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Bringing Value Models Back: Generative Critics for Value Modeling in LLM Reinforcement Learning8h◆Subagents vs Agent Skills: Executing Reusable Knowledge for Long-Horizon Agentic Tasks8h◆Distribution-Consistent Inference for Dynamic Sparse Mixture-of-Experts8h◆In RAG We Trust? Measuring Robustness of Retrieval-Augmented Generation Under Document Poisoning8h◆FrontierChallenge: Evaluating Scientific Workflow Completion8h◆IBIB: A Protocol for Measuring Enterprise AI Systems by Serving Route, Not Model Identifier8h◆AgenticGen: Reward-Guided Agentic Video Generation for Advertising8h◆Strangers to Themselves: What Language Models Say About Themselves Is Generic8h◆Omni Interaction Agent Technical Report8h◆CoGReV: A Confidence-Gated Post-Hoc Non-Monotonic Belief Revision Framework for Phishing Website Classification8h◆Bit-Flip Attacks on Vision-Language-Action Models: Action-Decoding Architecture Shapes the Vulnerability8h◆Can Foundation Models Moderate Online Content? Evaluating Instruction- vs. Example-Driven Policy Operationalization8h◆RiLM: Parameter-Efficient Language Modeling via Geodesic Decoding8h◆Tracing Computation Density in LLMs8h◆Cultural Binding Heads in Language Models8h◆Direct Diversity Optimization for Diverse Successful Trajectories in Preference Post-Training8h◆Left-Branching Transformers Excel at Right-Branching Languages: Data Shapes Word Order Preferences in Language Models8h◆Palmyra x6 Technical Report: An Agentic, Tool-Use Model Post-Trained via Anchored Supervised Fine-Tuning8h◆'Ghaib in Translation' aka Unseen Harm: Measuring Cross-Script Safety Inconsistency with 'Missed-in-Urdu' Scores in LLM Hate Speech Detection8h◆DexterSQL: Deep Schema Exploration and Rule-based Correction for Text-to-SQL Generation8h◆Bringing Value Models Back: Generative Critics for Value Modeling in LLM Reinforcement Learning8h◆Subagents vs Agent Skills: Executing Reusable Knowledge for Long-Horizon Agentic Tasks8h◆Distribution-Consistent Inference for Dynamic Sparse Mixture-of-Experts8h◆In RAG We Trust? Measuring Robustness of Retrieval-Augmented Generation Under Document Poisoning8h◆FrontierChallenge: Evaluating Scientific Workflow Completion8h◆IBIB: A Protocol for Measuring Enterprise AI Systems by Serving Route, Not Model Identifier8h◆AgenticGen: Reward-Guided Agentic Video Generation for Advertising8h◆Strangers to Themselves: What Language Models Say About Themselves Is Generic8h◆Omni Interaction Agent Technical Report8h◆CoGReV: A Confidence-Gated Post-Hoc Non-Monotonic Belief Revision Framework for Phishing Website Classification8h◆Bit-Flip Attacks on Vision-Language-Action Models: Action-Decoding Architecture Shapes the Vulnerability8h◆Can Foundation Models Moderate Online Content? Evaluating Instruction- vs. Example-Driven Policy Operationalization8h◆RiLM: Parameter-Efficient Language Modeling via Geodesic Decoding8h◆Tracing Computation Density in LLMs8h◆Cultural Binding Heads in Language Models8h◆Direct Diversity Optimization for Diverse Successful Trajectories in Preference Post-Training8h◆Left-Branching Transformers Excel at Right-Branching Languages: Data Shapes Word Order Preferences in Language Models8h◆Palmyra x6 Technical Report: An Agentic, Tool-Use Model Post-Trained via Anchored Supervised Fine-Tuning8h◆'Ghaib in Translation' aka Unseen Harm: Measuring Cross-Script Safety Inconsistency with 'Missed-in-Urdu' Scores in LLM Hate Speech Detection8h◆DexterSQL: Deep Schema Exploration and Rule-based Correction for Text-to-SQL Generation8h◆
Tag

