Papers17

#multimodal large language models

MAD: Modality-Adaptive Decoding for Mitigating Cross-Modal Hallucinations in Multimodal Large Language Models

Sangyun Chung, Se Yeon Kim et al.Jan 29arXiv

Multimodal AI models can mix up what they see and what they hear, making things up across senses; this is called cross-modal hallucination.

#multimodal large language models#cross-modal hallucination#contrastive decoding

WorldVQA: Measuring Atomic World Knowledge in Multimodal Large Language Models

Intermediate

Runjie Zhou, Youbo Shao et al.Jan 28arXiv

WorldVQA is a new test that checks if multimodal AI models can correctly name what they see in pictures without doing extra reasoning.

#WorldVQA#atomic visual knowledge#multimodal large language models

AdaReasoner: Dynamic Tool Orchestration for Iterative Visual Reasoning

Intermediate

Mingyang Song, Haoyu Sun et al.Jan 26arXiv

AdaReasoner teaches AI to pick the right visual tools, use them in the right order, and stop using them when they aren’t helping.

#AdaReasoner#dynamic tool orchestration#multimodal large language models

AVMeme Exam: A Multimodal Multilingual Multicultural Benchmark for LLMs' Contextual and Cultural Knowledge and Thinking

Intermediate

Xilin Jiang, Qiaolin Wang et al.Jan 25arXiv

AVMeme Exam is a new test made by humans that checks if AI can understand famous internet audio and video clips the way people do.

#AVMeme Exam#multimodal large language models#audio-visual memes

SIN-Bench: Tracing Native Evidence Chains in Long-Context Multimodal Scientific Interleaved Literature

Intermediate

Yiming Ren, Junjie Wang et al.Jan 15arXiv

The paper introduces SIN-Bench, a new way to test AI that read long scientific papers by forcing them to show exactly where their answers come from.

#multimodal large language models#long-context reasoning#evidence chains

BabyVision: Visual Reasoning Beyond Language

Intermediate

Liang Chen, Weichu Xie et al.Jan 10arXiv

BabyVision is a new test that checks if AI can handle the same basic picture puzzles that young children can do, without leaning on language tricks.

#BabyVision#visual reasoning#multimodal large language models

CogFlow: Bridging Perception and Reasoning through Knowledge Internalization for Visual Mathematical Problem Solving

Intermediate

Shuhang Chen, Yunqiu Xu et al.Jan 5arXiv

This paper teaches AI to solve diagram-based math problems by copying how people think: first see (perception), then make sense of what you saw (internalization), and finally reason (solve the problem).

#visual mathematical reasoning#multimodal large language models#perception-reasoning alignment

UniPercept: Towards Unified Perceptual-Level Image Understanding across Aesthetics, Quality, Structure, and Texture

Intermediate

Shuo Cao, Jiayang Li et al.Dec 25arXiv

This paper teaches AI to notice not just what is in a picture, but how the picture looks and feels to people.

#perceptual image understanding#image aesthetics assessment (IAA)#image quality assessment (IQA)

Robust-R1: Degradation-Aware Reasoning for Robust Visual Understanding

Beginner

Jiaqi Tang, Jianmin Chen et al.Dec 19arXiv

Robust-R1 teaches vision-language models to notice how a picture is damaged, think through what that damage hides, and then answer as if the picture were clear.

#Robust-R1#degradation-aware reasoning#multimodal large language models

GroundingME: Exposing the Visual Grounding Gap in MLLMs through Multi-Dimensional Evaluation

Intermediate

Rang Li, Lei Li et al.Dec 19arXiv

Visual grounding is when an AI finds the exact thing in a picture that a sentence is talking about, and this paper shows today’s big vision-language AIs are not as good at it as we thought.

#visual grounding#multimodal large language models#benchmark

4D-RGPT: Toward Region-level 4D Understanding via Perceptual Distillation

Intermediate

Chiao-An Yang, Ryo Hachiuma et al.Dec 18arXiv

This paper teaches a video-understanding AI to think in 3D plus time (4D) so it can answer questions about specific objects moving in videos.

#4D perception#multimodal large language models#perceptual distillation

Differences That Matter: Auditing Models for Capability Gap Discovery and Rectification

Intermediate

Qihao Liu, Chengzhi Mao et al.Dec 18arXiv

AuditDM is a friendly 'auditor' model that hunts for where vision-language models get things wrong and then creates the right practice to fix them.

#AuditDM#model auditing#cross-model divergence

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