MEGA: Self-Evolving Agent Optimization Infrastructure via Wisdom Graph
As coding agents increasingly handle implementation, the central challenge shifts from building individual agents to building an infrastructure that systematically improves them. Current approaches optimize agent systems without accumulating transferable knowledge, accumulate knowledge without compositional reasoning over it, and lack a mechanism for that knowledge to self-evolve through operational evidence. MEGA (Meta Evaluation-Grounded Adaptation) addresses these gaps as a self-evolving infr
Record details
Published: 11 August 2026
Source: arXiv
Category: Research
Topics: Agents & autonomy
Retrieved: 12 August 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
AutoDesign: Meta-Harness Optimization for Long-Horizon Agentic Design
HuggingFace Daily Papers · 12 August 2026
AutoDesign: Meta-Harness Optimization for Long-Horizon Agentic Design
arXiv · 13 August 2026
Constructing Executable Analytical Knowledge Representations for Meta-Analysis Synthesis Using an Agentic Harness
arXiv · 3 August 2026
Counsel: A Meta-Evaluation Dataset for Agentic Tasks
arXiv · 19 June 2026
Knowledge Reutilization in Meta-Reinforcement Learning
arXiv · 16 June 2026
The Meta-Agent Challenge: Are Current Agents Capable of Autonomous Agent Development?
arXiv · 3 June 2026
How to cite this record
ethics.ai (11 August 2026), “MEGA: Self-Evolving Agent Optimization Infrastructure via Wisdom Graph,” evidence record 18424, https://ethics.ai/record/18424 (originally published by arXiv).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.