Title: Tiny Moves: Game-based Hypothesis Refinement
Primary area: foundation or frontier models, including LLMs
Keywords: scientific reasoning, hypothesis refinement, multi-agent system, LLM, symbolic reasoning, game-based framework

Abstract: Scientific discovery is an iterative process, yet most machine learning approaches treat it as an end-to-end prediction task, limiting interpretability and alignment with scientific reasoning workflows. We introduce The Hypothesis Game, a symbolic, game-based framework where a system of agents refines hypotheses through a fixed set of reasoning moves (a reasoning grammar). Inspired by the idea that scientific progress often relies on small, incremental changes, our framework emphasizes “tiny moves” as the building blocks of incremental hypothesis evolution. We evaluate the approach on pathway-level reasoning tasks derived from Reactome, focusing on reconstruction from partial cues and recovery of corrupted hypotheses. Across 820 reconstruction and 2880 corruption experiments, it matches strong prompting baselines on reconstruction and achieves superior precision and error recovery in corruption. Beyond accuracy, it produces concise, interpretable hypotheses and enables controllable reasoning, highlighting the potential of game-based reasoning for accelerating discovery across the sciences.

TL;DR: The Hypothesis Game is a symbolic, game-based framework where LLM agents refine hypotheses through small reasoning moves, enabling accurate, interpretable, and controllable scientific discovery.