The paper introduces SAGE, a unified framework for RGB‑Thermal (RGB‑T) image alignment and fusion that integrates frequency equalization, hierarchical alignment, and subband fusion. SAGE uses invertible joint encoding and source‑specific low‑frequency modulation to generate structural and gain guidance, then performs hierarchical frequency collaborative alignment to estimate global affine geometry and refine high‑frequency details. Guided subband fusion aggregates aligned frequency coefficients under propagated guidance, coordinating complementary low‑ and high‑frequency information to reconstruct the fused image via inverse wavelet transform. Experiments on real‑world and synthetic misaligned RGB‑T datasets show that SAGE achieves competitive performance in both alignment and fusion, validating the effectiveness of source‑anchored guidance for weakly registered RGB‑T images.
By Timing Li, Yiming Sun, Boan Tao, Xiyuan Gao, Haifang Cao, Pengfei Zhu
VLALight is a lightweight end‑to‑end vision‑language‑action framework designed for traffic signal control. It fuses multiple camera views and textual instructions to directly predict signal actions, avoiding intermediate image‑to‑text conversions. The model, with only 0.5 B parameters, achieves superior emergency vehicle service, cutting pooled waiting time by 21.1% compared to cascaded methods while running in real time on local hardware.
By Kemou Jiang, Maonan Wang, Xingchen Zou, Jiayue Zhu, Yuhang Fu, Sicheng Wang, Xi Chen, Yirong Chen, Zhiyong Cui
NavGen introduces a text-to-video data generation pipeline that creates about 400K vision‑language navigation episodes for both indoor and outdoor scenes, using high‑fidelity visual generative models. The approach includes a style‑diversification method to scale up rare, hard‑to‑collect data. Models trained on NavGen data outperform those trained on existing UAV navigation datasets and achieve a 75% success rate in real‑world flying experiments.
By Xijie Huang, Yongyang Wan, Chengbin Dong, Zimo Ding, Mo Zhu, Yijin Wang, Zhiyang Liu, Fei Gao, Yuze Wu, Xin Zhou
The paper introduces LIRSeg, a method that replaces explicit Chain-of-Thought reasoning in multimodal large language models with a compact set of learnable latent tokens for reasoning segmentation. LIRSeg is trained in two stages—spatial alignment and GRPO—while employing extreme-advantage sampling, decoupled exploration-stability updates, and latent diversity amplification to enhance token informativeness. Experiments show that LIRSeg improves segmentation accuracy and reasoning efficiency, achieving significant gIoU gains over the VisionReasoner baseline and reducing reasoning tokens by about 16×.
By Tianhang Guo, Yulin He, Wei Chen, Wenjuan Zhou, Yuhang Li, Xinbiao Gan
The paper introduces iDriveVLA, a multi‑modal planning framework for autonomous driving that addresses a generation‑evaluation asymmetry by improving candidate trajectory spaces and providing a unified, safety‑aware evaluator. It combines a Safety‑aware Scorer for risk estimation with a VLM‑guided Modulator that adapts weighting to the scene, and employs an oracle‑aligned progressive training strategy. On the NAVSIM v1 leaderboard, iDriveVLA achieves a new state‑of‑the‑art PDMS score of 94.95, surpassing human‑expert performance.
By Zeyu He, Shiqi Liu, Ke Chen, Yun Yan, Jinzi Wu, Dianqiao Lei, Sirui Wang, ShuRui Peng, Tao Chen, Zhuo Huang, Yu Wu, Yadong Shao, Zhichao Li, Ke Sun, Yang Guan, Keqiang Li, Shengbo Eben Li
UltraG-Bench is a large‑scale, multi‑task benchmark designed to evaluate pixel‑level evidence grounding in ultrasound images. It comprises 40 public segmentation datasets covering 13 anatomical categories and includes three progressive tasks—instruction‑guided segmentation, evidence‑grounded VQA, and evidence‑grounded report generation—with a total of 736,726 annotations. Evaluation of 14 state‑of‑the‑art models shows a significant gap between semantic understanding and fine‑grained pixel‑level localization, and the authors propose UltraG‑Agent, which combines a vision‑language model with the ultrasound‑specific segmentation model UltraSAM to improve both semantic prediction and visual grounding.
By Quanhao Zhu, Bo Xu, Rui Lin, Chenyuan Wang, Yu Shao, Boling Zhu, Jiuyan Sun, Liang Zhao, Hongfei Lin, Feng Xia
MVVBench is a new benchmark for multi‑view video reasoning that tests vision‑language models on tasks requiring integration of spatial and temporal evidence across multiple, often non‑overlapping camera streams. The benchmark contains questions that cannot be answered from any single view or single moment, forcing models to jointly reason across views and time. It evaluates six capabilities—including attribute identification, relative distance, camera pose, and compositional counting—and provides human‑authored QA, rigorous verification, and detailed error analysis.
