Adnan KhanAdnanProduct & AI
San Francisco Bay Area
Get in touch

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 introduces Eureka, a Meta-Agent architecture that can complete long-horizon tasks by forming specialized Macro-Agents through receding-horizon planning and architecture promotion. The agents can evolve to overcome bottlenecks and are theoretically efficient in terms of regret, planning invalidation, amortization, and more. Experimentally, Eureka has shown success in completing various recursive tasks and generating certificates without false acceptances. It also demonstrated the discovery of structural results in quantum-process and spacetime theory, as well as advancements in Riemann Hypothesis research.

What the Document Says

  • The paper introduces a Meta-Agent architecture called Eureka.
  • Eureka compiles long-horizon tasks into dynamic obligation graphs.
  • During execution, Eureka forms Macro-Agents with specialized components via receding-horizon planning, architecture promotion, and minimal-sufficient compilation.
  • When bottlenecks recur, cost-benefit-gated evolution updates the local architecture under constraints.
  • The paper presents theoretical 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 can instantiate 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.
  • These results suggest that the capability of scientific agents depends not only on the base model but on whether an architecture can be formed to match the task's cognitive structure.

Key Concepts Mentioned

  • Meta-Agent
  • Dynamic obligation graphs
  • Macro-Agents
  • 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.
  • Eureka can evolve to overcome bottlenecks.
  • The paper provides theoretical results on various aspects of Eureka's performance.
  • Experimentally, Eureka has shown success in completing recursive tasks and generating certificates without false acceptances.
  • Active context compression is achieved with Eureka.
  • Incremental processing avoids recomputation with Eureka.
  • 16,000 concurrent executions serialize consistently with Eureka.
  • The same Meta-Agent can instantiate a Theory-Discovery Agent and a Math/Conjecture Agent.
  • The former yields structural results in quantum-process and spacetime theory.
  • The latter advances a positivity certificate for Suzuki's localized Weil quadratic form.

Evidence Used

  • The description of Eureka's function and operation in the text.
  • The experimental results presented in the text.
  • The theoretical results presented in the text.

Methods / Approach

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

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

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.