The paper introduces a disagreement‑aware dynamic facial expression recognition framework that directly learns from raw annotator vote vectors using a Dirichlet‑Multinomial likelihood, preserving both predictive mean and scale‑sensitive supervision. It adds an ambiguity head to estimate annotation entropy for unseen clips and employs a Chow‑style reject rule that integrates ambiguity, vacuity, temporal instability, and input quality for selective prediction. On the DFEW benchmark, the method maintains recognition accuracy while cutting expected calibration error by 30 % and area‑under‑risk‑curve by 15 %, with predicted ambiguity correlating 0.52 (Spearman) with true annotation entropy, and these gains transfer to FERV39k and hold under identity‑ and movie‑disjoint splits.
By Yiming Wang, Frederick W. B. Li, Jingyun Wang
The survey "World-Action Models for Robot Learning and Control" reviews recent advances in coupling future world prediction with executable action generation for robots in open environments. It clarifies the scope of World-Action Models (WAMs) relative to conventional world models, model-based RL, and Vision‑Language‑Action policies, and organizes existing methods through a unified taxonomy covering representations, transition modeling, action interfaces, architectures, training pipelines, data modalities, and scaling strategies. The paper also surveys applications in manipulation, navigation, and autonomous driving, summarizes datasets, benchmarks, and metrics, and discusses key challenges such as action alignment, spatial consistency, long‑horizon memory, and efficient inference.
By Zuxing Lu, Hongjia Zhai, Guanzhi Wang, Huajian Zeng, Jiaqi Yang, Jingyu Liu, Lei Cheng, Yuantai Zhang, Yuheng Qiu, Zezhou Cheng, Ivan Laptev, Danfei Xu, Benjamin Riviere, Giuseppe Loianno, Eric Xing, Xingxing Zuo
The paper introduces ImpossibleRubrics, a benchmark of 169 impossible tasks designed to test the robustness of language‑model‑generated rubrics as reward signals. Each task is paired with a verifiable oracle certificate that defines what constitutes an honest answer, and the benchmark includes 48 answerable controls. Experiments show that many rubric generators are exploited frequently—up to 36% on a stress cut—highlighting a significant gap in rubric quality rather than task difficulty, and that generic rubrics can be more vulnerable than tailored ones.
By Bowen Qin, Yi Xie, Yesheng Liu, Xi Yang
The paper introduces Decision‑Relevant Fresh Comparison (DRFC), a method for decentralized bandit systems with heterogeneous agents whose local reward changes may not affect the global best action. DRFC gathers balanced samples from all agents and only switches the common best arm when fresh global evidence indicates a change, yielding a dynamic regret bound that does not depend on the number of local changes. An anytime‑valid sliding‑window extension further handles gradual drift, and experiments on synthetic, semi‑real, and MovieLens‑1M data demonstrate that DRFC ignores decision‑irrelevant local changes while the extension avoids false switches.
By Zhaojun Peng
OmniHarness is a framework that enables generalizable visual generation by learning symbolic policies from verified executions. It abstracts shared procedures and applicability conditions, allowing these policies to be instantiated, adapted, and composed for new tasks while keeping model parameters fixed. The system uses intermediate verification for refinement, self-directed inquiry to generate practice tasks, and continuous feedback to expand capabilities, achieving strong results on multiple benchmarks and outperforming baselines on Creative tasks.
By Xu Xu (Beihang University), Jinxiu Liu (The Chinese University of Hong Kong), Zhangbo Qiao (Beihang University), Jiaxing Lu (Beihang University), Xiangyu Zhang (Beihang University), Yubin Gu (National University of Singapore), Fangwei Ning (Beihang University), Yan Shi (Beihang University)
MUUNRiver-Bench is a diagnostic benchmark for music retrieval that uses natural‑language instructions to define relevance for reference‑audio queries. It contains 3,440 tracks across 13 genres and 116 sub‑genres and covers seven tasks such as similar‑music, style‑preserving lyric‑rewriting, cover, and segment retrieval. Experiments with six models in eight configurations show that acoustic encoders favor local identity while text‑aligned encoders favor semantic relations, and that instruction‑aware and audio‑text fusion systems do not consistently outperform their backbones.
By Zhancheng Guo, Congren Dai, Shangda Wu, Jianhuai Hu, Danni Zhao, Xiaobing Li, Maosong Sun
RAG-CT is a defense mechanism designed to protect Retrieval-Augmented Generation (RAG) systems from leaking personally identifiable information (PII). It works by detecting malicious queries through analysis of entropy and margin distributions, applying a score-based detection method. Experiments across four attack strategies and four baseline defenses on two datasets show that RAG-CT significantly reduces PII leakage while outperforming existing defenses, all without altering the underlying LLM or retriever.
By Xingyu Lyu, Jiayimei Wang, Jianfeng He, Ning Wang, Yidan Hu, Yimin Chen
Auto-HSI is a system that creates personalized human‑swarm interaction interfaces on demand using large language models to automatically generate code from natural language descriptions and gesture demonstrations. The prototype enables untrained operators to control a swarm of 50 simulated robots with one‑ or two‑hand gestures, allowing teleoperation of motion, formation shape, and shape deformation. Experiments demonstrate the system’s gesture tracking, code generation, and live operation capabilities, including real‑time updates and deployment on real robots.
