The study examines how vision‑language models handle multi‑turn pragmatic interpretation in iterated reference games, where participants repeatedly identify novel referents using language. Researchers compared human performance with that of several models, manipulating context by varying its amount, order, and relevance. While humans consistently performed well, the models could use prior context but struggled to build relevant context for effective interpretation, indicating missing core skills for efficient linguistic collaboration.
By Alvin Wei Ming Tan, Ben Prystawski, Veronica Boyce
The study evaluates whether multimodal large language models (MLLMs) can produce open‑ended aesthetic critiques comparable to humans. Eight open‑weight MLLMs (7 B–397 B) and GPT‑5.5 were tested on 1,227 r/photocritique posts under various prompts, revealing that reference‑based similarity metrics often misrepresent model performance, while shorter critiques and image‑omission had limited impact. Human judges and annotators found the models’ critiques largely different from human ones, noting that models tend to be overly comprehensive and repetitive rather than selective and specific.
By Sajjad Ghiasvand, Maryam Amirizaniani, Haniyeh Ehsani Oskouie, Mahnoosh Alizadeh, Ramtin Pedarsani
arXiv:2608.30498v1 Announce Type: new
Abstract: Multimodal Large Language Models (MLLMs) have shown remarkable success in STEM domains, where progress is often driven by vertical, step-by-step deduct...
By Qi Li, Zhaojie Kang, Yingjie He, Zheng Lin, Hao Zhang, Guangxin Wu, Yan Gong, Rong Fu, Jianyuan Ni
The study demonstrates that a simple, sequence-only approach using 330 interpretable descriptors and the TabPFN tabular foundation model can outperform complex multimodal deep learning methods for multi-label antimicrobial peptide activity prediction. On the ESCAPE benchmark (82,359 peptides, five labels), a label‑powerset TabPFN model achieved a mean average precision of 77.8%, surpassing the previous best of 72.1%. The approach also shows that predicted structure is unnecessary, that a small set of global physicochemical scalars can recover most performance, and that modeling label dependence benefits rare activities and informs assay prioritization.
By Raunak Kumar, Anuj Pal, Dhruvi Solanki, Parikshit Pareek, Juhi Singh, Jitin Singla
arXiv:2506.11635v2 Announce Type: replace-cross
Abstract: Credit card fraud mitigation plays a significant role in modern society. While fraud detection systems are essential, they often struggle to...
By Shaun Shuster, Eyal Zloof, Asaf Shabtai, Rami Puzis
arXiv:2606.17446v2 Announce Type: replace-cross
Abstract: Simulation enables scalable robot data collection, but raw 3D assets provide only geometry, lacking the semantic, interactive, and physical k...
By Haoran Lu, Mutian Shen, Shuyang Yu, Yu Xiao, Songling Liu, Jianshu Zhang, Shang Wu, Yue Chen, Guo Ye, Jiayi Wang, Zhaoran Wang, Han Liu
The paper introduces Image Bundle Composition (IBC), a new paradigm that moves beyond point-wise image matching to dynamically assemble cohesive image bundles from large, unstructured photo collections. It presents IBCBench, a benchmark with over 109,000 images and 667 verified queries, and proposes BundleWeaver, an agentic framework that uses a Large Language Model for relational role discovery and a Vision‑Language Model for bundle verification. Experiments show that BundleWeaver outperforms existing embedding and decompose‑and‑rerank methods, underscoring the importance of relational composition over atomic scoring.
By Rong Shan, Tianyi Xu, Congmin Zheng, Wenteng Chen, Jiachen Zhu, Junjie Wu, Teng Wang, Weiwen Liu, Changwang Zhang, Weinan Zhang, Jun Wang, Jianghao Lin
CogEvol is a family of models designed to generate learning environments—structured JSON slides or interactive HTML pages—from course briefs in a single pass. In production, it completes a slide in a median of 17 seconds and an interactive page in 59 seconds, vastly faster than multi‑turn agent scaffolding. The system enforces reliability through a production‑grounded data pipeline that produces over 53,000 verified SFT samples and a hybrid rule‑plus‑VLM reward that guides GRPO‑based reinforcement learning, with a 27B model achieving high quality scores while using far fewer parameters than flagship coding models.
By Shangqing Tu, Daniel Zhang-Li, Yucheng Wang, Shiyu Gan, Yanpeng Wang, Huiqiang Rong, Mofei Chen, Shen Yang, Yini Chen, Yinuo Duan, Haoxuan Li, Binglin Liu, Ye He, Danqi Zheng, Zhanxin Hao, Yuxuan Wu, Mengting Tao, Yuqiu Liu, Jifan Yu, Juanzi Li, Bin Xu, Lei Hou, Huiqin Liu, Yu Zhang
arXiv:2605.31556v2 Announce Type: replace-cross
Abstract: Alignment teaches vision-language models (VLMs) to avoid expressing demographic biases, and when gender is clearly visible they largely succe...
