The article discusses how large language models can now generate user interfaces from natural‑language descriptions, and proposes a framework that turns HCI design knowledge into machine‑readable ‘skills’ that guide on‑demand UI construction. By treating the dialogue between user and AI as a shared cognitive workspace, these skills encode established HCI principles (e.g., Nielsen’s heuristics, WCAG criteria) as editable, version‑controlled artifacts. The authors outline a research agenda that envisions HCI moving from heuristic checklists to a machine‑executable, open‑source craft.
By Nathan Conklin, Miranda Capra, Chris North
The paper introduces AdsCVR, a benchmark for e‑commerce cross‑video reasoning with 2,483 videos and 6,110 QA pairs across six reasoning dimensions. It proposes AdSeek, an agentic framework that actively selects visual and audio tools during multi‑turn exploration and uses an offline trajectory rectification mechanism to improve reinforcement learning. AdSeek achieves 74.30% accuracy on AdsCVR, outperforming its backbone by 27.90 percentage points and generalizes to the open‑domain CrossVid benchmark.
By Jinghan Zhao, Yiman Hu, Liang Wu, Jian Xu, Bo Zheng
The paper introduces VERSE, a Verified Self‑Evolving optimizer that enhances LLM agent harnesses by allowing the optimizer to test edits, replay failures, and perturb steps while tracking fixes and regressions. VERSE builds its own tools for failure analysis, verification, training audits, and workflow control, and uses this feedback to revise the harness’s prompts, skills, tools, hooks, and notes without changing model weights. In experiments across five executors and multiple languages, VERSE improves all evaluated harness optimizers, achieving higher accuracy on held‑out and out‑of‑distribution tasks compared to the strongest baselines.
By Zekai Wang, Yingqiang Ge, Zekun Wang, Hai Wang, Yuhui Xu, Joshua Frandsen, Shancong Fu, Ashia C. Wilson, Chandan K. Reddy
The paper examines how transformer policies, which process agents as ordered token sequences, perform in multi‑agent robot learning where agent teams are unordered. It finds that low permutation error can mask action collapse, where all agents choose the same action, and proposes additional diagnostics such as action diversity and same‑action fraction. Experiments show that while a PPO‑ID baseline avoids collapse, it remains order‑sensitive, and that strong equivariance regularization can still cause homogeneous behavior; a weak penalty improves robustness and preserves diversity for three‑agent teams, but four‑agent teams need much smaller regularization weights.
By Amit Thakur, Mukesh Singhal
APDMem is a hierarchical long‑term memory system for personalized LLM assistants that uses progressive disclosure to retrieve conversation history. It organizes memory into four layers—thematic summaries, personalized key facts, turn‑level evidence notes, and raw messages—and a controller reads from the top layer, drilling down only when necessary. This approach balances cost and fidelity, allowing simple queries to finish early while deeper inspection is triggered for complex or exact‑evidence requests, and a note synthesizer structures retrieved evidence before answer generation. Experiments on LongMemEval show APDMem performs strongly while accessing only 8% of the total conversations.
By Chin-Lun Fu, Anagha Kulkarni, Hong Ni, Behrouz Madahian
The paper reports the first demonstration of autonomous robotic navigation for endovascular brain‑computer interface access in the cerebral venous system. Soft Actor‑Critic controllers were trained in simulation to navigate from the right internal jugular vein to the superior sagittal sinus, and were evaluated both in silico and in vitro, achieving high success rates in simulation and 70% overall in physical runs. Online failure‑prediction models were also tested, detecting most failures with low false‑alarm rates, though calibration degraded when transferring from simulation to physical experiments.
By Harry Robertshaw, Weijie Qi, Nikola Fischer, Alejandro Granados, Thomas C. Booth, Sam E. John
The paper studies how language‑model agents adopt beliefs, measuring the probability that an agent accepts a claim based on the number of peers endorsing it. The adoption curve is sigmoid, indicating complex contagion, with a threshold influenced by the claim’s plausibility, the source’s reliability, and the agent’s disposition—dimensions that can be collapsed into a single coherence metric relative to the agent’s prior beliefs. In networks of AI agents, belief spread is stronger on clustered than random networks, and the system shows a bifurcating cascade window and hysteretic consensus that makes consensus difficult to reverse once formed.