#multimodal

35 articles tagged #multimodal

arxivJul 31bullish

VAD: Attributing Visual Evidence for Target Reconstruction in Multimodal On-Policy Distillation

arXiv:2607.28590v1 Announce Type: cross Abstract: Multimodal on-policy distillation (OPD) transfers fine-grained visual knowledge by supervising student-generated trajectories with a privileged-view teacher. Yet its next-token corrections are source-mixed, combining visual signals with linguistic pr

VI1 model#computer-vision#distillation#multimodalRead on arxiv →
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arxivJul 31bullish

Zero-Shot Face-to-Speech Synthesis via Latent Space Adaptation of a Style-Diffusion TTS Model

arXiv:2607.26742v1 Announce Type: cross Abstract: Zero-shot text-to-speech (TTS) clones a voice from a short audio prompt, but this reliance on reference audio is a barrier when only visual information is available, e.g. for historical figures or video-game characters. In this work, we propose a Fac

STFA2 models#speech-synthesis#face-recognition#multimodalRead on arxiv →
arxivJul 30bullish

From Cellular Responses to Pharmacological Domains: Multimodal Zero-Shot Drug Representation Learning

arXiv:2607.25322v1 Announce Type: new Abstract: Multimodal drug discovery enables drug representation learning beyond chemical structure by incorporating cellular responses such as gene expression and cell morphology. However, direct fusion and instance-level contrastive alignment may mix mechanism-

#multimodal#drug-discovery#representation-learningRead on arxiv →
arxivJul 30bullish

Dual-Path LLM Reasoning for Multimodal Few-Shot Knowledge Graph Completion

arXiv:2607.26909v1 Announce Type: new Abstract: Knowledge graph completion (KGC) aims to infer missing facts in knowledge graphs (KGs), thereby improving their completeness and supporting downstream intelligent applications. However, emerging entities and relations in real-world deployments make ind

DU1 model#knowledge-graph#few-shot#multimodalRead on arxiv →
arxivJul 18bullish

InCarEmo: A Multimodal Dataset for In-Cabin Emotion Recognition and Driver State Monitoring

arXiv:2607.14683v1 Announce Type: new Abstract: Understanding driver emotion and state is critical for the next generation of intelligent in-cabin systems that ensure safety and enhance human-vehicle interaction. However, existing public datasets for in-cabin affective computing are largely limited

#dataset#emotion-recognition#multimodalRead on arxiv →
arxivJun 30

Forewarned is Forearmed: When Non-Sequential Embedding Turns Into an Anomaly Detector

arXiv:2606.30196v1 Announce Type: cross Abstract: This paper offers an in-depth analysis of non-sequential multimodal sentence-level embeddings, with a particular focus on the SONAR model. We demonstrate that certain embedding dimensions are sensitive to perturbations and can serve as indicators of

SO1 model#multimodal#embeddings#reliabilityRead on arxiv →
arxivJun 30bullish

LLM-based Multimodal Personality Recognition via Facial Action Unit-Text Semantic Fusion

arXiv:2606.29900v1 Announce Type: cross Abstract: Personality recognition in asynchronous video interviews (AVIs) has become increasingly important due to their widespread adoption in modern recruitment. Existing approaches often rely on large language models (LLMs) to analyze textual responses of i

LA1 model#computer-vision#personality-recognition#multimodalRead on arxiv →
arxivJun 29

Lost at the End: Primacy Bias in Multimodal Retrieval-Augmented Question Answering

arXiv:2606.16494v2 Announce Type: replace-cross Abstract: Knowledge-based visual question answering (KB-VQA) lets vision-language systems answer questions that exceed their parametric knowledge by conditioning a reader on passages retrieved from a Wikipedia-scale knowledge base. In pure-text long-co

#knowledge-based#visual-question-answering#multimodalRead on arxiv →
arxivJun 27bullish

