AutoSR把符号回归从找公式变成做研究，9个挑战全部恢复，连3个cp3-bench难题都解决了。
AutoSR是一个全自动符号回归系统，通过搜索持续的研究状态而非孤立方程来发现科学关系。在9个选自两个基准套件的挑战中，AutoSR全部恢复了代数等价关系，包括3个cp3-bench问题和6个LSR-Transform问题。系统采用proposer-reviewer智能体在渐进加宽蒙特卡洛树搜索（PW-MCTS）下开发研究状态。与仅优化数值拟合和语法复杂度的方法不同，AutoSR保留动机、计算证据和独立评审，最终生成报告解释所选关系。
AutoSR: Automatic Symbolic Regression by Searching Research States
We introduce Automatic Symbolic Regression (AutoSR), a fully automated system that instantiates Research-Space Symbolic Regression by searching persistent scientific investigations rather than isolated equations. Finite, noisy data often yield numerically competitive expressions that imply very different behavior outside the observed regime, making numerical fit and syntactic complexity insufficient measures of scientific credibility. Existing approaches largely focus on improving expressions, yet the search typically retains little beyond the resulting formula and score, losing the scientific record, such as motivations and probes, that inform what to try next. AutoSR preserves this record in a \textbf{Research State}, coupling each candidate equation with the reasoning, computational evidence, and independent review developed along its branch. Proposer--reviewer agents develop these states under progressive-widening Monte Carlo tree search (PW-MCTS), which allocates computation across competing investigations, while the accumulated research record is ultimately synthesized into a final report that explains the leading relation and the basis for its selection. Across nine selected challenges from two benchmark suites, AutoSR recovers algebraically equivalent relations in every case, including three cp3-bench problems that no published system recovers and six structurally diverse LSR-Transform problems. Overall, AutoSR extends symbolic regression from equation-level search toward automated scientific investigation, allowing scientific knowledge and accumulated evidence to shape both what is explored and how the resulting equation is justified.