OmniVChat defines a native audio‑visual dialogue task where models receive raw audio and video from a user and produce text responses, eliminating the need for separate text queries or speech recognition. To address data scarcity and evaluation challenges, the authors introduce OmniVChat‑Studio, a multi‑agent engine that synthesizes single‑ and multi‑turn dialogues, and OmniVChat‑Bench, a benchmark assessing models across five dialogue ability categories. They also propose OmniVChat‑RL, a reinforcement‑learning reward that balances reply correctness, efficiency, and style, and demonstrate that training Qwen3‑Omni‑Instruct with this reward on synthesized data improves performance on both synthetic and human‑recorded benchmarks.
By Haolin He, Yunfei Chu, Qi Chen, Wen Huang, Yuan Feng, Muzhi Zhu, Zheqi Dai, Haoning Xu, Dongchao Yang, Chunyat Wu, Zining Liang, Zhengxi Liu, Xiquan Li, Xie Chen, Xize Cheng, Qize Yang, Jin Xu, Qiuqiang Kong
CIPL (Channel Inversion for Privacy Leakage) is a channel-aware framework designed to evaluate black-box privacy leakage in large language model agents. It models the leakage process through stages of sensitive source, selection, assembly, execution, observation, and extraction, assessing how selected sensitive units become attacker-recoverable outputs. Experiments across memory, retrieval, and tool-mediated targets, plus a live-agent case study, reveal that recoverability depends on factors beyond storage labels, such as observation surface, prompt alignment, retrieval depth, and provider behavior, and that a semantic audit can uncover disclosures missed by exact matching.
By Tao Huang, Guosen Wu, Guolong Zheng, Jiayang Meng, Chen Hou, Xu Yang, Xuechao Yang, Feng Xia
The paper introduces Bayesian Chronicle Agents (BCA), a lightweight belief layer that separates an LLM agent’s internal stance from its outward speech. Each stance is represented as a probability updated via a single Bayesian step per utterance, with a single prior‑strength parameter κ controlling stubbornness. By sweeping κ, the authors generate three controllable opinion‑dynamics regimes—consensus, persistent disagreement, and committed‑minority influence—matching Friedkin–Johnsen theory and demonstrating recoverable, auditable belief states across models.
By Hafsa Akbar, Daniel Platnick, Marjan Alirezaie, Hossein Rahnama
The paper introduces a compositional continual learning benchmark for world models in robot manipulation, designed to isolate knowledge reuse from learning speed and capacity. Tasks are curated to combine previously seen action and perception components, allowing analysis of how different modalities affect reuse. Experiments show that modular world models better balance reuse and forgetting than conventional methods, yet none fully solve the challenge, highlighting the need for models explicitly built to reuse knowledge without forgetting.
By Haoyu Zhou, Joe Watson, Anson Lei, Ingmar Posner
The study evaluates six advanced language models on a two‑agent <log_2 N>‑Questions game, where a questioner must identify a secret Wikipedia paragraph using exactly <log_2 N> binary questions answered by an agent that only sees the target. Across 408 games, win rates decline geometrically with horizon length (p≈0.93), and per‑round failure rates remain flat, indicating that errors compound because more rounds must succeed rather than because individual rounds become harder. Adjudication reveals that losses stem from single‑agent answer errors and discrimination failures, with Claude Opus 5 lagging due to high false‑negative rates, while the top five models cluster closely; maximizing information gain requires structural partitioning, and neither reasoning‑token usage nor API cost correlates with success.
By Peter Potash
The paper introduces PARTS, a real‑world subtask reinforcement learning framework that fine‑tunes a pretrained robot policy by focusing on critical bottleneck subtasks while keeping the base policy frozen. It uses agent‑generated selectors and success verifiers to provide local rewards, enabling learning even when full‑task successes are rare. Experiments on bimanual YAM and single‑arm Franka robots show that PARTS raises complete‑task success from 32% to 61% and from 50% to 95%, respectively, with only tens of minutes of real‑world RL rollouts and minimal human intervention.
By Sichang Su, Benjamin Yang, Zhiyun Deng, Boyuan Liang, Yip Fun Yeung, Zelin Wang, Lingfeng Sun
ZYT-World is a real‑time, controllable world model designed for closed‑loop autonomous‑driving simulation. It generates a full fisheye‑pinhole camera rig at native resolution, using Plucker adapters for camera geometry, ego‑motion adaptive layer normalization for motion control, and a lightweight pixel‑aligned layout to condition traffic participants. The model achieves high fidelity to a 40‑step teacher while being over 100 times faster, and includes an implicit‑memory module that preserves place‑specific evidence for long‑horizon stability.
By Boni Hu, Xiong Wei, Haoming Huang, Yong Huang, Chenbo Wang, Yi Yang, Jiancheng Wang, Ruicheng Zhu, Zhimin Yang, Guanglai Liu, Qiaowan Jin, Dongzhuo Wang, Haiwei Kuang, Jiajun Fan, Yue Wu, Jiaxin Wei, Hao Sun, Feihong Yan, Wei Bi, Kaixuan Wang, Zichao Guo, Xiaozhi Chen
HERMES is a holistic end‑to‑end multimodal driving framework that incorporates long‑tail semantic knowledge into trajectory planning for autonomous vehicles. It uses a foundation‑model‑assisted annotation pipeline to build Long‑Tail Scene Context and Long‑Tail Planning Context, capturing hazard‑centric scene information, maneuver intent, and risk‑aware guidance. A Tri‑Modal Driving Module then fuses multi‑view visual observations, historical ego‑motion, and long‑tail semantic instructions to generate intent‑ and risk‑aware trajectories, achieving consistent performance gains on a large‑scale real‑world long‑tail driving benchmark.
