The paper introduces a multimodal generative framework that fine‑tunes Stable Diffusion XL with LoRA and couples it to a LLaMA 1.5‑7B language model to produce culturally faithful Batak Ulos motifs. Four conditioning mechanisms—text, image, representation, and semantic map—are jointly used to steer the generation, and an ablation study shows that combining all four actually degrades performance compared to selective combinations. A web prototype and user studies with weavers and the public demonstrate the system’s practical value for preserving and innovating Ulos design.
By Humasak Simanjuntak, Tamara Yunika Sianipar, Bronson T. M Siallagan, Difya Laurensya Ambarita, Arlinta Barus
ReFigBench is a benchmark that evaluates how well multimodal coding agents can transform scientific overview figures into editable PowerPoint slides, preserving text, layout, and document structure. The study uses 1,000 real figures from arXiv, testing agents from four model families across two workflows—direct code generation and a specialized PPTX workflow—within ten different harness configurations. Evaluation combines deterministic artifact checks, automated scoring by judges, and blinded human comparisons, revealing that workflow and harness choices significantly affect reconstruction quality and that even the best agents fall short of the ideal rubric.
By Liyang Fan, Chi Wei, Yitai Li, Xinping Bi, Guhong Chen, Chenghao Sun, Haoxiang Yang, Qingwen Li, Kai Yan, Hong Li, Bo Li
The paper introduces CUES, a lightweight heuristic for selecting encoder combinations in large audio‑language models by estimating complementarity through Pearson correlations of single‑encoder performance profiles. Using a frozen SmolLM2‑135M backbone, CUES consistently identifies optimal encoder sets for each track on the XARES‑LLM benchmark without requiring fusion training or test data. On broad audio tasks, CUES selects a diverse trio of encoders, improving performance by 4.3% over Whisper‑medium, while on text generation it opts for a focused speech‑only pair, outperforming mHuBERT‑147 by 6.3%. The results illustrate how correlation signals guide a diversity–interference trade‑off across different task families.
By Pei-Jun Liao, Hung-Shin Lee, Wenze Ren, Kuo-Hsuan Hung, Hung-yi Lee, Hsin-Min Wang
The paper introduces Aligned Consensus Teacher (ACT), a semi‑supervised framework for visible‑infrared object detection that operates under an image‑pair‑level setting with only a few labeled pairs. ACT combines Cycle‑Consistent Region Alignment, Cross‑Modal Consensus Mean‑Teacher, and Text‑Guided Cross‑Modal Instance Augmentation to address limited supervision, pseudo‑label errors, and scarce tail‑class annotations. Experiments on DroneVehicle and VEDAI demonstrate that with just 10% labeled pairs, ACT achieves 94.3% of the fully supervised mAP.
By Qi Ming, Xiaxin Yuan, Jiahuan Zhou, Jiangmeng Li, Xudong Zhao, Zhanchao Huang, Juan Fang, Shaoguang Huang, Aleksandra Pizurica
PhysVGGT is a feed‑forward model that predicts dense maps of friction coefficient, Shore hardness, Young's modulus, and density, along with object‑level mass, from a single RGB image in one forward pass. It treats physical property estimation as a dense per‑pixel prediction problem, using a visual geometry transformer to extract geometry‑aware tokens and separate dense and global prediction branches. A scalable pseudo‑label generation pipeline enables large‑scale weakly supervised training, and the model achieves state‑of‑the‑art performance on the ABO‑500 dataset while running 27× faster than previous methods.
By Sneha Paul, Guile Wu, Bingbing Liu, Dongfeng Bai
LiteViLNet is a lightweight RGB‑geometry fusion network for road segmentation that uses a MobileNetV3 RGB encoder and a tiny depth‑wise‑separable geometry encoder. Its multi‑scale fusion module enhances modality‑specific features, performs cross‑modal interaction, and applies adaptive gating, while a depth‑wise large‑kernel bridge expands contextual support with minimal overhead. The U‑Net‑style decoder is trained with deep supervision, achieving state‑of‑the‑art performance on KITTI and ORFD benchmarks and running at up to 68.73 FPS on a Jetson Orin NX with TensorRT FP16.
By Daojie Peng, Bingtao Wang, Fulong Ma, Liang Zhang, Jun Ma
arXiv:2609.17548v1 Announce Type: new
Abstract: Myovox, from myo (muscle) and vox (voice), decodes open-vocabulary English text from 31-channel surface electromyography (sEMG) recorded from the muscl...
By Varshith Madishetty
arXiv:2609.18417v1 Announce Type: new
Abstract: Multi-turn agent trajectories often contain redundant rounds (failed tool calls, parallel sub-queries, verification-only steps) that inflate both train...
