The paper introduces component routing for self‑improving GUI agents, separating experience into locators, procedures, state facts, and lessons, and directing each to either the model weights or the prompt context. Experiments across three backbone families, two environments, and multiple seeds show that routing improves performance over whole‑trajectory baselines, with a rule based on recurrence and state‑conditionality accurately predicting the optimal destination. The study also analyzes how training dynamics and producer‑consumer differences affect the value of each destination, revealing that readout decreases for frequently recurring items when written to weights, while context gains grow with the information gap and weight gains shrink with the policy gap.
arXiv:2608. 15787v1 Announce Type: cross Abstract: Two Mixture-of-Experts (MoE) forward passes can share every weight yet route the same token through different experts.
arXiv:2608. 04968v1 Announce Type: new Abstract: The capabilities of an LLM agent depend not only on its model but on the harness: the executable program that constructs context, invokes tools, verifies results, and recovers from failure.
arXiv:2609.24974v1 Announce Type: cross Abstract: Agent harnesses, the external systems that mediate model-environment interaction, can substantially improve agent performance, but their gains remain...
arXiv:2609.15982v1 Announce Type: cross Abstract: Skills extend an LLM agent beyond its parametric knowledge, and the gain they promise rests on picking the right one. Deployed harnesses route by pre...
The paper introduces Agent Evolving Learning (AEL), a two‑timescale framework that dynamically evolves an LLM agent’s memory‑retrieval harness in open‑ended environments. A fast Thompson‑Sampling bandit selects among retrieval policies each episode, while a slower LLM reflection diagnoses performance drops and injects new policies when the current set plateaus. AEL outperforms ten self‑improving and non‑LLM baselines on a sequential portfolio benchmark, boosting Sharpe ratio by 27% and achieving significant accuracy gains on a support‑ticket routing stream.
Computer-use agents learn from what their actions change, so training one needs applications it can act on, break and reset. The applications that matter most are login-gated and stateful, so synthetic environments stand in for them.
arXiv:2608. 10333v1 Announce Type: new Abstract: LLM agents execute heterogeneous sequences of model calls within a single task: some invocations require careful reasoning, while others are structured steps such as formatting or tool-argument construction.
arXiv:2607. 28074v1 Announce Type: cross Abstract: Computer-use agents learn from what their actions change, so training one needs applications it can act on, break and reset.
TRACE (TRAjectory-Contrastive Evolution) is a self‑evolving skill bank that improves the consistency and limit‑awareness of large‑language‑model agents without changing the model weights. By iteratively refining modular skills based on successful and failed trajectories, TRACE raises consistent performance (Pass^3) on the CAR‑bench in‑car assistant tasks from 59.9 % to 94.5 % on GPT‑5.5 and achieves first place on the hidden set with GPT‑5.6‑Sol. The approach demonstrates that a skill‑based, self‑evolution loop can convert a model’s potential into stable, reliable behavior.
arXiv:2608. 10504v1 Announce Type: new Abstract: As coding agents increasingly handle implementation, the central challenge shifts from building individual agents to building an infrastructure that systematically improves them.
arXiv:2608. 02508v1 Announce Type: new Abstract: Learning-based memory systems for self-evolving LLM agents face two tightly coupled challenges.
arXiv:2609.36746v1 Announce Type: new Abstract: Agent skills provide a lightweight mechanism for self-evolving agents to accumulate reusable procedural knowledge without updating model parameters. Ho...