首个跨领域科学假说生成数据集，带可验证的推理链。
CrossTrace: A Cross-Domain Dataset of Grounded Scientific Reasoning Traces for Hypothesis Generation
- 构建1389条跨领域的科学推理链，每步均源自论文原文。
- 微调后模型在假说质量与结构合规性上显著提升，跨领域训练更优。
- 适合研究假说生成、AI科研助手及可解释推理的学者使用。
科学假说生成是加速科研的关键瓶颈，但现有数据集多局限于单一领域，且缺乏连接已有知识与新发现的显式推理链条。本文提出CrossTrace，一个包含1,389条跨领域科学推理链的数据集，涵盖生物医学（518条）、人工智能/机器学习（605条）和跨领域工作（266条）。每条推理链系统地呈现从既有知识经由中间逻辑步骤到新假说的完整过程，每个步骤均基于原始论文文本进行锚定。本文定义了输入/推理链/输出的范式，扩展了HypoGen的位翻转火花框架，引入八种发现模式分类法及多领域覆盖。使用QLoRA对Qwen2.5-7B-Instruct在CrossTrace上进行微调，结果显著优于未微调基线：在GPT-4o评分下IAScore从0.828升至0.968，在Claude Opus 4.5评分下从0.716升至0.888；结构合规率从0%提升至100%；火花相似度从0.221增至0.620。平衡的跨领域训练（生物医学+AI/ML+CS）表现优于单领域训练，表明科学推理模式具有跨学科迁移能力。对150条分层抽样样本的人工验证显示，步骤级锚定准确率达99.7%，无一例虚构。据笔者所知，CrossTrace是首个大规模、跨领域的、具备步骤级锚定推理链的假说生成数据集，结果证明此类推理链是有效且至少部分具备领域通用性的训练信号。
原文摘要 · Abstract (English)
Scientific hypothesis generation is a critical bottleneck in accelerating research, yet existing datasets for training and evaluating hypothesis-generating models are limited to single domains and lack explicit reasoning traces connecting prior knowledge to novel contributions. I introduce CrossTrace, a dataset of 1,389 grounded scientific reasoning traces spanning biomedical research (518), AI/ML (605), and cross-domain work (266). Each trace captures the structured reasoning chain from established knowledge through intermediate logical steps to a novel hypothesis, with every step grounded in source paper text. I define an Input/Trace/Output schema that extends the Bit-Flip-Spark framework of HypoGen with step-level verification, a taxonomy of eight discovery patterns, and multi-domain coverage. Fine-tuning Qwen2.5-7B-Instruct on CrossTrace via QLoRA yields substantial improvements over the untuned baseline: IAScore rises from 0.828 to 0.968 (GPT-4o judge) and from 0.716 to 0.888 (Claude Opus 4.5), structural compliance improves from 0% to 100%, and spark cosine similarity increases from 0.221 to 0.620. Balanced cross-domain training (biomedical + AI/ML + CS) outperforms single-domain training, providing evidence that scientific reasoning patterns transfer across disciplines. Human validation of 150 stratified records confirms 99.7% step-level grounding accuracy and a 0.0% fabrication rate. To my knowledge, CrossTrace is the first large-scale, cross-domain dataset with step-level grounded reasoning traces for hypothesis generation, and my results demonstrate that such traces are an effective training signal whose benefits are at least partially domain-general.
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