English(EN) OpenDiscoveryTrace: Process Traces for Evaluating AI Scientist Workflows

新数据集OpenDiscoveryTrace追踪人工智能科学家推理过程

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-11 04:00

发布了一个名为OpenDiscoveryTrace的新数据集，包含558个详细的人工智能科学代理轨迹。该数据集捕获了模型的逐步推理过程，而不仅仅是最终输出，以便审计科学方法和诊断故障模式。它包括来自GPT-5.4、Claude Opus-4.6和Gemini-3.1 Pro等前沿模型的数据，以及Qwen2.5-7B和Mistral-7B-v0.3等开源模型的数据。使用该数据集进行的初步分析显示，模型在错误类型和频率上存在显著差异，而仅凭输出评估无法发现这些差异。 AI

影响能够更稳健地审计和理解人工智能的推理过程，从而可能改进人工智能治理和科学方法。

排序理由该集群包含一篇介绍用于评估人工智能科学家工作流的新数据集和新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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: 15 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇介绍用于评估人工智能科学家工作流的新数据集和新方法的论文。[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, product; 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: Breaking (< 6h); Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Aayam Bansal, Keertan Balaji · 2026-09-11 04:00

OpenDiscoveryTrace: 用于评估AI科学家工作流的进程追踪

arXiv:2609.09203v1 Announce Type: new Abstract: Existing benchmarks for autonomous AI scientists evaluate only final outputs---generated code, hypotheses, or papers---yet discard the reasoning process by which those outputs were obtained. This makes it impossible to audit scienti…

本文如何被排名

报道来源 [1]

OpenDiscoveryTrace: 用于评估AI科学家工作流的进程追踪

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