CrossTrace: A Cross-Domain Dataset of Grounded Scientific Reasoning Traces for Hypothesis Generation

2026-04-01 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Advanced, extended

Summary

CrossTrace is a new dataset comprising 1,389 grounded scientific reasoning traces across biomedical research (518), AI/ML (605), and cross-domain work (266). Each trace details a structured reasoning chain from established knowledge to a novel hypothesis, with every step explicitly grounded in source paper text. The dataset extends the Bit-Flip-Spark framework with step-level verification, an eight-pattern discovery taxonomy, and multi-domain coverage. Fine-tuning Qwen2.5-7B-Instruct on CrossTrace using QLoRA significantly improves performance over an untuned baseline: IAScore rises from 0.828 to 0.968 (GPT-4o judge) and 0.716 to 0.888 (Claude Opus 4.5 judge), structural compliance reaches 100% from 0%, and spark cosine similarity increases from 0.221 to 0.620. Crucially, balanced cross-domain training outperforms single-domain training, demonstrating that scientific reasoning patterns are transferable across disciplines. Human validation confirmed 99.7% step-level grounding accuracy, and expert evaluation rated model-generated hypotheses highly (usefulness 4.18/5, soundness 3.76/5, overall 3.84/5).

Key takeaway

For AI Scientists and Machine Learning Engineers developing hypothesis generation models, you should prioritize training data that includes structured, grounded reasoning traces across multiple scientific domains. This approach, as demonstrated by CrossTrace, significantly improves model performance, structural compliance, and the ability to identify core insights, even for smaller models like Qwen2.5-7B-Instruct. Consider integrating diverse, high-fidelity trace datasets to teach domain-general reasoning competencies rather than relying solely on domain-specific or unstructured data.

Key insights

Structured, grounded reasoning traces encode transferable, domain-general scientific reasoning primitives for hypothesis generation.

Principles

• Explicit reasoning traces improve LLM hypothesis generation.
• Cross-domain training enhances scientific reasoning transfer.
• Grounding steps to source text ensures trace fidelity.

Method

A five-stage pipeline extracts reasoning traces from preprints using Claude Sonnet 4, including PDF parsing, structured extraction with few-shot examples, domain classification, and quality filtering, producing Input/Trace/Output records.

In practice

• Fine-tune LLMs on structured reasoning traces for hypothesis generation.
• Utilize cross-domain datasets to improve model generalization.
• Implement step-level grounding for verifiable reasoning chains.

Topics

• CrossTrace Dataset
• Scientific Hypothesis Generation
• Grounded Reasoning Traces
• Cross-Domain Transfer Learning
• Large Language Models

Code references

• andrewbouras/crosstrace

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.