Eureka: Task-Conditioned Meta-Agent Orchestration for Scientific Discovery

2026-08-19

AI Summary

Eureka通过任务条件元代理架构解决科学发现中的长周期任务问题，利用动态义务图和宏代理等方法提高任务执行效率与准确性。

Abstract

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 tokens; incremental processing avoids 65.38% recomputation across 12,000 tasks; 16,000 concurrent executions serialize consistently. The same Meta-Agent instantiates a Theory-Discovery Agent and a Math/Conjecture Agent. The former yields structural results in quantum-process and spacetime theory. The latter identifies bottlenecks in Riemann Hypothesis research and advances a positivity certificate for Suzuki's localized Weil quadratic form to 0 < a <= 69/200 = 0.345, reaching ~99.55% of (log 2)/2. These results suggest that scientific-agent capability depends not only on the base model but on whether an architecture can be formed to match the task's cognitive structure.

Problem

• Meta-Agent
• Scientific Discovery
• Long-horizon Tasks
• Dynamic Obligation Graphs
• Receding-horizon Planning

Innovation

• task-conditioned Meta-Agent
• dynamic obligation graphs
• receding-horizon planning
• cost-benefit-gated evolution
• scientific discovery