Multimodal models

Vision-language models, speech and cross-modal systems that read, look and listen in the same forward pass.

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arXiv AI

Jun 2

ELF: A Family of Encoder-Free ECG-Language Models

arXiv:2601. 18798v2 Announce Type: replace-cross Abstract: ECG-Language Models (ELMs) extend recent advances in Multimodal Large Language Models (MLLMs) to automated ECG interpretation.

By William Han, Tony Chen, Chaojing Duan, Xiaoyu Song, Yihang Yao, Yuzhe Yang, Michael A. Rosenberg, Emerson Liu, Ding Zhao

llms multimodal benchmarks

arXiv AI

Jun 2

MMG2Skill: Can Agents Distill In-the-Wild Guides into Self-Evolving Skills?

arXiv:2606. 01993v1 Announce Type: cross Abstract: Abundant procedural knowledge on the Web holds great potential for helping agents solve long-horizon tasks.

By Xinyu Che, Junqi Xiong, Yunfei Ge, Xinping Lei, Shihao Li, Hang Yan, Han Li, Yuanxing Zhang, Zhiqi Bai, Jinhua Hao, Ming Sun, Han Li, Jiaheng Liu

llms agents multimodal benchmarks

arXiv AI

Jun 2

RadAgent: A tool-using AI agent for stepwise interpretation of chest computed tomography

arXiv:2604. 15231v2 Announce Type: replace Abstract: Vision-language models (VLM) have markedly advanced AI-driven interpretation and reporting of complex medical imaging, such as computed tomography (CT).

By M\'elanie Roschewitz, Kenneth Styppa, Yitian Tao, Jiwoong Sohn, Jean-Benoit Delbrouck, Benjamin Gundersen, Nicolas Deperrois, Christian Bluethgen, Julia E. Vogt, Bjoern Menze, Farhad Nooralahzadeh, Michael Krauthammer, Michael Moor

llms agents multimodal safety

arXiv AI

Jun 2

v-HUB: A Benchmark for Video Humor Understanding from Vision and Sound

arXiv:2509. 25773v3 Announce Type: replace-cross Abstract: AI models capable of comprehending humor hold real-world promise -- for example, enhancing engagement in human-machine interactions.

By Zhengpeng Shi, Yanpeng Zhao, Jianqun Zhou, Yuxuan Wang, Qinrong Cui, Wei Bi, Songchun Zhu, Bo Zhao, Zilong Zheng

llms multimodal benchmarks

arXiv AI

Jun 2

A Structured Benchmark for Text-Guided Anomaly Detection: When Language Stops Conditioning the Decision

arXiv:2606. 01992v1 Announce Type: cross Abstract: Industrial anomaly detection has historically been a unimodal task.

By Stefano Samele, Eugenio Lomurno, Teodora Jovanovic, Sanjay Shivakumar Manohar, Alberto Crivellaro, Matteo Matteucci

llms rag multimodal benchmarks

arXiv AI

Jun 2

GiPL: Generative augmented iterative Pseudo-Labeling for Cross-Domain Few-Shot Object Detection

arXiv:2605. 29539v2 Announce Type: replace-cross Abstract: Vision-language foundation models have shown promising zero-shot generalization for Cross-Domain Few-Shot Object Detection (CD-FSOD).

By Jiacong Liu, Shu Luo, Yikai Qin, Yaze Zhao, Yongwei Jiang, Yixiong Zou

llms computer-vision fine-tuning multimodal benchmarks

arXiv AI

Jun 2

Multimodal Approaches for Visually-Rich Document Type Classification: A Comparative Analysis

arXiv:2606. 02162v1 Announce Type: cross Abstract: Document type classification in visually rich documents remains challenging, as relevant information is distributed across textual, visual, and layout modalities.

By Catyana Heyne, J\"urgen Frikel, Filippo Riccio

llms computer-vision multimodal benchmarks

arXiv AI

Jun 2

AtomEval: Validity-Aware Atomic Evaluation of Adversarial Claim Rewriting in Fact Verification

arXiv:2604. 07967v3 Announce Type: replace-cross Abstract: Large language models (LLMs) can rewrite refuted claims to evade evidence-based fact verifiers, but conventional attack success rate (ASR) can be inflated when rewrites change, weaken, or correct the false proposition they are supposed to preserve.

By Hongyi Cen, Mingxin Wang, Yule Liu, Jingyi Zheng, Hanze Jia, Tan Tang

llms multimodal safety

arXiv Machine Learning

Jun 2

Lagrangian Perturbation Diffusion Steering: Latent Reinforcement Learning for Generative Policies

arXiv:2606. 01151v1 Announce Type: new Abstract: Behavior cloning with high-capacity generative policies achieves strong imitation performance, but is often limited by demonstration coverage and distribution shift.

By Hikmet Simsir, Ozgur S. Oguz

diffusion reinforcement-learning robotics fine-tuning multimodal benchmarks

arXiv AI

Jun 2

Beyond the Mouth: Upper-Face Affective Cues in Audiovisual Sentence Recognition under Acoustic Uncertainty

arXiv:2606. 00670v1 Announce Type: cross Abstract: Face-to-face speech comprehension is inherently multimodal, integrating acoustic signals with visible articulation, facial expression, head motion, and other socially relevant cues.

