arXiv AI
Evolver: Turning Agent Exploration into Evolving Capabilities
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arXiv AI
Sep 7
From Interaction Traces to Persistent Skills: Online Evolution for Computer-Use Agents
The paper introduces an online skill‑evolution framework that transforms interaction traces and evaluator feedback into a persistent, versioned library of reusable procedures for computer‑use agents. By executing each iteration against a frozen library snapshot, the system updates skills without altering the underlying model parameters. Experiments across four OSWorld domains show that the evolving library consistently outperforms an empty‑library baseline, with gains ranging from 5.7 to 18.6 percentage points, while also revealing domain‑specific temporal stability and challenges in skill retrieval and revision.
Hugging Face Trending Papers
Jul 6
EvoAgentBench: Benchmarking Agent Self-Evolution via Ability Transfer
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.
arXiv AI
Oct 9
AgentEvolver: System-Wide Self-Evolution Through Task Execution
AgentEvolver is a system that enables agents to develop reusable capabilities while executing tasks, without altering the underlying foundation model. It uses eight entity families to expose operations, methods, agents, control flow, interfaces, and state for revision, coordinated by a shared Runtime and persistent planning. In evaluations on SWE-bench Pro Public, it achieved an 82.08% resolution rate, demonstrating retained capabilities across subsequent website, game, and research tasks, though some objectives remained incomplete.
arXiv AI
2d ago
Towards Robust Long-Lived Agents: Benchmarking Experience Lifecycles in Evolving Environments
The paper introduces ReScopeBench, a benchmark designed to evaluate how agents manage the lifecycle of experience in evolving environments. It differentiates between structural experience—knowledge about how an environment is organized—and rule experience—principles that guide decisions across tasks. Using an experience-first construction approach, the study tracks how agents acquire, reuse, and revise experience across task streams, revealing that while retained experience boosts performance, agents often fail to keep it current when environments change.
arXiv AI
Jul 7
EvoAgentBench: Benchmarking Agent Self-Evolution via Ability Transfer
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.
arXiv AI
Sep 23
Beyond Endpoint Performance: Process-Level Evaluation of Self-Evolving Agents
arXiv:2609.24663v1 Announce Type: new Abstract: Self-evolving agents convert interaction feedback into persistent artifacts, such as memories or skills, which in turn guide subsequent decisions. As t...