arXiv:2606.13020v1 Announce Type: new Abstract: Three paradigmatic forms of inference recur across scientific reasoning: deduction, induction, and causal abduction. Reliably evaluating LLMs on these in scientific settings is currently out of reach: scientific benchmarks built on human annotations are costly and lack mechanistic ground truth, while synthetic logical-reasoning benchmarks do not resemble real scientific documents. We introduce SciR, a benchmark that combines multi-paradigm reasoning with controllable scientific rendering, anchored on three paradigmatic scientific problems. Tasks
The proliferation of advanced LLMs necessitates robust evaluation methods to understand their true capabilities and limitations in complex domains like scientific reasoning.
This benchmark provides a critical tool for developing more capable and reliable AI, especially for scientific discovery and problem-solving, by addressing existing limitations in evaluation.
The ability to systematically and controllably evaluate LLMs on scientific reasoning tasks significantly improves the development cycle for AI models aiming for scientific applications.
- · AI researchers
- · LLM developers
- · Scientific research institutions
- · AI ethics and safety organizations
- · Developers of poorly evaluated LLMs
- · Benchmarks relying solely on human annotations
Improved scientific reasoning capabilities in future LLMs due to more rigorous evaluation.
Accelerated scientific discovery and innovation through AI systems that can effectively engage in complex reasoning.
New forms of scientific collaboration where LLMs act as intelligent assistants or co-reasoners alongside human researchers.
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Read at arXiv cs.AI