By Tathagata Banerjee, Nima Moghaddas
The paper presents a geometry-driven framework for beam management in high-frequency massive MIMO systems. It constructs an offline virtual base station database using 3D LiDAR point clouds and location data to model dominant reflection paths, enabling a coarse channel reconstruction. A VBS-assisted orthogonal-pilot scheme and a dual-agent dueling double deep Q-network are then employed to refine beam estimates and perform coordinated beam selection, yielding improved training efficiency and performance over existing baselines.
By Yijie Bian, Wei Guo, Jie Yang, Shenghui Song, Jun Zhang, Shi Jin, Khaled B. Letaief
arXiv:2307. 07113v2 Announce Type: replace-cross Abstract: In this paper, we consider the decentralized, stochastic nonconvex strongly-concave (NCSC) minimax problem with nonsmooth regularization terms on both primal and dual variables, wherein a network of $m$ computing agents collaborate via peer-to-peer communications.
By Gabriel Mancino-Ball, Muhammad Khan, Yangyang Xu
The paper introduces a data‑driven control framework that uses surrogate modeling and reinforcement learning to optimize target polarization in nuclear physics scattering experiments. By training Gaussian process and multilayer perceptron models on operational data from the APOLLO cryogenic target system, the authors demonstrate that Gaussian processes provide calibrated uncertainty estimates and better handle distributional shifts. Embedding the surrogate model in a simulation environment, a reinforcement learning agent trained with a lower‑confidence‑bound reward achieves nearly a 2× improvement over manual operator tuning.
By Armen Kasparian, Torri Jeske, Monibor Rahman, Chris Keith, James Maxwell, Thomas Britton, Malachi Schram, David Lawrence
The paper introduces AURA, an LLM-powered mask‑reconstruct framework for anonymizing text while preserving utility. It decouples privacy localization from utility‑preserving reconstruction and uses adversarial checks to select candidates. Experiments on real‑user interview transcripts show that AURA achieves the lowest agentic re‑identification rates among non‑DP methods and retains more contextual utility than prior LLM anonymizers.
By Ziwen Li, Jianing Wen, Tianshi Li
The paper introduces a self‑supervised framework called software‑in‑the‑loop reconstruction (SWR) that automatically generates reference outputs and verification targets for terminal agents by leveraging existing scientific software workflows. SWR executes multiple input configurations, partitions cases into public observations and hidden evaluations, and uses a hierarchical verifier to assess agent‑generated programs against hidden workflow outputs. The authors demonstrate the approach on 500 workflows across six domains, achieving significant performance gains on the Terminal‑Bench 2 benchmark with the Qwen3.8‑Max and Qwen3.8‑27B models.
By Zhongzhi Li, Yucheng Shi, Zongxia Li, Junyao Yang, Ruhan Wang, Yu Wang, Jingyuan Huang, Jichao Yu, Ninghao Liu, Haitao Mi, Leowei Liang
OmniAct3D is a framework that adapts Vision Foundation Model (VFM) detectors trained on perspective images to equirectangular projection (ERP) panoramic images for 3D detection. It introduces the ERP-Ray Geometry Adapter (ERGA-Ray) to model spherical rays, the Visual-Action Reasoning Chain (VARC) to ground hypotheses in panoramic evidence, and the Appearance-Guided Heading Expert (AGHE) to recover high‑resolution heading cues. The method achieves a 2.96 NDS improvement on Spheriverse and a 24.87 mAP gain on PanoMMOcc, while preserving most of the baseline’s performance across configurations.
By Runtong Wu, Fei Teng, Di Wen, Guoqiang Zhao, Kunyu Peng, Kailun Yang
The paper introduces the concept of world editing—intervening on an existing executable world while preserving unchanged properties—and defines intervention depth as a measure of how strongly edits affect world entities, dynamics, and systems. It presents IGMWorld and IGMBench, a benchmark of 110 tasks across Minecraft and Terraria, evaluating edits through deterministic executability, behavioral, preservation, and visual checks. Frontier coding agents achieve up to 78.2% task-level success and 94.8% criterion-level success, though reliability drops with deeper interventions and visual consistency remains low.
