arXiv (All)
open access
Agent Plasticity: Measuring Self-Improvement Through Experience
AI agents increasingly operate in environments where they can diagnose failures and improve through experience, yet existing evaluations largely measure what an agent can do at a fixed point in time rather than how effectively it learns. Evaluating self-improvement requires answering three questions
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- EvolveNet: Collaborative Harness Evolution for Agent Self-Improvement#429368
- A Survey on Self-Improving Test-Time Intelligence: Feedback-Driven Adapting, Learning, and Scaling at Inference#822114
- EvoSkill Injection: Red-Teaming Autonomous Skill Generation and Evolution in Self-Evolving Agents#799632
- AgentEvolver: System-Wide Self-Evolution Through Task Execution#1248893
- An Empirical Study of Automating Agent Evaluation#1076077
Record · ID 1232941
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive