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Source note3 min read

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

Source

arXiv

arxiv.org/abs/2608.19047v1

Read the source

Source

  • Source name: arXiv
  • Source kind: paper
  • Authors / organization: Alizer Wong, Heng Cui, Yi Tan, Xiongchao Zhan, Liang Lin, Yuxiang Guo, Zhaorong Dai, Zixin Zeng, Wenyuan Li
  • Venue: arXiv: cs.AI
  • Published: 2026-08-19
  • Collected: 2026-08-20
  • URL: http://arxiv.org/abs/2608.19047v1
  • PDF: https://arxiv.org/pdf/2608.19047v1
  • DOI: UNKNOWN
  • Registry group: academic_apis_and_preprints
  • Evidence role: canonical_academic_index
  • Language: en
  • Country / region: UNKNOWN

Plain-Language Brief

The paper presents Eureka, a Meta-Agent architecture that can complete long-horizon tasks by forming specialized Macro-Agents for specific tasks. The architecture allows for cost-benefit-gated evolution to update the local architecture when bottlenecks occur. Experimentally, Eureka completes all recursive tasks and generates certificates without false acceptances. It also demonstrates its use in scientific discovery by yielding structural results in quantum-process and spacetime theory, identifying bottlenecks in Riemann Hypothesis research, and advancing a positivity certificate for Suzuki's localized Weil quadratic form.

What the Document Says

  • Eureka is a task-conditioned Meta-Agent architecture
  • Eureka forms Macro-Agents with specialized state, memory, operators, tools, verifiers, and local topology
  • Receding-horizon planning, architecture promotion, and minimal-sufficient compilation are used in forming Macro-Agents
  • Cost-benefit-gated evolution updates the local architecture under constraints when bottlenecks recur
  • Theoretical results on regret, planning invalidation, amortization, subtree interfaces, serializability, and verification are established
  • 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
  • Theory-Discovery Agent yields structural results in quantum-process and spacetime theory
  • Math/Conjecture Agent identifies bottlenecks in Riemann Hypothesis research and advances a positivity certificate for Suzuki's localized Weil quadratic form

Key Concepts Mentioned

  • Meta-Agent
  • Macro-Agent
  • Receding-horizon planning
  • Architecture promotion
  • Minimal-sufficient compilation
  • Cost-benefit-gated evolution
  • Theory-Discovery Agent
  • Math/Conjecture Agent

Claims Made by the Document

  • Eureka can complete long-horizon tasks
  • Eureka can form specialized Macro-Agents for specific tasks
  • Eureka can update the local architecture when bottlenecks occur
  • Theoretical results on regret, planning invalidation, amortization, subtree interfaces, serializability, and verification are established for Eureka
  • Eureka completes all recursive tasks and generates certificates without false acceptances
  • Active context compression is achieved with Eureka
  • Incremental processing avoids recomputation across 12,000 tasks with Eureka
  • Serializing 16,000 concurrent executions is consistent with Eureka
  • The same Meta-Agent can instantiate a Theory-Discovery Agent and a Math/Conjecture Agent
  • The Theory-Discovery Agent yields structural results in quantum-process and spacetime theory
  • The Math/Conjecture Agent identifies bottlenecks in Riemann Hypothesis research and advances a positivity certificate for Suzuki's localized Weil quadratic form

Evidence Used

  • Eureka is presented in the paper
  • Details about forming Macro-Agents, cost-benefit-gated evolution, theoretical results, experimental results, and scientific discovery are described in the paper

Methods / Approach

  • Receding-horizon planning
  • Architecture promotion
  • Minimal-sufficient compilation
  • Cost-benefit-gated evolution

Limitations Stated by the Authors

  • UNKNOWN
  • UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

  • The paper does not provide information about the base model used in the Meta-Agent
  • The paper does not provide detailed information about how the Meta-Agent identifies bottlenecks or advances positivity certificates

Open Questions

  • What is the base model used in the Meta-Agent?
  • How does the Meta-Agent identify bottlenecks and advance positivity certificates?

Notes for Future Q&A

May include interpretation. Labeled where it does.

  • The authors do not provide information about the base model used in the Meta-Agent
  • The authors only mention that the Meta-Agent can identify bottlenecks and advance positivity certificates, but they do not explain the process

Raw Abstract / Excerpt

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.