By Max Ku, Nok-Kan Law, Yu-Chien Tang, Shih-Ying Yeh, Ping Nie, Andy Zheng, Tat Hei Lai, Fei-Yueh Chen, Nikko Yu, Wei-Chieh Sun, Suzy Huang, Chiao-Wei Hsu, Chih-Chuan Huang, Chak-Wing Mak, Ho Yin Sam Ng, Edisy Kin Wai Chan, Min-Hung Chen, Ho Kei Cheng
The paper introduces HACKTRACE, a behavior‑supervised monitor that detects reward hacking in code‑generation agents by analyzing the agents’ internal states during multi‑turn coding. Using 173,561 annotated trajectories from Qwen3‑8B, the authors show that supervising shortcut behavior independently of exploit success markedly improves detection, achieving a mean per‑problem AUC of 0.997 with minimal monitoring overhead. HACKTRACE also serves as an inexpensive signal for reinforcement learning, dramatically reducing cheating rates while preserving correct solutions.
By Hao Jiang, Xin Li, Annan Wang, Yichi Zhang, Weisi Lin
The paper proposes using Small Language Models (SLMs) as a task‑tool relevance classifier to verify each tool call made by AI agents. By evaluating every selected tool against the assigned task, the SLM provides a relevance signal that can be used for downstream enforcement. The authors introduce a novel dataset of multi‑tool tasks across distinct Model Context Protocol servers and explore prompt‑optimization, supervised fine‑tuning, and reinforcement learning (GRPO) to optimize and specialize the SLMs.
By Chiara Troiani, Arash Salarian, Majed El Helou, Benjamin Ryder, Jean Diaconu, Herv\'e Muyal, Marcelo Yannuzzi
LPS-Bench is a benchmark designed to evaluate the safety awareness of computer‑use agents (CUAs) in long‑horizon planning tasks that involve tool workflows. It uses a template‑guided multi‑agent pipeline to generate user instructions, simulated toolkits, and case‑specific safety criteria, followed by human review, allowing scalable expansion without building separate application environments. The benchmark includes 570 cases from 65 scenarios across seven task domains and nine planning‑risk types, and an LLM‑based evaluator assesses tool choices, arguments, and responses throughout execution. Evaluations of 13 LLM agents show persistent safety failures in both benign and adversarial settings, with prompt‑based interventions providing only model‑dependent improvements.
By Tianyu Chen, Chujia Hu, Dongrui Liu, Xia Hu, Wenjie Wang
TerrainForge is a framework that generates counterfactual road geometry edits—such as crests, sags, speed humps, and friction changes—by linking a unified road model to four‑wheel vehicle dynamics. It propagates these edits through vehicle motion, camera viewpoint, and inter‑vehicle clearance, and validates the dynamics against CarSim and reconstructed Waymo scenes. The system also simulates thousands of road edits to train a surrogate model that predicts changes in the ego vehicle’s terminal gap, enabling efficient screening of safety‑critical edits.
By Yang Chen, Yicheng Zhu, zhenning Li, Tao Li, Zilin Bian
World Action Planner is a robot planning system that uses an action-conditioned world model to search for and compose executable action plans. The agent performs a coarse-to-fine search: first a global action optimization over imagined rollouts to spot potential failures, then a local action search comparing neighboring candidates to pick the best action. In compositional long-horizon tasks, novel object layouts, and real‑robot planning without expert demonstrations, it consistently outperforms state‑of‑the‑art end‑to‑end generalist policies and VLM planners.
By Xiangcheng Zhang, Runhan Huang, Yilun Du
The paper proposes a scalable traffic modeling approach that uses a single representative large language model (LLM) agent for each homogeneous traveler group, rather than one LLM per traveler. The representative agent maintains a mixed strategy over routes, updates it daily based on positive reinforcement signals, and uses a tunable step size to adjust its strategy. This design improves scalability, stabilizes learning, and produces interpretable dynamics that reproduce realistic behavioral patterns such as the decoy effect and income‑based willingness‑to‑pay differences.
By Hanlin Sun, Jiayang Li