arXiv:2610.36660v1 Announce Type: new Abstract: Self-improving AI systems can revise the designs that govern their own behavior. Yet existing approaches center improvement on selected parent agents a...
arXiv:2610.29784v1 Announce Type: new Abstract: Most contemporary artificial intelligence (AI) systems are released with frozen parameters and improve through external updates, whereas an ideal syste...
arXiv:2606. 26294v1 Announce Type: cross Abstract: Self-improving agents are state-of-the-art (SOTA) on agentic coding benchmarks and have recently been extended to general domains.
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.
FREEEVOLVE automates the optimization loop used by agent evolvers, allowing the system to decide what to test, how much evidence to gather, which candidates to pursue, and when to stop, all within specified resource limits. By treating these decisions as an editable evolution skill and improving them through meta‑evolution, the method learns a capability that replaces hand‑crafted search loops. Experiments on several benchmarks show that FREEEVOLVE improves primary metrics by an average of 13.6 points and that meta‑evolved skills add further gains, indicating transferability across environments.
The paper introduces Successive Scale Racing (SSR), a framework that improves agentic harness evolution by adaptively selecting optimal evidence combinations during each evolution round. SSR constructs diverse evidence sets based on task quality and coverage, then efficiently races candidate harnesses under a limited compute budget. Across three benchmarks, SSR outperforms baselines, steadily raising pass rates as the task pool expands from 40 to 200 while cutting validation executions by 60%.
The paper introduces Agent S4, an autonomous system that learns to design and refine user interfaces for computer‑use agents by observing task execution without explicit success labels. By evolving a repository of interfaces through population‑based selection on unseen validation tasks, Agent S4 achieves state‑of‑the‑art performance on OSWorld V2 and significant gains on ScienceBoard, outperforming large commercial models at lower cost. The work demonstrates that interface design can be treated as an agent capability, enabling continuous improvement beyond fixed, hand‑crafted interfaces.
arXiv:2610.20431v1 Announce Type: cross Abstract: Recursive self-improvement (RSI) in scientific research aims to improve agents' research capabilities through their own experimental experience. Scie...
arXiv:2610.36826v1 Announce Type: new Abstract: Autonomous agents increasingly depend on harnesses that govern interaction, memory, tools, and execution around large language models (LLMs). While rec...
arXiv:2610.22984v1 Announce Type: new Abstract: Experience-driven self-evolution offers a promising path for large language model (LLM) agents to continually improve through interaction. Yet existing...
arXiv:2608. 09629v1 Announce Type: new Abstract: Self-evolving agents are usually built around prescribed optimization pipelines: the framework decides how to gather evidence, revise a persistent artifact, select candidates, and stop.
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...