Semantic Generative Tuning for Unified Multimodal Models

arXiv:2605.18714v2 Announce Type: replace-cross Abstract: Unified multimodal models (UMMs) strive to consolidate visual understanding and visual generation within a single architecture. However, prevailing training paradigms independently optimize understanding via sparse text signals and generation

#multimodal#computer-vision#generative-modelsRead on arxiv →
arxivJun 27

MIRROR: Novelty-Constrained Memory-Guided MCTS Red-Teaming for Agentic RAG

arXiv:2606.26793v1 Announce Type: cross Abstract: Multimodal agentic retrieval-augmented generation (RAG) systems expand the attack surface beyond prompt injection to include text poisoning, image injection, direct-query attacks, and orchestrator-level tool manipulation. Existing red-teaming approac

#security#adversarial-attacks#multimodalRead on arxiv →
arxivJun 27bullish

TOPS: First-Principles Visual Token Pruning via Constructing Token Optimal Preservation Sets for Efficient MLLM Inference

arXiv:2606.27161v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have achieved strong multimodal reasoning capabilities, but their efficiency is limited by the large number of visual tokens, which introduces substantial computational overhead. Visual token pruning offers a na

LL1 model#multimodal#pruning#efficiencyRead on arxiv →
arxivJun 18bullish

Artemis: Anatomy-Resolved inTervention for Eliminating Multimodal NeuroImage confounderS

arXiv:2606.18287v1 Announce Type: new Abstract: Multimodal neuroimaging, integrating functional connectivity from fMRI and structural connectivity from DTI, enables non-invasive analysis of brain networks using graph neural networks. However, demographic factors such as age and sex systematically co

AR1 model#neuroimaging#graph-neural-networks#causalityRead on arxiv →
arxivJun 18

MetaboNet-Bench: A Multi-modal Benchmark for Glucose Forecasting in Type 1 Diabetes

arXiv:2606.18640v1 Announce Type: new Abstract: Glucose forecasting algorithms are an important aspect of glycemic control management in type 1 diabetes. So far, the research community has developed numerous algorithms and models for forecasting. However, it is well-recognized that the lack of stand

ME1 model#glucose-forecasting#benchmark#multimodalRead on arxiv →
arxivJun 18bullish

ThinkDeception: A Progressive Reinforcement Learning Framework for Interpretable Multimodal Deception Detection

arXiv:2606.18988v1 Announce Type: new Abstract: Multimodal deception detection is critical for identifying fraudulent intentions, yet existing approaches predominantly rely on end to end black--box paradigms. These methods suffer from a severe lack of interpretability failing to provide transparent

THTH2 models#multimodal#deception-detection#interpretabilityRead on arxiv →
arxivJun 16

MAND: Modality-Aware Novelty Detection for Open-World Egocentric Activity Recognition

arXiv:2603.16970v2 Announce Type: replace-cross Abstract: Multimodal egocentric activity recognition integrates visual and inertial cues for robust first-person behavior understanding. However, deploying such systems in open-world environments requires detecting novel activities while continuously l

MA1 model#open-world#continual-learning#multimodalRead on arxiv →
arxivJun 6bullish

Toward Culturally Aligned LLMs through Ontology-Guided Multi-Agent Reasoning

arXiv:2601.21700v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) increasingly support culturally sensitive decision making, yet often exhibit misalignment due to skewed pretraining data and the absence of structured value representations. Existing methods can steer outputs, but

#ontology#multimodal#cultural-sensitivityRead on arxiv →
arxivJun 6bearish

The Mirage of Performance Gains: Why Contrastive Decoding Fails to Mitigate Object Hallucinations in MLLMs?

arXiv:2504.10020v4 Announce Type: replace-cross Abstract: Contrastive decoding strategies are widely used to reduce object hallucinations in multimodal large language models (MLLMs). These methods work by constructing contrastive samples to induce hallucinations and then suppressing them in the outp

