Eureka: Task-Conditioned Meta-Agent Orchestration for Scientific Discovery
Score
78提出任务条件元智能体架构这一新范式，并给出理论保证与多场景实证，信息增量显著。
论文结构完整，理论分析与实验设计严密，数据翔实，质量上乘。
为科学发现自动化提供了可复用的架构编排方法，对AI Agent研发有直接参考价值。
发布于arXiv预印本，紧追AI for Science与多智能体编排的研究前沿。
arXiv为预印本平台，文章来自AI/CL/LG领域，未经同行评审但作者团队及内容专业度较高。
Base 86.5 × T2 Source weight ×0.9 = 78
Curation line 70Selected
Why selected
The paper proposes a complete meta-agent orchestration framework with both theoretical analysis and large-scale experimental validation, contributing significant incremental value to agent architecture research.
AI commentary
「What stands out is that Eureka shifts the view of agent capability from 'how large the model is' to 'how the architecture grows', which is provocative for current agent research. If you are working on automating scientific discovery or long-horizon task agents, this orchestration approach centered on obligation graphs and evolution mechanisms deserves a close read.」
Quick overview
Eureka is a task-conditioned Meta-Agent architecture that compiles long-horizon tasks into dynamic obligation graphs with explicit acceptance semantics, forming specialized Macro-Agents via receding-horizon planning, architecture promotion, and minimal-sufficient compilation. Experiments show that Eureka completes 170/170 recursive tasks and generates 3,948 certificates with no false acceptances, while active context compression reduces median input by about 58% and incremental processing avoids 65.38% recomputation. The same architecture instantiates a Theory-Discovery Agent and a Math/Conjecture Agent, yielding structural results in quantum-process and spacetime theory and advancing a positivity certificate for Suzuki's localized Weil quadratic form. 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.
- Eureka introduces dynamic obligation graphs with explicit acceptance semantics for reliable decomposition and verification of long-horizon tasks.
- It avoids 65.38% recomputation across 12,000 tasks and maintains serialization consistency across 16,000 concurrent executions.
- The same Meta-Agent instantiates Theory-Discovery and Math/Conjecture Agents, yielding concrete results in quantum-process, spacetime theory, and Riemann Hypothesis research.
Full text
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