By Zhuo Chen, Zhen Zhang, Xinyu Wang, Kewei Tu
arXiv:2609.17981v1 Announce Type: cross
Abstract: Speech Large Language Models (Speech-LLMs), typically built from a pre-trained speech encoder, a modality projector, and an LLM fine-tuned with Low-R...
By Mohan Shi, Zilai Wang, Natarajan Balaji Shankar, Kaiyuan Zhang, Eray Eren, Abeer Alwan
arXiv:2609.18009v1 Announce Type: cross
Abstract: Lightweight audio-visual speech enhancement (AVSE) models face a critical trade-off between computational efficiency and cross-modal alignment accura...
By Guo-Ruei Tseng, Hung-Shin Lee, Hsin-Min Wang, Berlin Chen
arXiv:2609.17800v1 Announce Type: new
Abstract: Vision-language models (VLMs) remain largely unreliable on panoramic dental radiographs and can rely on learned anatomical priors rather than evidence...
By Ahmed Rafid, Fariya Ahmed, Rumman Adib, Mehedi Ahamed, Ajwad Abrar, Tareque Mohmud Chowdhury
arXiv:2609.17790v1 Announce Type: new
Abstract: Pre-trained vision-language models (VLMs) exhibit strong cross-domain recognition performance even without additional training. However, this robustnes...
By Akanksha Singh, Vinod K. Kurmi
arXiv:2505.24427v2 Announce Type: replace
Abstract: Accurate modeling of subjective phenomena such as emotion expression requires data annotated with authors' intentions. Commonly such data is collec...
By Christopher Bagdon, Aidan Combs, Carina Silberer, Roman Klinger
RankGround is a two‑stage framework for GUI grounding that uses a single Vision‑Language Model call per query. It introduces GroundRanker, a lightweight multimodal reranker that selects the most promising crop from a dense candidate set, trained with a two‑stage curriculum on ranking supervision data derived from existing grounding datasets. Experiments show RankGround outperforms strong baselines, achieving 1.4× faster inference and a 5.5% average improvement in localization accuracy over the second‑best method across all backbones and screen scales.
By Liyang Fan, Xinping Bi, Yitai Li, Shuaimin Li, Hui Li, Min Yang
The paper introduces Code Consistency Preference Optimization Verification (CCPO), a method that generates computationally sound solutions with dependency graphs to improve execution-consistent preference optimization for language models. By building a scientific reasoning dataset and extracting reasoning steps, prerequisites, and derivability relationships, the authors compute execution consistency scores that are used to fine‑tune models such as Llama‑3‑8B and DeepSeekMath‑7B, achieving significant performance gains on MATH (+17.0%) and GSM8K (+15.1%). The extended Scientific Feasibility Control framework further boosts accuracy on PhyX physics reasoning to 50.1%, surpassing existing models while maintaining high scientific validity and reducing law violations.
By Yunlong Tan, Mingqiao Mo, Hao Zhang
arXiv:2609.18345v1 Announce Type: new
Abstract: Large vision-language models (LVLMs) are commonly used with only a single text prompt as the input, or plus an image. In this paper, we demonstrate tha...
By Xiaomeng Wang, Martha Larson, Zhengyu Zhao
arXiv:2609.18323v1 Announce Type: new
Abstract: Recent Omni-Modal Generative Models (Omni-Models) have advanced content generation toward unified modeling of text, images, video, and audio. MiniMax-H...
By Haoyu Zhao, Zihao Zhao, Tianyu Deng, Ziqin Xu, Zihao Zhang, Xudong Wang, Jinxiang Guo, Chen Gao, Ziyi Ye, Yeying Jin, Jiaxi Gu, Zuxuan Wu, Shuicheng Yan
arXiv:2609.17913v1 Announce Type: new
Abstract: Face--voice association models may rely on language or gender cues in the voice rather than on speaker-specific voice characteristics, which can lead t...
By Marta Moscati, Swapnil Khandoker, Muhammad Saad Saeed, Shah Nawaz, Fatima Noor, Rohan Kumar Das, Mubashir Noman, Junaid Mir, Muhammad Haroon Yousaf, Khalid Malik, Markus Schedl
arXiv:2609.17923v1 Announce Type: new
Abstract: Sports highlight detection aims to identify the most exciting and meaningful moments from long sports videos. While existing methods often emphasize vi...
By Hao Xu, Meenakshi Sarkar, Vishnu Raj, David Gunawan
arXiv:2609.18133v1 Announce Type: new
Abstract: Visible-infrared object detectors are used for robust perception under challenging illumination and weather conditions. Current physical attacks apply...
By Yueqi Zhu, Qi Ming, Guo Cheng, Yongkang Zhang, Feiran Liu, Juan Fang, Jiahuan Zhou, Jiangmeng Li, Yuhan Zhang