The paper investigates whether compact AI agents can display strategic behavior in poker under hidden information and whether preference optimization can surpass static and zero‑shot policies. After evaluating a static baseline and three frozen language models (SmallThinker, Qwen3, Gemma‑3), the authors introduce Strategic Preference Optimization with KL‑anchored Equilibrium (SPOKE), which blends teacher‑relative value penalties, policy anchoring, risk constraints, and structured rationale supervision. SPOKE outperforms other methods on Kuhn and Leduc games, achieving the lowest mean exploitability and the highest six‑max top‑1 agreement while maintaining teacher‑consistent behavior.
By Phurinat Polasa, Thanapon Thanadunpremdet, Phatthanasak Kraiduang, Saksorn Ruangtanusak
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:2610.27588v1 Announce Type: new
Abstract: LLM agents have become capable kernel engineers on mainstream GPU stacks, but they struggle on emerging accelerators such as Ascend NPUs, whose program...
By Xi Chen, Ming Shen, Haotian Yan, Ruoyu Sun, Tianjun Yao
arXiv:2610.27645v1 Announce Type: new
Abstract: As computer-use agents (CUAs) move to long-horizon workflows spanning multiple applications, undetected errors can silently corrupt persistent state an...
By Xu Hu, Haoming Li, Yuxiang Wang, Jinxin Yang, Kangshuo Li, Licheng Wang, Dingyi Kang, Jiayu Song, Tianhao Wang, Zhanhua Hu, Xu Yang, Qiannan Li, Bingzhe Li, Feng Chen
arXiv:2610.27670v1 Announce Type: new
Abstract: This paper introduces a novel framework for efficiently evaluating in-game sound designs using multimodal learning and reinforcement learning. Sound de...
By Thai Nguyen Van, Ibrahim Khan, Mury Fajar Dewantoro, Pratch Suntichaikul, Hai Van Pham, Ruck Thawonmas
arXiv:2610.27756v1 Announce Type: new
Abstract: Vision-language models (VLMs) are exact on small sets and drift into estimation on larger ones, a profile reminiscent of children before they master co...
By Yingxin Jia, Hui Geng, Wenxin Yang, Mohan Zhang
arXiv:2610.27777v1 Announce Type: new
Abstract: Tool-using LLM agents increasingly answer e-commerce queries through multi-step tool interactions. The expected output of a tool call can shift between...
By Wenshuo Zhang, Yuquan Lin, Yushi Sun, Yishi Lin, Rui Sheng, Peng He, Han Gao, Wang Jikai, Yifeng Rong
GRADUS introduces a method for training large language model agents in policy-constrained tool environments by decoupling task design from solutions. It treats a fixed environment as a latent syllabus, extracting guarded behavioral operators and composing them into progressively harder tasks while using corpus-level feedback to target missing outcome combinations. The approach jointly calibrates tasks and solutions against the environment, turning verification into a recovery signal that preserves difficult examples, leading to significant performance gains on benchmarks such as $ au^2$-Bench and CAR-Bench.
By Qing Ping, Wei Yang, Panpan Xu, Johannes Kirmayr, Lukas Stappen, Junjie Tang, Jun Huan
arXiv:2610.27797v1 Announce Type: new
Abstract: 6G will be designed faster than it can be built: AI-driven search already proposes physical-layer algorithms at machine speed, while carrying a single...
By Eric Mittag, Lukas Brechtel, Maximilian Stark, Hans D. Schotten
COMPASS is an agentic router that uses a capability-oriented model profiling approach to select large language models (LLMs) efficiently. It follows a routing recipe, gathers performance and cost evidence through tools, and can inspect candidate answers before making a selection. In evaluations on LLMRouterBench and DeepSWE, COMPASS improves accuracy while reducing model‑call cost compared to specialized and trained routing baselines, and it can adapt to new models without retraining.
By Amin Rezaei, Pouya Parsa
arXiv:2610.27847v1 Announce Type: new
Abstract: Scaling the training of Large Language Model (LLM) agents typically requires a large collection of executable environments, whose construction, mainten...
By Juncheng Diao, Haibin Wang, Wei Shi, Yifan Ding, Qingbin Li, Rongxiang Weng, Xunliang Cai
arXiv:2610.28121v1 Announce Type: new
Abstract: Safe autonomous navigation of UASs requires tactical deconfliction to address conflicts that strategic coordination cannot prevent. This task is challe...
By Christopher Conrad, Deepak Panda, Weisi Guo, Antonios Tsourdos
arXiv:2610.28282v1 Announce Type: new
Abstract: Multimodal large language models (MLLMs) have substantially advanced chart understanding and reasoning, yet their performance remains highly uneven acr...
By Qi Wang, Peijie Wang, Jun Cao, Haoyang Tong, Xiao-Hui Li, Fei Yin, Cheng-Lin Liu
arXiv:2610.28361v1 Announce Type: new
Abstract: Long-horizon deep search requires LLM agents to iteratively decompose complex information needs, gather evidence from external sources, and maintain a...
By Zijun Dou, Kun Luo, Chaofan Li, Shuqi Lu, Zheng Liu
arXiv:2610.28484v1 Announce Type: new
Abstract: AI agents increasingly act on behalf of people or other agents, purchasing services, exchanging information and delegating work. What an agent must res...
By Carlo Ciliberto, Alessandro Rudi, Enrico Tassi
arXiv:2610.28560v1 Announce Type: new
Abstract: Visual anomaly detection aims to detect abnormal objects, while anomaly samples are often scarce during model training. Assembling multiple pretrained...
By Shangquan Sun, Wenjun Luo, Xu He, Yoonsoo Lee, Shijian Lu
arXiv:2610.24806v1 Announce Type: cross
Abstract: Despite breakthroughs in AI-driven molecular generation, existing discovery pipelines operate largely in silico, with wet-lab feedback reserved for v...
By Zachary Cutts, John Coller, Rachel Chen, Hua Tang, Mathias Bellaiche, Ryder Easterlin, Dina Zverinski, Petar Sirkovic, Avinatan Hassidim, Yossi Matias, Pooja Rao, Annalisa Pawlosky
arXiv:2610.24833v1 Announce Type: cross
Abstract: As AI agents become increasingly connected through networks and take on tasks that require negotiation and joint decision-making, a systematic approa...
By Jin Gao
arXiv:2610.26932v1 Announce Type: cross
Abstract: Large language models (LLMs) are increasingly deployed as autonomous agents in non-verifiable domains such as financial services, where they must int...
By Ashish Bansal, Hossam Amer, Keerat Kaur Guliani, Parinita Edke, Lesley Matheson, Leizer Teran
arXiv:2610.27162v1 Announce Type: cross
Abstract: In robot-assisted education, a robot must understand how to complete a task in order to effectively guide a student through it. We present a novel me...
By Lada Kudl\'a\v{c}kov\'a