"whyItMatters":"The benchmark offers a rigorous evaluation of 4D multi‑view reasoning and a foundation for future progress toward reliable embodied perception."
By Hyungjin Chung, Byeongjun Park, Joonseok Lee, Hojun Kim, Jaeho Choi, Byung-Hoon Kim
The paper proposes a theoretical framework called the Linear Representation Hypothesis (LRH) for vision‑language‑action (VLA) models, extending the concept from large language models to systems where physical quantities of interest (QoI) evolve with the dynamics. It introduces a signature‑based formulation that unifies representations and policies, proving that future QoI evolution can be linearly probed from representations and that a generalized linear model for stochastic action chunks allows monotonic steering of QoI. The authors validate their theory with an explicit oracle representation in a planar control‑affine navigation experiment, demonstrating the predicted linear probing and steering mechanisms.
By Minseok Jeong, Hyewon Choi, Hiroyasu Tsukamoto, SooJean Han
DepthEvidence is a 4B multimodal language model that integrates dense metric depth predictions into language generation. It employs a camera‑conditioned decoder to produce full‑resolution depth maps and a dense‑to‑language interface that converts these predictions into object‑aligned geometry tokens. The model is trained with geometric supervision and instruction tuning, and it sets new state‑of‑the‑art results on a Depth‑VQA benchmark and on instance‑level metric depth estimation across nine datasets.
By Jiangning Wei, Yuan Yao, Miaomiao Cui, Mingsheng Li, Humen Zhong, Shuai Bai, Zhibo Yang
Pocket-STVG (P-STVG) is a lightweight cascade architecture for Spatio-Temporal Video Grounding that combines efficient pre‑trained components: a temporal‑aware video encoder based on MobileViCLIP, a spatial encoder‑decoder from MDETR, and a shared aligned text encoder. Temporal localization is achieved with a lightweight 1D U‑Net or a simple thresholding strategy, allowing the model to work in both weakly supervised and zero‑shot settings. With fewer than 90 M parameters, P-STVG matches or surpasses prior weakly supervised and zero‑shot methods while offering a more memory‑ and compute‑efficient pipeline for large‑scale video collections.
By Alberto Presta, Michal Byra, Grzegorz Stefa\'nski, Karol Szurkowski, Eryk Ko{\l}odziejczyk, Krzysztof Arendt
BAT-CLIP is a trimodal alignment framework that jointly aligns intracranial EEG (iEEG) neural embeddings to both pretrained audio and text anchors within a shared, frozen audio‑text manifold. Unlike existing CLIP‑style brain‑speech models that anchor neural activity to a single modality, BAT‑CLIP leverages both audio and text to preserve temporal structure and linguistic separability. On the naturalistic Podcast benchmark, BAT‑CLIP produces more robust representations than bimodal CLIP baselines and demonstrates the value of self‑supervised foundation models for CLIP training.
By Suhyun Kim, Jinmo Han, Danny Dongyeop Han, Ahhyun Lucy Lee, Jewoon Lee, Yonghyeon Gwon, Zach Paris, Chun Kee Chung, Saewoong Bahk, Nam Soo Kim, Seong Jae Hwang, Jiook Cha
UniAR is a unified framework that improves autism spectrum disorder (ASD) recognition by using multi-granularity prompt learning and a large multimodal model to generate diagnostic descriptions at word, phrase, and sentence levels. It aligns these semantic representations with visual evidence through a Mixture-of-Experts-based Multi-Scale Alignment Module, enabling robust ASD detection across heterogeneous data types. Experiments on four brain MRI and facial expression benchmarks show that UniAR outperforms state‑of‑the‑art methods, achieving 75.9% accuracy on MRI and 91.6% on facial benchmarks, with gains of 1.5 and 1.2 percentage points respectively.
By Lei Xin, Zeheng Wang, Jiayin Zhu, Shihong Huang, Fanhu Zeng, Changjiang Jiang, Dengbo He, Yutao Yue, Zhenglun Kong
The paper proposes a new architecture for Vision‑Language‑Action (VLA) models that improves sample efficiency by training a predictive world model on the vision encoder’s embedding space. It argues that these embeddings are action‑relevant and can be used to predict future states, addressing the lack of an explicit world model in current VLAs. The trained model can also support short‑term planning by sampling actions that lead to desired goal images.
