Eureka: Task-Conditioned Meta-Agent Orchestration for Scientific Discovery· Eureka：用于科学发现的任务条件元代理编排
We present Eureka, a task-conditioned Meta-Agent architecture that compiles long-horizon tasks into dynamic obligation graphs with explicit acceptance semantics. During execution, Eureka forms Macro-Agents with specialized state, memory, operators, tools, verifiers, and local topology via receding-horizon planning, architecture promotion, and minimal-sufficient compilation. When bottlenecks recur, cost-benefit-gated evolution updates the local architecture under constraints. Theoretically, we establish results on regret, planning invalidation, amortization, subtree interfaces, serializability, and verification. Experimentally, Eureka completes 170/170 recursive tasks and generates 3,948 certificates with no false acceptances. Active context compresses median input from 9,490 to 4,005 token
Eureka 架构自动编排元代理，实现高效科学发现任务处理。
- 核心方法
- 使用任务条件元代理架构，通过滚动时域规划、架构提升和最小必要编译动态生成义务图。
- 适合谁读
- 研究者 / 工程师
- 要解决的问题
- 解决科学发现中长周期任务的自动化与优化问题。
- 关键实验
- 完成170/170递归任务，生成3,948个证书，无误接受；上下文压缩中位数从9,490降至4,005个token。
- 主要贡献
- 提出了Eureka架构，有效处理长周期任务，并确保无误接受。
- 意义与局限
- 显著提高了科学发现任务的自动化和效率，但可能局限于特定类型的任务。