---
title: "Eureka: Task-Conditioned Meta-Agent Orchestration for Scientific Discovery"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2608.19047v1"
permalink: "https://feed.astralor.com/items/itm_abf86bf10c3e62d4613b0219"
published: "2026-08-19T15:40:21.000Z"
score: 78
channel: "tech"
tags: ["Eureka","Meta-Agent","Macro-Agent","Agent Orchestration","Scientific Discovery"]
---

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

## 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.

## At a glance

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.

## Key points

- 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

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.

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