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The paper introduces CrossTrace, a novel dataset of 1,389 grounded scientific reasoning traces across biomedical research, AI/ML, and cross-domain work, designed to train and evaluate hypothesis generation models. Each trace meticulously captures the reasoning chain from established knowledge to a novel hypothesis, with each step grounded in source paper text. Fine-tuning Qwen2.5-7B-Instruct on CrossTrace significantly improves hypothesis generation performance, demonstrating the effectiveness and domain-general benefits of using grounded reasoning traces as a training signal.
Training on grounded reasoning traces doesn't just improve hypothesis generation鈥攊t makes models 100% structurally compliant and boosts spark cosine similarity by nearly 3x.
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.