By Weizhe Tang, Junwei You, Jiaxi Liu, Zhaoyi Wang, Rui Gan, Zilin Huang, Feng Wei, Bin Ran
arXiv:2609.21192v1 Announce Type: new
Abstract: Organizations deploying agentic artificial intelligence must determine more than whether a model is trustworthy; they must establish what to validate,...
By John Cuneo, David Chun, Gaurav Khanna
arXiv:2609.21940v1 Announce Type: new
Abstract: Long-term memory is essential for large language model (LLM) agents to maintain consistency and personalization over extended interactions. Existing me...
By Zijie Cao, Xijun Qu, Zhicheng Gu, Xiaoshu Chen, Duanyang Yuan, Yanning Hou, Sihang Zhou, Jianxing Gong, Jian Huang, Yang Mei
arXiv:2609.21117v1 Announce Type: cross
Abstract: AI productivity is often measured by task completion time, economic value, or improvements in outcome quality. However, these measures usually treat...
By Saki Imai, Mert \.Inan, Malihe Alikhani
arXiv:2609.21562v1 Announce Type: cross
Abstract: Coding agents can modify and test code across large software projects. Game development is a domain where agents must implement gameplay rules. A gam...
By Xinyu Che, Yunfei Ge, Shihao Li, Yanchen Liu, Hang Yan, Xinping Lei, Yanghai Wang, Zixuan Dong, Yifan Yao, Qianqian Xie, Letian Zhu, Jiaheng Liu
arXiv:2609.21967v1 Announce Type: cross
Abstract: We introduce NemotronLabs VoiceChat, an open full-duplex speech-to-speech model with native tool-calling capabilities. NemotronLabs VoiceChat combine...
By Jagadeesh Balam, Travis Bartley, Edresson Casanova, Sanjay Chauhan, Chen Chen, Zhehuai Chen, Zijia Chen, Francesco Ciannella, Slyne Deng, Mikyas Desta, Harishchandra Dubey, Slim Essid, Nourchene Ferchichi, Boris Ginsburg, Mariana Graterol Fuenmayor, Negar Habibi, Kevin Hu, Anand Joseph, Viraj Karandikar, Myungjong Kim, Viacheslav Klimkov, Seelan Lakshmi Narasimhan, Lily Lee, Jason Li, Eileen Long, Ameya Mahabaleshwarkar, Aditya Malte, Adi Margolin, Sasha Meister, Valentin Mendelev, Oluwatobi Olabiyi, Ankita Pasad, Yifan Peng, Elena Rastorgueva, Jayda Ritchie, Jason Roche, Nikhil Srihari, Yuanhang Su, Yoshi Suhara, Viet Anh Trinh, Jinhan Wang, Piotr Zelasko, Hui Wang, Puhui Meng, Chaosen Zhang, Yunsheng Liu, Shawn Wang, Wenjing Li, Zhonglei He
arXiv:2603.16086v2 Announce Type: replace-cross
Abstract: While recent Vision-Language-Action (VLA) models have begun to incorporate audio, they typically treat sound as static pre-execution prompts...
By Chang Nie, Tianchen Deng, Guangming Wang, Zhe Liu, Hesheng Wang
arXiv:2604.27167v3 Announce Type: replace-cross
Abstract: On the named Prisoner's Dilemma under direct prompting, three larger instruction-tuned models, Llama-3-70B, Qwen2.5-32B, and Qwen2.5-72B, loc...
By Paraskevas V. Lekeas, Giorgos Stamatopoulos
arXiv:2609.21108v1 Announce Type: new
Abstract: Deep reinforcement learning (DRL) has achieved strong performance across a wide range of continuous-control problems. These continuous-control policies...
By Sachini Weerasekara, Sagar Kamarthi, Jacqueline Isaacs
arXiv:2609.21533v1 Announce Type: new
Abstract: LLM-based multi-agent systems generate collaboration traces that record how agents plan tasks, verify intermediate results, and repair failures. Reusin...
By Kairui Yang, Minghao An, Xunkai Li, Ziheng Yi, Zekai Chen, Guangyuan He, Rong-Hua Li
arXiv:2609.21945v1 Announce Type: new
Abstract: Urban transportation networks present complex optimization challenges spanning calibration of high-fidelity simulators and real-time operational contro...
By Adewumi Augustine Adepitan, Christopher J. Haruna, Oluwasegun Adegoke, Ayooluwatomiwa Ajiboye, Oluwatobi Oluwasakin
arXiv:2609.21281v1 Announce Type: cross
Abstract: Embedding-based retrieval on user-generated content at the trillion-document scale exposes a sharp conflict between two production demands: deep, exp...
By Hao Fu, Jichao Sun, Baiting Zhu, Qiaoling Liu, Yan Shi, Cheng Lu, Liu Liu, Yubo Wang, Xin Yao, Xiangyu Niu, Xu Dong, Wenhan Lyu, Chiyao Shen, Yinjie Huang, Minglei Chen, Shuai Ding, Li Fan, Xiao Kong
arXiv:2609.21944v1 Announce Type: cross
Abstract: In recent years, quantum game theory has gained significant attention as a framework for studying decision-making in multi-agent systems using quantu...
By Alireza Habibi, Luis F. Abanto Leon, Setareh Maghsudi