#multimodal#hallucinations#language-modelsRead on arxiv →
arxivJun 3bullish

Before Fusion, Ask What to Keep: Contextual Calibration of Multimodal Signals

arXiv:2606.02679v1 Announce Type: new Abstract: Multimodal systems often benefit from combining information across language, sound, and visual streams, but this benefit is not guaranteed. A modality that is useful for one input may become distracting for another, and local feature responses within t

#multimodal#fusion#calibrationRead on arxiv →
arxivJun 2bullish

AdaCodec: A Predictive Visual Code for Video MLLMs

arXiv:2606.02569v1 Announce Type: cross Abstract: Video is temporally redundant: adjacent frames usually share most objects, background, and layout. Yet existing video multimodal large language models (video MLLMs) usually encode each sampled frame as an independent RGB image, causing visual tokens

ADQW2 models#video#multimodal#compressionRead on arxiv →
arxivMay 25bullish

Scaling-Aware Adapter for Structure-Grounded LLM Reasoning

arXiv:2602.02780v3 Announce Type: replace Abstract: Large language models (LLMs) are enabling reasoning over 2D and 3D structures, yet existing methods remain modality-specific and typically compress structural inputs through sequence-based tokenization or fixed-length query connectors. Such archite

CU1 model#large-language-models#multimodal#reasoningRead on arxiv →
arxivMay 22bullish

DeFacto: Counterfactual Thinking with Images for Enforcing Evidence-Grounded and Faithful Reasoning

arXiv:2509.20912v4 Announce Type: replace Abstract: Recent advances in multimodal language models (MLLMs) have made thinking with images a dominant paradigm for multimodal reasoning. However, existing methods still fail to ensure evidence-answer consistency, where correct answers must be supported b

#multimodal#reasoning#counterfactualRead on arxiv →
arxivMay 16

MediaClaw: Multimodal Intelligent-Agent Platform Technical Report

arXiv:2605.14771v1 Announce Type: new Abstract: MediaClaw is a multimodal agent platform built on the OpenClaw ecosystem. Its core design follows a three-layer architecture of unified abstraction, pluginized extension, and workflow orchestration. The system is intended to address practical deploymen

#multimodal#architecture#artificial-intelligenceRead on arxiv →
arxivMay 16bullish

MHSA: A Lightweight Framework for Mitigating Hallucinations via Steered Attention in LVLMs

arXiv:2605.14966v1 Announce Type: cross Abstract: Large vision-language models (LVLMs) have achieved remarkable performance across diverse multimodal tasks, yet they continue to suffer from hallucinations, generating content that is inconsistent with the visual input. Prior work DHCP (Detecting Hall

MHDH2 models#hallucination#mitigation#multimodalRead on arxiv →
arxivMay 12bullish

Towards Customized Multimodal Role-Play

arXiv:2605.08129v1 Announce Type: new Abstract: Unified multimodal understanding and generation models enable richer human-AI interaction. Yet jointly customizing a character's persona, dialogue style, and visual identity while maintaining output consistency across modalities remains largely unexplo

UN1 model#multimodal#roleplay#character-generationRead on arxiv →
arxivMay 8bullish

MACS: Modality-Aware Capacity Scaling for Efficient Multimodal MoE Inference

arXiv:2605.05225v1 Announce Type: cross Abstract: Mixture-of-Experts Multimodal Large Language Models (MoE MLLMs) suffer from a significant efficiency bottleneck during Expert Parallelism (EP) inference due to the straggler effect. This issue is worsened in the multimodal context, as existing token-

MIMA2 models#multimodal#efficiency#inferenceRead on arxiv →
arxivMay 1bullish

SpecVQA: A Benchmark for Spectral Understanding and Visual Question Answering in Scientific Images

arXiv:2604.28039v1 Announce Type: new Abstract: Spectra are a prevalent yet highly information-dense form of scientific imagery, presenting substantial challenges to multimodal large language models (MLLMs) due to their unstructured and domain-specific characteristics. Here we introduce SpecVQA, a p

