖涌现能力，降低推理能力，并隐藏尾部风险。 AI

影响提倡在LLM中使用随机推理，可能改进不确定性建模、涌现能力和安全对齐。

排序理由该集群包含一篇讨论LLM推理新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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Signal score: 16 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇讨论LLM推理新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv cs.AI TIER_1 English(EN) · Tanmay Joshi, Shourya Aggarwal, Anusa Saha, Aadi Pandey, Shreyash Dhoot, Vighnesh Rai, Raxit Goswami, Aman Chadha, Vinija Jain, Amitava Das · 2026-10-15 04:00

随机混沌：为何确定性推理致命，分布变异是人工智能的脉搏

arXiv:2601.07239v2 Announce Type: replace Abstract: Deterministic inference is a comforting ideal in classical software: the same program on the same input should always produce the same output. As large language models move into real-world deployment, this ideal has been importe…