The paper introduces RiseHarness, a framework that iteratively refines agent harnesses while keeping model weights fixed. It uses successful and failed runs under the same configuration to analyze failures, combining agent trajectories with runtime monitor traces to understand interactions between model actions and execution components. The framework then searches other harness frameworks for relevant implementations, adapts them, and validates changes through behavioral checks and repeated task evaluation, ultimately integrating mechanisms from multiple frameworks to improve reliability across tasks.
By Ruhan Wang, Yucheng Shi, Zongxia Li, Junyao Yang, Zhongzhi Li, Yue Yu, Haitao Mi, Dongruo Zhou, Leoweiliang
arXiv:2609.13543v1 Announce Type: new
Abstract: LLM agents are predominantly benchmarked on short, single-task trajectories, yet real deployments run for hours under contention, surfacing a different...
By Grace Chang Yuan, Xiaoman Zhang, Sung Eun Kim, Luyang Luo, Pranav Rajpurkar
The paper argues that deploying generative AI agents requires more than isolated task success; they must remain useful across repeated interactions, changing conditions, and dependencies on people within shared workflows. The authors introduce two complementary evaluation aspects—operational resilience and considerate participation—to assess how agents recover from blocked work, communicate limits, and adapt to affected people and role boundaries. Using 120 simulated healthcare trajectories across two AI models and twelve stakeholder-derived tasks under varying challenge levels, the study finds that agents shift toward greater human dependence and increased workload as challenge accumulates, while also broadening from task-focused adaptation to task reframing and wider coordination.
By Yuanchen Bai, Zijian Ding, Angelique Taylor
The paper introduces an evidence‑guided recovery framework for AI agents that encounter ambiguous tool outcomes and partial execution, such as timeouts or failures. It defines a shared admissibility check for key expiry, authorization timing, dependencies, and residual effects, and a terminal certificate to distinguish actual goal satisfaction from publicly supported safe completion. In a finite study of 37 assignments, the approach achieved 28 certified completions, and in a larger comparison of 58 paired units, evidence‑guided advice certified 30 completions versus 9 for evidence prompting, demonstrating a significant improvement in task completion rates.
By Genliang Zhu, Xiaobo Chen, Ziyuan Wang, Chu Wang
arXiv:2608. 14380v1 Announce Type: new Abstract: Many real-world tasks require LLM agents to interact with their environments over long execution horizons.
By Yu Zhuang, Kefei Chen, Yitong Duan, Shuxin Zheng, Jian Li, Xu-Yao Zhang
arXiv:2608. 13417v1 Announce Type: new Abstract: Autonomous agents are increasingly capable of improving models, systems, and other technical artifacts through long-horizon experimentation.
By Yiwei Li, Wanli Yang, Hexiang Tan, Xiangzhou Huang, Zhengyu Chen, Ziran Li, Borun Chen, Shanglin Lei, Huaisheng Zhu, Hao Tian, Fei Sun, Xunliang Cai, Jingang Wang
arXiv:2611.07595v1 Announce Type: cross
Abstract: Agent exploration produces valuable procedural knowledge, yet much of this experience remains confined to transient execution traces and is not syste...
By Haoyang Zhang, Qumeng Sun, Xiao Zhang, Xiang Liu, Zhidong Fan, Junjie Wang
arXiv:2607. 26598v1 Announce Type: cross Abstract: Large language model (LLM) agents may recover from a failure within an episode or after a retry, yet the same execution failure can recur in later tasks because post-episode feedback rarely revises the persistent harness that guides future interactions.
By Yuetian Du, Yucheng Wang, He Xu, Jiexu Xu, Shanwen Tan, Bing Zhao, Boyu Yang, Zhijie Xu, Ming Kong, Hu Wei, Jie Liu, Qiang Zhu
PILOT introduces a live self‑improvement framework for long‑horizon agents, enabling a supervisor to redirect or abort an active worker during execution and to distil learned procedures into reusable skills. Unlike existing single‑agent or subagent architectures, PILOT’s supervisor‑worker harness supports real‑time steering and self‑evolution. Experiments on two frozen backbones across three benchmarks show PILOT outperforming other harnesses, achieving significant gains in performance metrics and efficiency.
By Yang Xiao, Yusong Sun, Haoyi Wu, Wenyang Hui, Wen Da, Zhaokai Luo, Mu Chuan, Yao Hu, Wenjie Li, Chengyue Jiang
arXiv:2608. 09380v1 Announce Type: new Abstract: Long-horizon complex tasks require agents to repeatedly observe states, formulate plans, invoke tools, verify results, and recover from failures in continuously changing environments.
By Siqi Wang, Xinlin Li, Zhenglin Li, Li Li
arXiv:2609.21423v1 Announce Type: new
Abstract: Online agent deployments produce abundant execution traces, while task-specific verification and expert annotation are costly to scale. We study how to...
By Siyuan Liu (Fudan University, Meituan Longcat Team), Fan Yu (Fudan University, Meituan Longcat Team), Dongyu Ru (Meituan Longcat Team), Yizhu Liu (Meituan Longcat Team), Yifan Yang (Meituan Longcat Team), Xuezhi Cao (Meituan Longcat Team), Xunliang Cai (Meituan Longcat Team), Yixin Cao (Fudan University)
The paper introduces Sibling Disagreement in Imagination (SDI), a method that uses a shared actor-based reference to guide exploration and planning in world models. SDI compares action sequences imagined from the same state, scaling exploration signals and action acceptance based on the variability of the actor’s own rollouts. Experiments on Meta-World, Adroit, and DM‑Control show SDI outperforms baselines on most tasks and improves recovery from actor failures while preserving successful behavior.
By Hieu Tran, Hong Yu