arXiv AI
MILO: Automated Harness Discovery via Orchestrated Multi-Agent Evolution
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arXiv AI
Aug 11
Hierarchical Self-Improvement: A Framework for Task-Specific Evolvable Agent Harnesses
arXiv:2608. 08466v1 Announce Type: new Abstract: Modern LLM agents are often improved by modifying prompts, tools, or workflows manually, while the executable scaffold surrounding the model---the \emph{harness}---is typically treated as a fixed artifact after deployment.
arXiv AI
Jul 7
MetaSkill-Evolve: Recursive Self-Improvement of LLM Agents via Two-Timescale Meta-Skill Evolution
arXiv:2607. 05297v1 Announce Type: new Abstract: Recent LLM agents tackle increasingly long-horizon, open-ended tasks, and external skills, reusable procedural knowledge supplied to the agent, further extend this capability.
arXiv Machine Learning
Sep 25
EvoTreeNAD: Genealogy-Guided Evolution for LLM-Driven Neural Architecture Discovery
EvoTreeNAD is a genealogy‑guided evolutionary algorithm that autonomously discovers neural architectures without a predefined seed or search space. Starting from an empty root, it builds a persistent genealogy where each node represents a complete architecture; top‑percentile values from nodes and descendants steer lineage selection. The method combines an Idea Agent that proposes variants and a Code Agent that implements them, with theoretical analysis showing stationary variation regimes and empirical results demonstrating superior performance on CIFAR‑10/100 and MedMNIST‑v2 tasks.
arXiv AI
3d ago
Self-Evolving Harness on Multiple Tasks with the Agent as Its Own Optimizer
The paper introduces a self‑evolving harness framework where a frozen language‑model agent first solves tasks and then edits its own harness based on run records. Using a 49‑line seed harness, the evolved harness improves average scores on in‑distribution benchmarks by 4.48 points and on out‑of‑distribution benchmarks by 12.64 points, surpassing Codex on the former and matching it on the latter. Continued evolution on a specific out‑of‑distribution benchmark further raises performance, and the study analyzes emergent mechanisms such as output truncation and history compaction.
arXiv AI
Aug 20
Eureka: Task-Conditioned Meta-Agent Orchestration for Scientific Discovery
Eureka is a task‑conditioned Meta‑Agent architecture that transforms long‑horizon scientific tasks into dynamic obligation graphs with explicit acceptance semantics. During execution it constructs Macro‑Agents equipped with specialized state, memory, operators, tools, verifiers, and local topology, using receding‑horizon planning, architecture promotion, and minimal‑sufficient compilation. The system demonstrates strong empirical performance, completing all 170 recursive tasks, generating 3,948 certificates without false acceptances, and achieving significant reductions in input size, recomputation, and consistent serialization across 16,000 concurrent executions.
arXiv AI
Aug 11
DarwinX: Evolving Agent Harnesses Through Natural Selection
arXiv:2608. 07545v1 Announce Type: cross Abstract: An LLM agent's capability depends not only on model weights but on its harness: prompts, tools, skills, and control flow.