English(EN) From Prompts to Posteriors: Can LLM Agents Perform Bayesian Inference in Context?

LLM通过代码执行展示出改进的贝叶斯推理能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-23 04:00

研究人员开发了一个新的评估框架，用于测试大型语言模型（LLM）的贝叶斯推理能力。该框架评估LLM在上下文中更新信念的能力，这是科学发现的一个关键方面。虽然目前的LLM经常违反贝叶斯法则，但研究发现，具备高级推理和代码执行能力的模型可以显著提高其性能，非常接近复杂的后验分布。这表明，具备适当工具的LLM可能很快通过实现复杂的信念更新来加速AI驱动的科学发现。 AI

影响具备代码执行能力的LLM可以近似复杂的后验分布，可能加速AI驱动的科学发现。

排序理由关于LLM能力的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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Signal score: 5 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 关于LLM能力的研究论文。[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, model release; 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: Same-day; Cluster formed today. Ranking reflects the current source set at time of score.

完整方法见我们的编辑标准。

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arXiv cs.AI TIER_1 English(EN) · Linus Filbry, Luckeciano C. Melo, Yarin Gal · 2026-10-23 04:00

From Prompts to Posteriors: Can LLM Agents Perform Bayesian Inference in Context?

arXiv:2610.29331v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly applied across the scientific discovery pipeline, from synthesizing literature to generating hypotheses to informing experimental design. A unifying principle behind these tasks is the n…

本文如何被排名

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From Prompts to Posteriors: Can LLM Agents Perform Bayesian Inference in Context?

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