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New dataset OpenDiscoveryTrace tracks AI scientist reasoning processes

By PulseAugur Editorial · [1 sources] ·

A new dataset called OpenDiscoveryTrace has been released, containing 558 detailed AI scientific agent trajectories. This dataset captures the step-by-step reasoning processes of models, not just their final outputs, to enable auditing of scientific methodology and diagnosis of failure modes. It includes data from frontier models like GPT-5.4, Claude Opus-4.6, and Gemini-3.1 Pro, as well as open-weight models such as Qwen2.5-7B and Mistral-7B-v0.3. Initial analysis using the dataset revealed significant differences in error types and frequencies between models, which were not apparent in output-only evaluations. AI

IMPACT Enables more robust auditing and understanding of AI reasoning, potentially improving AI governance and scientific methodology.

RANK_REASON The cluster contains a research paper introducing a new dataset and methodology for evaluating AI scientist workflows. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New dataset OpenDiscoveryTrace tracks AI scientist reasoning processes

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The cluster contains a research paper introducing a new dataset and methodology for evaluating AI scientist workflows. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Aayam Bansal, Keertan Balaji ·

    OpenDiscoveryTrace: Process Traces for Evaluating AI Scientist Workflows

    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…

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