By Alessandro Nazzari, Nathan Cerisara, Dorian Tonnis, Raina Zakir, Lorenzo Labarile, Weixu Zhu, Marco Dorigo, Mary Katherine Heinrich
The paper introduces ASURRE, a benchmark dataset for detecting AI‑assisted responses in online surveys. It evaluates how different LLM usage strategies—ranging from full generation to persona‑grounded agentic completion—affect the performance of existing machine‑generated text detectors. The study finds that while naive AI usage is easily detected, more sophisticated persona‑grounded agents approach chance performance, yet still leave identifiable behavioural traces that can be aggregated to improve detection.
By Qizhou Wang, Bogdan Mamaev, Christopher Leckie
PARSA‑Bench is the first dedicated benchmark for evaluating large audio‑language models on Persian, addressing unique challenges such as classical poetry, traditional music, and code‑switching. It comprises 16 tasks—10 of which are new—covering speech understanding, paralinguistic analysis, and culturally grounded audio reasoning. Across most tasks, text‑only baselines outperform audio‑based models, indicating that audio understanding remains the main limitation, except for Persian poetry where prosody provides additional information that audio beats text.
By Mohammad Javad Ranjbar Kalahroodi, Mohammad Amini, Parmis Bathayan, Heshaam Faili, Azadeh Shakery
The paper investigates multi‑hop question answering systems and identifies two distinct failure modes: retrieval failures, where the necessary passage is not retrieved, and extraction failures, where the passage is retrieved but the required fact cannot be extracted—a phenomenon termed the fact‑grounding gap. Across three standard benchmarks, extraction failures account for nearly half of all per‑hop deficiencies and are invisible to standard retrieval metrics, remaining unresolved by retrieval‑only interventions. The study shows that these two bottlenecks require different solutions, a distinction currently missing from evaluation practices.
By Kevin Mo, Nathan Mo, Richard Zhu
The paper explores whether CLIP embeddings can detect AI-generated images by using a frozen CLIP model to extract visual embeddings and training lightweight classifiers on top. On the CIFAKE benchmark, the approach achieves 95% accuracy without language reasoning, and 85% accuracy after few-shot adaptation with 20% of the data. Certain image types, such as wide-angle photographs and oil paintings, remain challenging, highlighting unexplored difficulties in AI-generated image classification.
By Ziyang Ou
The paper introduces ATRBench, a benchmark that measures the proactivity gap in long‑lived LLM agents by evaluating their ability to ask for user preferences that are not needed immediately but may be useful in future sessions. It defines the Ask‑to‑Remember (ATR) task, where agents must decide whether to request a reusable preference now, and shows that current state‑of‑the‑art agents perform significantly below an oracle. The study identifies preference acquisition as the main bottleneck and provides a diagnostic framework for improving agent proactivity.
By Bin Wu, Guanyun Zou, Bingbing Wang, Huan Zhao, Chuan Shi
The paper introduces a new training framework for Visual Question Answering that leverages counterfactual contrastive learning to mitigate language bias and improve out‑of‑distribution generalization. It comprises a three‑stage curriculum for stable optimization, an enhanced Batch‑Contrastive loss for discriminative feature learning, and two regularizers—Answer‑Contrastive and Gradient‑Discrepancy—to refine predictions and enforce causal visual grounding. The resulting model attains 61.64% accuracy on the bias‑sensitive VQA‑CP v2 benchmark while maintaining 62.80% on the standard VQA v2 dataset, achieving a small generalization gap of 1.16%.
By Truong-Binh Duong, Thanh-Ngan Tran, Ngoc-Thao Nguyen, Bac Le
arXiv:2609.16393v1 Announce Type: new
Abstract: We introduce ParsHate, a manually annotated dataset of 10,000 Persian tweets spanning 2013-2022, representing the first decade-long benchmark for hate...
By Zahra Bokaei, Walid Magdy, Bonnie Webber
arXiv:2605.22611v2 Announce Type: replace
Abstract: Antimicrobial stewardship (AMS) is critical in pediatric intensive care units (PICUs), where diagnostic uncertainty often drives broad-spectrum ant...
By Niklas Raehse, Luregn J. Schlapbach, Daphn\'e Chopard
arXiv:2602.00532v2 Announce Type: replace-cross
Abstract: Constraint handling is central to constrained black-box optimization (BBO), where objective improvement and feasibility restoration often pro...
By Sijie Ma, Zeyuan Ma, Yue-Jiao Gong, Ran Cheng
arXiv:2609.16626v1 Announce Type: new
Abstract: Virtual try-on (VTON) enables customers to visualize how fashion products appear when worn and has become an important technology for online shopping....
By Xinlei Niu, Peixia Li, Jun Wang, Chenchen Xu, Jiayu Yang, Jing Zhang, Pulak Purkait, Hongdong Li
arXiv:2609.16772v1 Announce Type: new
Abstract: A Digital Surface Model (DSM) is a fundamental geospatial data product for representing the elevation of the Earth's surface. Recently, 3D Gaussian Spl...
By Jie Yang, Yingdong Pi, Qiyan Luo, Xiaoyu Wang, Lekang Wen, Mi Wang
arXiv:2609.17026v1 Announce Type: new
Abstract: Continual learning must balance the learning of new knowledge with the retention of previously learned knowledge to incrementally learn tasks from a da...
By Yunxiang Fu, Meng Lou, Zicheng Liao, Yizhou Yu