English(EN) Why Conclusions Diverge from the Same Observations: Formalizing World-Model Non-Identifiability via an Inference

论文形式化解释了为何AI和人类从相同数据中得出不同结论

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-12 15:24

该论文提出了一个形式化框架，解释了为何个体或AI系统可以从相同的观察集中得出不同的结论。它提出了两个层面的不可识别性：由于推理设置不同而导致结论分歧，以及学习到的世界模型本身的分歧。作者定义了一个“推理画像”来模拟这些差异，并将该框架与深度表示学习的概念联系起来，以AI监管辩论作为案例研究。 AI

影响提供了一个理论视角来理解和潜在地缓解AI决策和人机交互中的分歧。

排序理由学术论文发布在arXiv上，详细介绍了理解推理分歧的新理论框架。[lever_c_demoted from research: ic=1 ai=1.0]

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本文如何被排名

Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 学术论文发布在arXiv上，详细介绍了理解推理分歧的新理论框架。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, safety, other; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: 148 days old; Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

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arXiv cs.AI TIER_1 English(EN) · Toru Takahashi · 2026-05-12 15:24

为何同一观察会得出不同结论：通过推理形式化世界模型不可辨识性

When people share the same documents and observations yet reach different conclusions, the disagreement often shifts into a judgment that the other party is cognitively defective, irrational, or acting in bad faith. This paper argues that such divergence is better described as a …