#multimodal#benchmark#scientific-researchRead on arxiv →
arxivApr 29

FAIR_XAI: Improving Multimodal Foundation Model Fairness via Explainability for Wellbeing Assessment

arXiv:2604.23786v1 Announce Type: new Abstract: In recent years, the integration of multimodal machine learning in wellbeing assessment has offered transformative potential for monitoring mental health. However, with the rapid advancement of Vision-Language Models (VLMs), their deployment in clinica

PHQW2 models#fairness#explainability#multimodalRead on arxiv →
arxivApr 27bullish

EuropeMedQA Study Protocol: A Multilingual, Multimodal Medical Examination Dataset for Language Model Evaluation

arXiv:2604.14306v2 Announce Type: replace-cross Abstract: While Large Language Models (LLMs) have demonstrated high proficiency on English-centric medical examinations, their performance often declines when faced with non-English languages and multimodal diagnostic tasks. This study protocol describ

LA1 model#multilingual#medical-ai#benchmarkRead on arxiv →
arxivApr 24bullish

Spec-o3: A Tool-Augmented Vision-Language Agent for Rare Celestial Object Candidate Vetting via Automated Spectral Inspection

arXiv:2601.06498v3 Announce Type: replace Abstract: Due to the limited generalization and interpretability of deep learning classifiers, The final vetting of rare celestial object candidates still relies on expert visual inspection--a manually intensive process. In this process, astronomers leverage

SP1 model#astronomy#spectroscopy#multimodalRead on arxiv →
arxivApr 23

Beyond Text-Dominance: Understanding Modality Preference of Omni-modal Large Language Models

arXiv:2604.16902v2 Announce Type: replace Abstract: Native Omni-modal Large Language Models (OLLMs) have shifted from pipeline architectures to unified representation spaces. However, this native integration gives rise to a critical yet underexplored phenomenon: modality preference. To bridge this g

#research#language-models#multimodalRead on arxiv →
arxivApr 21bullish

Multilingual Training and Evaluation Resources for Vision-Language Models

arXiv:2604.18347v1 Announce Type: new Abstract: Vision Language Models (VLMs) achieved rapid progress in the recent years. However, despite their growth, VLMs development is heavily grounded on English, leading to two main limitations: (i) the lack of multilingual and multimodal datasets for trainin

PIPICO3 models#multilingual#multimodal#benchmarkRead on arxiv →
arxivApr 17

Knowing When Not to Answer: Evaluating Abstention in Multimodal Reasoning Systems

arXiv:2604.14799v1 Announce Type: new Abstract: Effective abstention (EA), recognizing evidence insufficiency and refraining from answering, is critical for reliable multimodal systems. Yet existing evaluation paradigms for vision-language models (VLMs) and multi-agent systems (MAS) assume answerabi

#multimodal#evaluation#abstentionRead on arxiv →
arxivApr 9

ValueGround: Evaluating Culture-Conditioned Visual Value Grounding in MLLMs

arXiv:2604.06484v1 Announce Type: new Abstract: Cultural values are expressed not only through language but also through visual scenes and everyday social practices. Yet existing evaluations of cultural values in language models are almost entirely text-only, making it unclear whether models can gro

#multimodal#evaluation#cultureRead on arxiv →
arxivApr 8bullish

HybridKV: Hybrid KV Cache Compression for Efficient Multimodal Large Language Model Inference

arXiv:2604.05887v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have advanced unified reasoning over text, images, and videos, but their inference is hindered by the rapid growth of key-value (KV) caches. Each visual input expands into thousands of tokens, causing caches to

QW1 model#multimodal#compression#optimizationRead on arxiv →
arxivApr 7bullish

PDF Retrieval Augmented Question Answering

arXiv:2506.18027v2 Announce Type: replace Abstract: This paper presents an advancement in Question-Answering (QA) systems using a Retrieval Augmented Generation (RAG) framework to enhance information extraction from PDF files. Recognizing the richness and diversity of data within PDFs--including tex

RELA2 models#question-answering#multimodal#information-extractionRead on arxiv →