Copertina

The Expense of Seeing: Attaining Trustworthy Multimodal Reasoning Within the Monolithic Paradigm

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autore principale:	Goyal, Karan
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Computer Vision and Pattern Recognition Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

CAGE: Bridging the Accuracy-Aesthetics Gap in Educational Diagrams via Code-Anchored Generative Enhancement
di: Kukreja, Dikshant, et al.
Pubblicazione: (2026)

DISSECT: Diagnosing Where Vision Ends and Language Priors Begin in Scientific VLMs
di: Kukreja, Dikshant, et al.
Pubblicazione: (2026)

Beyond Seeing: Evaluating Multimodal LLMs on Tool-Enabled Image Perception, Transformation, and Reasoning
di: Guo, Xingang, et al.
Pubblicazione: (2025)

Language Models Can See Better: Visual Contrastive Decoding For LLM Multimodal Reasoning
di: Pang, Yuqi, et al.
Pubblicazione: (2025)

More Thinking, Less Seeing? Assessing Amplified Hallucination in Multimodal Reasoning Models
di: Liu, Chengzhi, et al.
Pubblicazione: (2025)

Pixelis: Reasoning in Pixels, from Seeing to Acting
di: Zhou, Yunpeng
Pubblicazione: (2026)

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms
di: Richards, Luke E., et al.
Pubblicazione: (2025)

Multimodal Language Models See Better When They Look Shallower
di: Chen, Haoran, et al.
Pubblicazione: (2025)

Can Multimodal LLMs See Science Instruction? Benchmarking Pedagogical Reasoning in K-12 Classroom Videos
di: Shen, Yixuan, et al.
Pubblicazione: (2026)

ARIADNE: A Perception-Reasoning Synergy Framework for Trustworthy Coronary Angiography Analysis
di: Jin, Zhan, et al.
Pubblicazione: (2026)

VG-CoT: Towards Trustworthy Visual Reasoning via Grounded Chain-of-Thought
di: Lim, Byeonggeuk, et al.
Pubblicazione: (2026)

Do MLLMs Really See It: Reinforcing Visual Attention in Multimodal LLMs
di: Ou, Siqu, et al.
Pubblicazione: (2026)

Bad Seeing or Bad Thinking? Rewarding Perception for Vision-Language Reasoning
di: Wang, Haozhe, et al.
Pubblicazione: (2026)

MediSee: Reasoning-based Pixel-level Perception in Medical Images
di: Tong, Qinyue, et al.
Pubblicazione: (2025)

Think Twice to See More: Iterative Visual Reasoning in Medical VLMs
di: Chen, Kaitao, et al.
Pubblicazione: (2025)

See What You Are Told: Visual Attention Sink in Large Multimodal Models
di: Kang, Seil, et al.
Pubblicazione: (2025)

Seeing is Understanding: Unlocking Causal Attention into Modality-Mutual Attention for Multimodal LLMs
di: Wang, Wei-Yao, et al.
Pubblicazione: (2025)

See&Trek: Training-Free Spatial Prompting for Multimodal Large Language Model
di: Li, Pengteng, et al.
Pubblicazione: (2025)

Learning to See the Elephant in the Room: Self-Supervised Context Reasoning in Humans and AI
di: Liu, Xiao, et al.
Pubblicazione: (2022)

Seeing but Not Thinking: Routing Distraction in Multimodal Mixture-of-Experts
di: Xu, Haolei, et al.
Pubblicazione: (2026)

A Cognitive Paradigm Approach to Probe the Perception-Reasoning Interface in VLMs
di: Vaishnav, Mohit, et al.
Pubblicazione: (2025)

MMEvalPro: Calibrating Multimodal Benchmarks Towards Trustworthy and Efficient Evaluation
di: Huang, Jinsheng, et al.
Pubblicazione: (2024)

Fair-Eye Net: A Fair, Trustworthy, Multimodal Integrated Glaucoma Full Chain AI System
di: Wei, Wenbin, et al.
Pubblicazione: (2026)

Seeing is Not Reasoning: MVPBench for Graph-based Evaluation of Multi-path Visual Physical CoT
di: Dong, Zhuobai, et al.
Pubblicazione: (2025)

Skin-R1: Toward Trustworthy Clinical Reasoning for Dermatological Diagnosis
di: Liu, Zehao, et al.
Pubblicazione: (2025)

Fill the GAP: A Granular Alignment Paradigm for Visual Reasoning in Multimodal Large Language Models
di: Miao, Yanting, et al.
Pubblicazione: (2026)

BLINK: Multimodal Large Language Models Can See but Not Perceive
di: Fu, Xingyu, et al.
Pubblicazione: (2024)

Seeing the Big Picture: Evaluating Multimodal LLMs' Ability to Interpret and Grade Handwritten Student Work
di: Henkel, Owen, et al.
Pubblicazione: (2025)

See Further, Think Deeper: Advancing VLM's Reasoning Ability with Low-level Visual Cues and Reflection
di: Wu, Zhiheng, et al.
Pubblicazione: (2026)

Beyond CLIP: Knowledge-Enhanced Multimodal Transformers for Cross-Modal Alignment in Diabetic Retinopathy Diagnosis
di: Samanta, Argha Kamal, et al.
Pubblicazione: (2025)

KAN See In the Dark
di: Ning, Aoxiang, et al.
Pubblicazione: (2024)

Can't See the Forest for the Trees: Benchmarking Multimodal Safety Awareness for Multimodal LLMs
di: Wang, Wenxuan, et al.
Pubblicazione: (2025)

The Paradigm Shift: A Comprehensive Survey on Large Vision Language Models for Multimodal Fake News Detection
di: Ai, Wei, et al.
Pubblicazione: (2026)

Towards a Multimodal Document-grounded Conversational AI System for Education
di: Taneja, Karan, et al.
Pubblicazione: (2025)

Seeing Culture: A Benchmark for Visual Reasoning and Grounding
di: Satar, Burak, et al.
Pubblicazione: (2025)

Hide to See: Reasoning-prefix Masking for Visual-anchored Thinking in VLM Distillation
di: Yu, Seonghoon, et al.
Pubblicazione: (2026)

From Sparse Decisions to Dense Reasoning: A Multi-attribute Trajectory Paradigm for Multimodal Moderation
di: Gu, Tianle, et al.
Pubblicazione: (2026)

Bringing together invertible UNets with invertible attention modules for memory-efficient diffusion models
di: Jain, Karan, et al.
Pubblicazione: (2025)

Trustworthy Large Models in Vision: A Survey
di: Guo, Ziyan, et al.
Pubblicazione: (2023)

Decoupling the Image Perception and Multimodal Reasoning for Reasoning Segmentation with Digital Twin Representations
di: Li, Yizhen, et al.
Pubblicazione: (2025)