By Parsa Mastouri Kashani, Jan-Gerrit Habekost, Stefan Wermter
The paper "Sorry Robot, Happy Human: Vision-Language Models Read Only One of Two Legible Typographic Layers" reports that vision‑language models (VLMs) struggle to read images containing two overlapping text layers—one with sharp contour lines and one with soft shading. Using the DecoyBench dataset of 300 such images, the authors evaluated six closed‑source VLMs under naive and guided prompting at high and low resolutions. While humans could read both layers accurately, the models reliably read only the contour layer at high resolution and failed to extract the shading layer; at low resolution, neither the models nor humans could read the contour layer, but the shading layer remained readable.
"whyItMatters":"The study highlights a consistent limitation of current VLMs in handling typographic structures with multiple spatial frequency layers, underscoring their vulnerability to typographic attacks and the need for more robust text‑recognition capabilities."
By Mert \.Incidelen, Yamen Kashkash, Asya Berker, Murat Aydo\u{g}an
The paper introduces NarrativeAttack, a jailbreak framework that exploits unified multimodal models (UMMs) by embedding a malicious query within a self‑contained three‑act visual narrative. The attack uses the model’s own image generator to create setup and resolution images, hiding the malicious event as a hidden climax, and concludes with an image‑based guessing game that forces the model to select the relevant answer. Experiments demonstrate that NarrativeAttack outperforms previous methods, achieving up to 88.25% attack success rate on Gemini‑2.5‑Flash, revealing a significant safety vulnerability in UMMs.
By Shaoxiong Guo, Tianyi Du, Lijun Li, Yuyao Wu, Jie Li, Jing Shao
The paper introduces Stepwise Marginal Information Gain (MIG), an intrinsic process reward that evaluates how each reasoning step of a large language model (LLM) or vision-language model (VLM) improves the likelihood of the reference answer. MIG rewards only new likelihood maxima, preventing duplicate credit, and is combined with outcome, format, and self‑distillation objectives to guide training. Experiments on eight benchmarks show that this method outperforms outcome‑only reinforcement learning and improves accuracy by up to 4.8 points over binary‑reward training, including a 12.6‑point gain on MathVerse and a 12.9‑point advantage on vision‑language transfer at 7B parameters.
By Xiangwei Wang, Wei Wang, Ken Chen, Nanduni Nimalsiri, Sachith Seneviratne, Saman Halgamuge
Agentick is a unified benchmark for sequential decision‑making agents that evaluates RL, LLM, VLM, hybrid, and human agents on 37 procedurally generated tasks across six capability categories, four difficulty levels, and five observation modalities via a single Gymnasium‑compatible interface. It includes a Coding API, oracle reference policies, pre‑built SFT datasets, a composable agent harness, and a live leaderboard. An evaluation of 27 configurations and over 90,000 episodes shows no single approach dominates, with GPT‑5 mini leading overall, PPO excelling in planning and multi‑agent tasks, and the reasoning harness boosting LLM performance by 3‑10×, while ASCII observations outperform natural language.
By Roger Creus Castanyer, Pablo Samuel Castro, Glen Berseth
PhysElite is a new bilingual multimodal benchmark designed to evaluate large language models on Olympiad-level physics problems. It contains 11,586 problems, each paired with visual diagrams, step-by-step bilingual Chinese‑English solution derivations, and the final answer. Benchmarking 18 models revealed that even the best reaches only 33.7% accuracy, and a step‑level analysis highlights where models falter in reasoning.
By Ruoran Xu, Wending Gao, Liyunfeng Chen, Aixin Shi, Haoyu Cheng, Zixiang Fang, Yiqiang Zou, Qiufeng Wang
LapDDPM is a conditional Graph Diffusion Probabilistic Model that generates high‑fidelity, biologically plausible single‑cell RNA sequencing data. It incorporates graph‑based inductive biases and a spectral adversarial perturbation mechanism to enforce robustness against structural noise, effectively acting as a Distributionally Robust Optimization framework. The model extends to spatial transcriptomics and multi‑modal data, and experimental results on datasets such as PBMC3K, Dentate Gyrus, HLCA, Visium, and 10x Multiome show it outperforms state‑of‑the‑art baselines in distribution matching, manifold preservation, and downstream utility.
By Lorenzo Bini, Stephane Marchand-Maillet
The paper introduces a formal framework for speech attribute conversion, focusing on deterministic autoencoders with an independence constraint between latent representations and controllable attributes. It provides theoretical guarantees linking reconstruction, independence, and successful attribute manipulation under explicit population-level assumptions. The authors also propose a practical voice conversion method based on these principles and demonstrate competitive performance on voice and pitch conversion tasks.
By Jonathan Svirsky, Ofir Lindenbaum, Uri Shaham