Stellar Colosseum: A Many-Agent Harness for Long-Horizon Research in Mathematics and Theoretical Computer Science· 星际竞技场:数学和理论计算机科学长期研究的多代理框架
Language models can produce plausible short proofs, but may still be unreliable on long-horizon research problems, where progress depends on a sequence of uncertain and interdependent decisions. We introduce Stellar Colosseum, a model-agnostic harness for allocating inference across research in mathematics and theoretical computer science. Colosseum explores alternative strategies before proof construction, uses a readiness gate to decide when a route is mature enough to decompose, represents the proof plan as interdependent section-level subproblems, and routes verifier findings back to the affected part of the argument. Across these stages, it generates candidates in parallel, attacks them with targeted falsification, and combines candidates and their critiques into a single research art
提出星际竞技场,解决多代理在数学和理论计算机科学中的长期研究问题
- 核心方法
- 星际竞技场通过探索替代策略、使用准备度门决定分解时机、将证明计划表示为相互依赖的子问题,并将验证结果反馈到论证的受影响部分
- 适合谁读
- 研究者
- 要解决的问题
- 语言模型在长跨度研究问题中不可靠,因为这些问题依赖于一系列不确定和相互依赖的决策
- 关键实验
- 未提供
- 主要贡献
- 提供了一个模型无关的框架,优化了推理资源的分配,提高了多代理在长期研究问题上的可靠性
- 意义与局限
- 为数学和理论计算机科学的长跨度研究提供了一种新的方法,可能改善这些领域的自动化研究能力;局限在于理论验证和实际应用尚需进一步研究