arXiv:2610.41225v1 Announce Type: new
Abstract: Large language models offer a promising route to register-transfer-level (RTL) design, but reliable implementations require checking and revision based...
By Wenxuan Shi, Yaoyu Zhu, Di Huang, Chongxiao Li, Xiaoyun Zhang, Muxin Song, Yutong Wu, Ziyuan Nan, Pengwei Jin, Tian Zhi, Xishan Zhang, Rui Zhang, Xing Hu, Zidong Du, Qi Guo, Yunji Chen
The paper introduces Boundary-Aware Skill Memory (BASM), a method that enriches skill memories for large language model agents with explicit boundary fields such as applicability conditions, risk cues, avoidance rules, and recovery notes. This approach transforms retrieved skills from unconditional templates into state‑conditioned guidance, preventing the Skill Imitation Trap where more skills lead to incorrect tool usage. Experiments on three agent benchmarks and four model scales show that BASM improves task success rates, accuracy, and reduces attack success while cutting average steps compared to memory‑free baselines.
By Zihan Lin, Zhenyu Chen, Jiawen Wei, Xiaohan Wang, Jie Cao, Jiajun Chai, Wei Lin, Guojun Yin, Ran He
arXiv:2606. 05680v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have enabled the automatic synthesis (generation) of register-transfer level (RTL) code from natural language instructions, offering a promising pathway to accelerate chip design.
By Mohammad Akyash, Nowfel Mashnoor, Kimia Azar, Hadi Kamali
Autonomous computer-use agents are increasingly applied to long-horizon tasks requiring coordinated application calls, persistent state tracking, and verifier-sensitive writes, yet they remain prone t...
The paper introduces EDV, an Execute‑Distill‑Verify framework that constructs offline experience for large language model agents without updating model parameters. In EDV, multiple heterogeneous agents execute tasks, a distiller extracts candidate lessons from their trajectories and feedback, and executors review these lessons, keeping only those unanimously approved. Experiments on τ²‑bench, Mind2Web, and MMTB demonstrate improved performance on revisited tasks and within‑domain transfer, while a Retail subset evaluation shows EDV cuts the Self‑Confirmation Trap Rate from 11.0% to 5.0%.
By Shiding Zhu, Yudi Qi, Yajie Wang, Jiaze Li, Chao Song, Yaorui Shi, Yibo Miao, Hanqi Gao, Kai Zhang
arXiv:2608.22533v1 Announce Type: new
Abstract: Autonomous computer-use agents are increasingly applied to long-horizon tasks requiring coordinated application calls, persistent state tracking, and v...
By Zheyuan Deng, Binghang Lu, Hanqi Feng, Shirley Huang, Dianzhuo Wang, Yuanda Xu, Zhiwei Zhang, Yige Sun, Changhong Mou, Runyu Zhang, Yuexing Hao, Barnabas Poczos, Xiaomin Li