HypoSpace: A Diagnostic Benchmark for Set-Valued Hypothesis Generation under Underdetermination and Sublinear Coverage Bounds
arXiv:2510.15614v3 Announce Type: replace Abstract: Many scientific problems are underdetermined: multiple distinct hypotheses are equally consistent with the same observations. In such settings, effective inference requires not only producing valid explanations, but also systematically exploring and covering the admissible hypothesis set. We introduce HypoSpace, a benchmark that treats large language models (LLMs) as samplers over finite hypothesis spaces and evaluates them on three metrics: Validity, Uniqueness, and Recovery. HypoSpace spans three structured domains (causal graph inference,
The proliferation of advanced LLMs necessitates more rigorous diagnostic benchmarks to understand their capabilities and limitations in complex reasoning tasks, especially as they move towards greater autonomy.
A strategic reader should care because this benchmark directly addresses a core challenge in AI development: reliably evaluating LLM performance in generating and exploring multiple valid hypotheses, critical for robust decision-making systems.
The introduction of HypoSpace provides a standardized method to assess LLMs' ability to handle ambiguity and explore solution spaces, shifting focus from single-best answer generation to comprehensive hypothesis coverage.
- · AI researchers
- · LLM developers
- · Companies implementing AI for complex problem-solving
- · LLMs lacking diagnostic reasoning capabilities
- · AI evaluation methods focused solely on single-point accuracy
HypoSpace will drive development towards LLMs that can systematically explore and articulate multiple valid hypotheses, crucial for scientific discovery and agentic systems.
Improved diagnostic capabilities in LLMs could accelerate scientific research by enabling AIs to generate and test a wider range of plausible theories more efficiently.
The ability of AI to explore 'underdetermined' problem spaces could fundamentally alter human-AI collaboration, with AI acting as a sophisticated, multi-perspective brainstorming partner rather than just a suggestion engine.
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Read at arXiv cs.CL