arXiv:2607.05202v1 Announce Type: new Abstract: Agent self-evolution in long-horizon LLM systems is largely procedural: useful experience is not merely stored information, but reusable procedures for searching, debugging, and verification. Yet current evaluations do not isolate this form of transfer. Agent benchmarks test single-episode task solving; memory benchmarks target information retention rather than procedural reuse. We introduce EvoAgentBench, a benchmark for agent self-evolution via Ability-guided transfer across four agentic domains: web research, algorithmic reasoning, software en
The increasing complexity and long-horizon nature of LLM systems necessitate better evaluation methods for agent self-evolution and ability transfer, moving beyond static task solving.
This benchmark addresses a critical gap in assessing how AI agents truly learn and reuse procedures, which is fundamental to developing more capable and autonomous systems.
The introduction of EvoAgentBench shifts the focus of agent evaluation from single-episode task achievement to the more complex and valuable metric of procedural learning and reusability across diverse domains.
- · AI agent developers
- · Research institutions
- · Businesses deploying autonomous systems
- · Companies relying on static AI evaluation metrics
- · AI paradigms focused solely on task completion
More sophisticated and robust AI agents capable of genuine self-evolution will emerge.
This will accelerate the deployment of autonomous systems into more complex and dynamic real-world environments.
The development of truly 'learning' AI agents could significantly impact white-collar workflows and the broader economy by enabling more adaptive automation.
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Read at arXiv cs.AI