# InfiniteScienceGym：无限程序生成的科学分析基准

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-04-14 08:00
- AIHOT 链接：https://aihot.virxact.com/items/cmo0vpnos02w3sli21lrtfu5r
- 原文链接：https://arxiv.org/abs/2604.13201

## AI 摘要

针对传统科学基准存在的发表偏倚、标签噪声及大规模存储需求，研究团队提出InfiniteScienceGym——一个程序生成的科学分析测试平台。该平台通过确定性算法从种子生成包含真实目录结构、文件与表格数据的自包含仓库，并配备带精确标准答案的可验证问答任务。对主流模型的评估显示，当前无模型整体准确率超过45%，识别不可回答问题仍是主要能力短板，而更强的模型倾向于更有效地使用工具而非单纯增加token消耗。

## 正文

Large language models are emerging as scientific assistants, but evaluating their ability to reason from empirical data remains challenging. Benchmarks derived from published studies and human annotations inherit publication bias, known-knowledge bias, label noise, and substantial storage requirements. We present InfiniteScienceGym, a procedurally generated benchmark of scientific repositories paired with a verifiable question-answering task. From a seed, the simulator deterministically generates a self-contained repository with realistic directory structure, files, and tabular data, and a privileged QA generator produces both answerable and unanswerable questions with exact ground truth. This makes it possible to evaluate evidence-grounded reasoning, abstention, and tool-mediated analysis in a controlled setting without distributing a large static corpus. InfiniteScienceGym complements real scientific benchmarks by targeting blind spots and failure modes that are hard to evaluate using published datasets alone. Evaluating both proprietary and open-weight models, we find that none achieve more than 45% accuracy overall, that recognizing unanswerable questions remains a major weakness, and that stronger models tend to use tools more effectively rather than simply consuming more tokens.