The paper introduces a benchmark for topic matching in real-world ASR transcripts from contact centers, where noisy, punctuation‑free speech data must be classified into predefined topics. It presents a human‑annotated dataset of topic‑utterance judgments and evaluates three matcher types—regex, zero‑shot sentence embeddings, and Gemini‑based LLMs—using two topic representations: keyphrases and natural language descriptions. Experiments show that lightweight LLM matchers outperform the other methods, especially when natural language descriptions are used.
By Saman Rahbar, Xiliang Zhu, Irvin Cardoza, David Rossouw
arXiv:2609.00866v1 Announce Type: cross
Abstract: The rapid advancement of vision-language models (VLMs) has accelerated progress in computational pathology; however, whole-slide image (WSI)-based pa...
By Yumi Lee, Harim Oh, Hyoryung Kim, Minji Kim, Eunsu Kim, Hyeseong Lee, Junya Fukuoka, Andrey Bychkov, Jijgee Munkhdelger, Rajiv Kumar Kaushal, Ayushi Sahay, Rajni Yadav, Bharathi Prabakaran, Sulen Sarioglu, Serdar Balc{\i}, Ilknur Turkmen, Yuri Tolkach, Christian Harder, Julian Westerdorf, Reinhard Buettner, Audun Ljone Henriksen, Sepp De Raedt, Byung Hyun Lee, Sungjin Lim, Joohoon Lee, Gwanghyun Kim, Se Young Chun, Suryakant Singh, Saarthak Kapse, Prateek Prasanna, Kyung A Kim, Yousun Kang, Sehwan Yoo, Sungman Hong, Shubham Innani, Michael Feldman, Spyridon Bakas, Ujjwal Baid, Prasad Dutande, Suhas Gajare, Bhakti Baheti, Serkan S\"okmen, Ece Tu\u{g}ba Cebeci, Ahmet Hal{\i}c{\i}, Musa Balc{\i}, Kardelen Pe\c{c}enek, Srividhya Sainath, Kyongseok Jang, Messi H. J. Lee, Noorul Wahab, Bodong Du, Jiaming Zhang, Qixiang Zhang, Jang-Hwan Choi, Sangjeong Ahn
Instance-Guided Report Anchoring (IGRA) is a model-agnostic module that links each abnormality instance in a chest CT to the corresponding finding in a radiology report during training, while discarding text components at inference. By reformulating free-text grounding as multi-label volumetric segmentation, IGRA allows all abnormality categories to be predicted in a single image-only forward pass. The method improves Dice scores by 22.5% over the strongest image-only baseline and matches state‑of‑the‑art performance on single-finding subsets, with consistent gains across multiple 3D segmentation backbones and datasets.
By Zhenyu Bu, Haoyan Ding, Chushu Shen, Xinyuan Zheng, Peiyu Duan, Xueqi Guo, Sepehr Farhand, Yoshihisa Shinagawa, Gerardo Hermosillo, Chaowei Wu
SymFold introduces a symmetric dual‑path architecture that combines protein language models (PLMs) and multimodal protein language models (MPLMs) to iteratively guide protein sequence generation for inverse folding. By leveraging pretrained sequence evolution knowledge from PLMs and structural knowledge from MPLMs, the method improves upon the traditional serial pipeline where structure encoders produce coarse sequences refined by PLMs. Experiments on standard inverse‑folding benchmarks show state‑of‑the‑art performance, and ablation studies confirm the effectiveness of the symmetric design.
By Handong Wang, Jiaxin Qi, Baisheng Lai, Jianqiang Huang
SCAFFOLD is a large-scale structured dataset of computer science research figures, each paired with captions, context, questions, answers, and chain-of-thought reasoning traces. It contains 157,387 figure–question pairs from 3,058 arXiv papers, with subsets of 36,797 and 12,000 pairs for medium and small-scale use. The dataset was created using layout detection, PDF parsing, and AI-assisted question generation, and was used to benchmark a vision‑language model (Qwen2.5‑VL‑3B‑Instruct).
By Ranjit Raut, Aarav Subedi, Sagun Rai, Sudan Jha
arXiv:2605.12413v4 Announce Type: replace
Abstract: Multimodal Large Language Models (MLLMs) show strong visual perception, yet remain limited in reasoning about space under changing viewpoints. We s...
By Yuangong Chen, Wai Keung Wong, Jiaxing Li, Ioannis Patras, Xu Zheng
arXiv:2605.28456v2 Announce Type: replace
Abstract: Existing Visual Speech Recognition (VSR) systems commonly rely on left-to-right autoregressive decoding, which can force premature decisions on vis...
By Jeong Hun Yeo, Chae Won Kim, Hyeongseop Rha, Yong Man Ro
arXiv:2609.00443v1 Announce Type: cross
Abstract: Language models learn about grammatical number primarily from co-occurrence, and show frequency effects as a result---sometimes taken to indicate tha...
By Zach Studdiford, Kanishka Misra
arXiv:2609.01224v1 Announce Type: new
Abstract: Visual token pruning reduces the inference overhead of multimodal large language models (MLLMs) by retaining only a subset of visual tokens. Existing m...