By Zhou Yang, Yueyi Yang

multimodal

arXiv AI

Jun 2

Distilling Neuro-Symbolic Programs into 3D Multi-modal LLMs

arXiv:2606. 01215v1 Announce Type: cross Abstract: Current 3D spatial reasoning methods face a fundamental trade-off: neuro-symbolic 3D (NS3D) concept learners achieve interpretable reasoning through compositional programs but are constrained to closed-set concept vocabularies and simple programs; end-to-end 3D multi-modal LLMs (3D MLLMs) could handle complex natural language and open-vocabulary concepts but suffer from black-box reasoning without explicit spatial verification.

By Wentao Mo, Yang Liu

llms nlp reinforcement-learning fine-tuning multimodal benchmarks safety

arXiv Machine Learning

Jun 2

TLG: Temporal-Logic Grounding for Video Question Answering via Source-Annotation Reconstruction and Category-Targeted Reasoning

arXiv:2606. 01591v1 Announce Type: cross Abstract: The TimeLogic Challenge evaluates formal temporal-logic reasoning over video - 16 operators (before, after, until, since, always, co-occur, ordering, ...

By Ali Alavi

llms nlp multimodal benchmarks

arXiv AI

Jun 2

Ryze: Evidence-Enriched Data Synthesis from Biomedical Papers

arXiv:2606. 00902v1 Announce Type: new Abstract: General-purpose VLMs remain unreliable for biomedical research because valid answers in scientific papers depend on evidence split across figures, tables, charts, captions, and referring text.

By Yeqi Huang, Yue Chen, Yanwei Ye, Guanhao Su, Luo Mai

llms computer-vision reinforcement-learning fine-tuning multimodal

arXiv Machine Learning

Jun 2

Reason, Retrieve, Re-rank: A Zero-Shot Reasoning-Aware Framework for Composed Video Retrieval

arXiv:2606. 00910v1 Announce Type: cross Abstract: Composed Video Retrieval (CoVR) seeks the target video that results from applying a free-form textual modification to a reference video.

By Ali Alavi

llms multimodal

arXiv AI

Jun 2

Deep Interest Mining for Intent-Enriched Semantic IDs in Multimodal Generative Recommendation

arXiv:2604. 20861v3 Announce Type: replace-cross Abstract: Semantic IDs (SIDs) provide the discrete item vocabulary used by generative recommendation, but their quality depends on what item evidence is preserved before quantization.

By Yangchen Zeng, Jinze Wang

llms rag reinforcement-learning efficiency multimodal

arXiv AI

Jun 2

Catch-Only-One: Non-Transferable Examples for Model-Specific Authorization

arXiv:2510. 10982v2 Announce Type: replace-cross Abstract: Recent AI regulations increasingly emphasize the need for mechanisms that preserve the utility of data for AI innovation while preventing misuse, particularly by enforcing purpose limitation in downstream AI applications.

By Zihan Wang, Zhiyong Ma, Zhongkui Ma, Shuofeng Liu, Akide Liu, Derui Wang, Minhui Xue, Guangdong Bai

llms multimodal benchmarks safety

arXiv AI

Jun 2

Property Prediction of Stacked Bilayer Materials: A Multimodal Learning Approach

arXiv:2606. 01012v1 Announce Type: new Abstract: AI for materials science is a critical topic within AI for science, aiming to accelerate materials discovery and produce accurate property predictions.

By An Vuong, Minh-Hao Van, Chen Zhao, Xintao Wu

efficiency multimodal

arXiv Machine Learning

Jun 2

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders

arXiv:2606. 00746v1 Announce Type: cross Abstract: Vision foundation models are bottlenecked by the quadratic cost of self-attention, which limits usable resolution and increases the cost of large-scale pretraining.

By Yitong Jiang, Hongjun Wang, Collin McCarthy, Hanrong Ye, David Wehr, Xinhao Li, Qi Dou, Tianfan Xue, Ka Chun Cheung, Simon See, Wonmin Byeon, Ke Chen, Kai Han, Jinwei Gu, Hongxu Yin, Pavlo Molchanov, Jan Kautz, Sifei Liu

rag computer-vision efficiency multimodal

arXiv AI

Jun 2

UF-AMA: A unified framework for cross-domain emotion recognition via adaptive multimodal alignment

arXiv:2606. 00170v1 Announce Type: cross Abstract: In recent years, emotion recognition based on physiological signals such as electroencephalogram (EEG) has gained considerable attention, as internal physiological data offer greater objectivity and reliability compared to external behavioral data like facial expressions.

By Zheng Wang, Shuo Wang, Junhong Wang

llms fine-tuning efficiency multimodal benchmarks safety

arXiv Machine Learning

Jun 2

Feature Alignment Determines Fusion Strategy: A Comparative Study of Cross-Attention and Concatenation in Multimodal Learning

arXiv:2606. 01207v1 Announce Type: cross Abstract: The choice between cross-attention and concatenation for multimodal fusion remains governed by practitioner intuition rather than principled understanding.

By Zhiqiang Zhou, Xuezhen Xie

llms multimodal safety

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