By Arnau Marin-Llobet, Simon Henniger, Mahzarin R. Banaji
arXiv:2608.29304v1 Announce Type: new
Abstract: Translating electroencephalography (EEG) into functional magnetic resonance imaging (fMRI) is important for medical neuroimaging, clinical brain-state...
By Xiangyu Liu, Zeting Yan, Zhitong Yin, Boyang Li, Xi Zhang
arXiv:2608.29483v1 Announce Type: cross
Abstract: Modern Vision-Language Models (VLMs) perform well above the human baseline in image geolocalization, a task critically important in disaster response...
By Arka Mukherjee, Soham Roy, Kartikeya Trivedi, Shreya Ghosh
RoboPhys-3D is a 3D‑grounded embodied world model benchmark built on RoboTwin 2.0, featuring 50 manipulation tasks, 5,000 episodes, and 25,000 multi‑view ground‑truth videos. It evaluates video world models by processing both generated and ground‑truth videos through the same 3D reconstruction pipeline, allowing the separation of reconstruction‑induced from generation‑induced errors. The benchmark defines 50 metrics across four sub‑dimensions—pixel fidelity, 3D geometry consistency, state understanding, and task completeness—and introduces the Average Full Score and RoboPhyscore for holistic assessment, with RoboPhyscore showing strong correlation with human judgments.
By Tianyi Wang, Jiazhou Chen, Yiming Xu, Xiangyu Li, Tianyi Zeng, Chih-Hsien Chou, Ning Lu, Liang Peng, Junfeng Jiao, Christian Claudel
arXiv:2509.17930v3 Announce Type: replace-cross
Abstract: Multilingual translation suffers from computational redundancy, especially when translating into multiple languages simultaneously. In additi...
By Yiwen Guan, Jacob Whitehill
The paper introduces a new framework for predicting grapevine cold hardiness that learns transferable latent representations capturing region-specific variation. By inferring embeddings from cultivar descriptions, growing region text, and limited historical data, the method supports zero‑shot and few‑shot transfer to unseen regions. Experiments across six North American regions show that this approach outperforms existing models, delivering more accurate predictions and better performance in data‑scarce areas.
By William Solow, Paola Pesantez-Cabrera, Markus Keller, Lav Khot, Sandhya Saisubramanian, Alan Fern
arXiv:2608.29395v1 Announce Type: new
Abstract: Vision-language models such as CLIP and SigLIP provide strong zero-shot recognition, but their predictions can degrade when deployed on target data tha...
By Pedram MohajerAnsari, Amir Salarpour, Run Wang, Mert D. Pes\'e
arXiv:2608.29590v1 Announce Type: new
Abstract: We propose a societal bias evaluation method for large vision-language models (LVLMs) in the era of strong safety guardrails. Existing benchmarks rely...
By Yusuke Hirota, Michael Ross Boone, Arun George Zachariah, Jibin Rajan Varghese, Yu-Chiang Frank Wang, Boyi Li, Ryo Hachiuma
LiteSearch‑VL demonstrates that distilling released agent trajectories into small vision‑language backbones can transfer the agent’s behavioral contract, enabling a 2B model to produce usable answers in 28.4% of cases on multimodal benchmarks. The approach uses parameter‑efficient LoRA adapters and synthetic step‑level preferences derived from GPT‑5 hard negatives to refine tool use and query quality. While synthetic preference learning and tool distillation provide incremental improvements, the main bottleneck identified is answer verification rather than search depth.
By Saeed Khaki, Nima Safaei, Kamal Ginotra
arXiv:2608.29802v1 Announce Type: new
Abstract: Face recognition systems are increasingly deployed in security-critical applications, yet they remain vulnerable to presentation and morph attacks. Pre...
By Hatef Otroshi Shahreza, Asif Hussain Khan, Peter Lorenz, Alain Komaty, S\'ebastien Marcel
ScenePilot introduces a retrieval‑augmented Grow‑and‑Repair framework for text‑driven 3D indoor scene generation. It uses a Hierarchical Retrieval‑Augmented Planning module to fetch room, group, and anchor layout priors, then incrementally inserts object groups with a base generator, while a Reinforcement Multimodal Repair module performs lightweight local corrections after each insertion and a final global repair. The approach is trained on a new SceneReverse‑17k dataset of perturbed scenes, enabling the policy to predict structured move‑rotate‑scale actions from rendered views, scene state, retrieved priors, and edit history, thereby improving physical plausibility, functional coherence, and controllability without heavy full‑scene optimization.
By Jiawei Zhang, Hongsong Wang, Pan Zhou
arXiv:2508.13073v3 Announce Type: replace-cross
Abstract: Robotic manipulation, a key frontier in robotics and embodied AI, requires precise motor control and multimodal understanding, yet traditiona...
By Rui Shao, Wei Li, Lingsen Zhang, Renshan Zhang, Zhiyang Liu, Ran Chen, Liqiang Nie