arXiv:2610.27954v1 Announce Type: new
Abstract: Recent advances in large (vision) language models have enabled computer-use agents that automate digital workflows via fine-grained keyboard and mouse...
By Byeonghun Bang, Sangwoo Park, Jinheon Baek, Kangsan Kim, Youngwan Lee, Sung Ju Hwang
Long-horizon coding requires agents to maintain an evolving view of the project they are changing: what problem is currently being solved, what has already been learned or modified, which other artifa...
arXiv:2608. 12851v1 Announce Type: new Abstract: Self-improving LLM agents convert successful trajectories into persistent cross-task state.
By Xutao Mao, Liangjie Zhao, Xiang Zheng, Cong Wang
The paper investigates how large language model (LLM) agents can improve performance by storing and reusing experience in external memory. It identifies a problem called error reproduction, where mistakes in stored abstractions propagate through cycles of abstraction, execution, and re-abstraction, leading to persistent errors. To mitigate this, the authors propose Anti-Persistence, a memory design that retains raw trajectories and outcomes but reconstructs task-specific guidance on demand, discarding it after use. Experiments across multiple benchmarks show that Anti-Persistence yields higher held‑out success rates and lower LLM‑call costs compared to other memory designs, supporting the idea of separating experience accumulation from abstraction persistence.
By Ruhan Wang, Kishan Panaganti, Dongruo Zhou
Agent-Controlled Forgetting for Tool-Using Agents: Reversible Context Curation in Practice explores a method where an agent selectively replaces detailed tool results with concise notes while archiving the originals for recovery. Using a Python harness, the approach reduces prompt tokens from 912,492 to 231,951 and cuts cumulative input tokens by 50%, yielding significant API cost savings. However, the method increases request count, slightly slows execution, and its effectiveness varies across different workloads.
By Jan-Peter Franke
The paper investigates how deleting portions of a software engineering agent’s past trajectory affects performance and cost. Across 9,400 runs with two state‑of‑the‑art agents, it finds that large deletions (even up to 100% of reasoning text) can maintain overall effectiveness if applied with a delay, but immediate deletion harms performance. The study also shows that cost savings depend on the pricing model: a flat model yields savings in 26 of 42 cases, while a caching‑aware model actually increases costs because it breaks a low‑rate cached prefix.
By Fernando Vallecillos-Ruiz, Islem Bouzenia, Michael Pradel, Leon Moonen