By Yuanyuan Jia, Shunpu Tang, Qianqian Yang
The paper introduces VGEBench, a new benchmark for evaluating Vision‑Language Models (VLMs) on generalizable, visually grounded exploration of household devices. Unlike existing datasets that rely on static images or annotated trajectories, VGEBench employs a logic‑driven state machine to simulate multi‑turn interaction loops, requiring agents to actively perceive, act, and refine their actions to achieve goals. Experiments show that current VLMs struggle to translate semantic knowledge into physical execution and to maintain long‑horizon state tracking.
By Linhao Zheng, Zeming Liu, Wangke Chen, Li Zeng, Wanxiang Che, Heyan Huang, Yuhang Guo
arXiv:2609.00396v1 Announce Type: new
Abstract: Histopathological whole slide images (WSIs) are central to cancer diagnosis, but their gigapixel scale, tissue heterogeneity, weak slide-level supervis...
By Chad Wong, Sicheng Chen, Tianyi Zhang, Enhui Chai, Yueming Jin, Zeyu Liu, Fei Xia
The paper introduces a new adversarial attack on multimodal large language models (MLLMs) that targets the models’ intrinsic focus in the frequency domain. By exploiting the models’ sensitivity to phase information, the authors design a phase‑aware perturbation strategy that restricts changes to structure‑relevant phase regions, making attacks both effective and imperceptible. An auxiliary adversarial prompt module further misaligns multimodal attention toward targeted structural patterns, and experiments on several MLLM models and datasets confirm the method’s superior performance over existing attacks.
By Daizong Liu, Junhao Dong, Zhiyuan Ma, Xiaoye Qu, Xiang Fang, Runwei Guan, Keke Tang, Jianfeng Dong, Yew-Soon Ong
VoiceLongMemEval (VLME) is a new benchmark that tests AI assistants on their ability to remember how users sounded by incorporating paralinguistic metadata—such as emotion labels, prosody descriptors, and voice events—into each conversational turn. The benchmark uses a three‑stage adversarial gate to ensure that models cannot succeed with transcript alone, revealing a significant affect gap: models gain 0.09 to 0.38 accuracy when provided with paralinguistic cues, and audio‑native models outperform standard ASR pipelines in extracting these signals. The dataset and code will be released upon acceptance.
By Ramit Pahwa, Parivesh Priye, Apoorva Beedu
The paper introduces a new Continual Audio‑Visual Segmentation (CAVS) task that enables continuous segmentation of new classes guided by audio. It identifies two key challenges—multi‑modal semantic drift and co‑occurrence confusion—and proposes a Collision‑based Multi‑modal Rehearsal (CMR) framework with Multi‑modal Sample Selection (MSS) and Collision‑based Sample Rehearsal (CSR) strategies to address them. Experiments on three audio‑visual incremental scenarios show that CMR outperforms single‑modal continual learning methods.
By Yuyang Hong, Qi Yang, Tao Zhang, Zili Wang, Zhaojin Fu, Kun Ding, Bin Fan, Shiming Xiang
arXiv:2609.00272v1 Announce Type: new
Abstract: Most advances in keypoint descriptions address monomodal settings, where image variations arise from viewpoint, illumination, or contrast changes. Mult...
By Paul Schneider, Nazim Haouchine
arXiv:2609.00232v1 Announce Type: new
Abstract: Multimodal Large Language Models (MLLMs) have achieved strong performance on OCR-centric document understanding and text-rich visual reasoning benchmar...
By Yue Zhou, Yuan Wu, Yi Chang
IntroConformal introduces a training‑free Conformal Risk Control framework that offers finite‑sample, distribution‑free factuality guarantees for Large Vision‑Language Models. It uses introspective signals—layer‑wise semantic stability and verification probability derived from the model’s own hidden states—to assess claim factuality. Experiments across multiple LVLM architectures show that IntroConformal meets the conformal risk guarantee while reducing abstention and matching or surpassing external verifier baselines in claim‑level discrimination.
By Md. Atabuzzaman, Christian Alexander, Chris Thomas
The paper introduces EquiSD, a label‑free training method that exploits scale equivariance to improve metric grounding in vision‑language models. By projecting model predictions onto a scale‑equivariant family and fine‑tuning on the resulting targets, EquiSD boosts a 3B model’s median response slope from 0.66 to 0.94 and raises mean relative accuracy by 9.2 points across simulated scales, with positive transfer to real QuantiPhy videos.
By Kaizhen Tan, Yang Feng, Heqing Du, Siru Tao, Xin Xu, Hanzhe Hong
SlideBank is a training‑free framework that turns each whole‑slide image into a persistent, concept‑indexed evidence bank. It performs coarse‑to‑fine exploration to locate informative regions and multi‑scale views, converts them into explicit morphological observations, and anchors pathology signals to the supporting patches and slide coordinates. During inference, questions are routed to relevant signals and evidence scales, and a confidence‑based cross‑level consensus integrates global, regional, and patch evidence, achieving high accuracy on WSI‑VQA and SlideBench‑BCNB while enabling consistent re‑phrasing and reduced inference cost.
By Beidi Zhao, Gexin Huang, Ciro Zhang, Anqi Li, Yusheng Tan, Chen Zhou, Gang Wang, Zu-hua Gao, Xiaoxiao Li
arXiv:2609.00802v1 Announce Type: new
Abstract: Multi-party interaction is a central setting for human communication and a necessary target for human-agent interaction systems that must participate i...
By Taiga Mori, Koji Inoue, Mikey Elmers, Divesh Lala, Tatsuya Kawahara