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

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 system designed for scientific discovery tasks. Eureka forms specialized agents through planning, promotion, and minimal-sufficient compilation, adapting to recurring bottlenecks via cost-benefit-gated evolution. The authors report that Eureka completes 170/170 recursive tasks without false acceptances, reduces input size significantly, minimizes recomputation, and handles multiple concurrent executions consistently. Eureka has been used to make structural discoveries in quantum process and spacetime theory, and to advance a positivity certificate for Suzuki's localized Weil quadratic form.

What the Document Says

• Eureka is a task-conditioned Meta-Agent architecture that creates 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.
• 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.
• 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.

Key Concepts Mentioned

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

Claims Made by the Document

• Eureka completes all given tasks without errors.
• Active context compression and incremental processing are efficient in Eureka.
• Multiple concurrent executions can be handled consistently by Eureka.

Evidence Used

• 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.

Methods / Approach

UNKNOWN

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.