arXiv AI
Jun 17
Perceptual compensation for tonal context in self-supervised speech models
arXiv:2606. 17835v1 Announce Type: cross Abstract: This study examines the extent to which the wav2vec2.
Multimodal models
Vision-language models, speech and cross-modal systems that read, look and listen in the same forward pass.
arXiv AI
Jun 17
Breaking the Code: Security Assessment of AI Code Agents Through Systematic Jailbreaking Attacks
arXiv:2510. 01359v2 Announce Type: replace-cross Abstract: Code-capable large language model (LLM) agents are embedded in software engineering workflows where they can read, write, and execute code, raising "jailbreak" stakes beyond text-only settings.
arXiv AI
Jun 17
ThinkJEPA: Empowering Latent World Models with Large Vision-Language Reasoning Model
arXiv:2603. 22281v2 Announce Type: replace-cross Abstract: Recent progress in latent world models (e.
arXiv AI
Jun 17
SPATIA: Multimodal Generation and Prediction of Spatial Cell Phenotypes
arXiv:2507. 04704v3 Announce Type: replace-cross Abstract: Understanding how cellular morphology, gene expression, and spatial context jointly shape tissue function is a central challenge in biology.
arXiv AI
Jun 17
MODE: Modality-Decomposed Expert-Level Mixed-Precision Quantization for MoE Multimodal LLMs
arXiv:2606. 17118v1 Announce Type: cross Abstract: Mixture-of-Experts Multimodal Large Language Models (MoE-MLLMs) offer remarkable performance but incur prohibitive GPU memory costs, making compression essential.
arXiv AI
Jun 17
StepGuard: Guarding Web Navigation via Single-Step Calibration
arXiv:2606. 17871v1 Announce Type: new Abstract: Web navigation requires agents to follow natural language goals, interact with web pages, and produce accurate answers.
arXiv AI
Jun 17
See First, Answer Later: Visual Evidence Pre-Alignment via Sufficiency-Driven RL
arXiv:2606. 17678v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) integrate strong text reasoning with visual inputs, yet their responses can be inconsistent with the underlying images, indicating ineffective utilization of visual evidence during inference.
arXiv Machine Learning
Jun 17
Moderating Illicit Online Image Promotion for Unsafe User-Generated Content Games Using Large Vision-Language Models
arXiv:2403. 18957v3 Announce Type: replace-cross Abstract: Online user generated content games (UGCGs) are increasingly popular among children and adolescents for social interaction and more creative online entertainment.
arXiv AI
Jun 17
Rethinking Multimodal Fusion for Time Series: Text Modalities Need Constrained Fusion
arXiv:2603. 22372v2 Announce Type: replace-cross Abstract: Recent advances in multimodal learning have motivated the integration of auxiliary modalities such as text or vision into time series (TS) forecasting.
arXiv Machine Learning
Jun 17
CoCoEmo: Composable and Controllable Human-Like Emotional TTS via Activation Steering
arXiv:2602. 03420v2 Announce Type: replace-cross Abstract: Emotional expression in human speech is nuanced and compositional, often involving multiple, sometimes conflicting, affective cues that may diverge from linguistic content.
arXiv AI
Jun 17
Correct When Paired, Wrong When Split: Decoupling and Editing Modality-Specific Neurons in MLLMs
arXiv:2606. 17057v1 Announce Type: cross Abstract: Although Knowledge Editing provides an efficient mechanism for updating the knowledge of Multimodal Large Language Models (MLLMs), we find that current paradigms still suffer from an important yet remain underexplored issue : editing decoupling failure, where entity-related knowledge can be updated when the model is triggered by multimodal inputs (text--image query pairs), however, it often reverts to outdated pre-edit facts when the paired inputs are split into unimodal ones.
arXiv AI
Jun 17
Visuals Lie, Consistency Speaks: Disentangling Spatial Attention from Reliability in Vision-Language Models
arXiv:2606. 17389v1 Announce Type: cross Abstract: Multimodal Foundation Models are increasingly used as reasoning agents, making reliability, knowing when a model may hallucinate, critical.
arXiv AI
Jun 17
R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model?
arXiv:2602. 03300v2 Announce Type: replace-cross Abstract: In this work, we aim to develop effective data synthesis techniques that autonomously synthesize multimodal training data for enhancing MLLMs in solving complex real-world tasks.
arXiv AI
Jun 17
LLM-Powered Multi-Agent System for Automated Crypto Portfolio Management
arXiv:2501. 00826v3 Announce Type: replace-cross Abstract: Cryptocurrency portfolio management requires the fusion of heterogeneous multi-modal signals, including structured price and on-chain time series, unstructured news text, and technical indicators, under high-volatility and real-time constraints.
arXiv AI
Jun 17
Membership Inference Attacks against Large Audio Language Models
arXiv:2603. 28378v2 Announce Type: replace-cross Abstract: We present the first systematic Membership Inference Attack (MIA) evaluation of LALMs.
arXiv AI
Jun 17
Divide, Deliberate, Decide: A Multi-Agent Framework for Fine-Grained Egocentric Action Recognition
arXiv:2606. 17627v1 Announce Type: cross Abstract: Fine-grained action recognition in egocentric video is challenging for Vision-Language Models (VLMs): actions often differ only in small visual cues, and a single model tends to be biased toward a subset of these cues.
arXiv AI
Jun 17
MODE-RAG: Manifold Outlier Diagnosis and Energy-based Retrieval-Augmented Generation Evaluation
arXiv:2606. 17449v1 Announce Type: cross Abstract: While Multimodal Retrieval-Augmented Generation (M-RAG) enhances Large Vision-Language Models, it remains highly susceptible to cross-modal hallucinations, causal fabrications, and sycophancy.
arXiv AI
Jun 17
MLLP-VRAIN UPV system for the IWSLT 2026 Simultaneous Speech Translation task
arXiv:2606. 17255v1 Announce Type: cross Abstract: This work describes the participation of the MLLP-VRAIN research group in the shared task of the IWSLT 2026 Simultaneous Speech Translation track.
arXiv Machine Learning
Jun 17
Loss Landscape Poisoning: Targeted Extraction of Unseen Training Data from LLMs
arXiv:2606. 17110v1 Announce Type: cross Abstract: Large Language Models are increasingly trained on proprietary or sensitive data, from private healthcare and financial records to user conversations containing secrets.
arXiv AI
Jun 17
Plug-and-Adapt: Multimodal Coreference Resolution at First Sight with a Pretrained Alignment Model
arXiv:2606. 17950v1 Announce Type: cross Abstract: Visual information helps resolve ambiguity in coreference resolution, leading to notable performance gains.