:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Yin, Hang, Lin, Zhifeng, Liu, Xin, Sun, Bin, Li, Kan
Format:	Preprint
Published:	2024
Subjects:	Artificial Intelligence
Online Access:	https://arxiv.org/abs/2412.16599
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Do MLLMs Really Understand Space? A Mathematical Reasoning Evaluation
by: Lu, Shuo, et al.
Published: (2026)

Incentivizing Multimodal Reasoning in Large Models for Direct Robot Manipulation
by: Tang, Weiliang, et al.
Published: (2025)

Benchmarking Defeasible Reasoning with Large Language Models -- Initial Experiments and Future Directions
by: Tachmazidis, Ilias, et al.
Published: (2024)

CellVerse: Do Large Language Models Really Understand Cell Biology?
by: Zhang, Fan, et al.
Published: (2025)

Spatial Reasoning in Multimodal Large Language Models: A Survey of Tasks, Benchmarks and Methods
by: Liu, Weichen, et al.
Published: (2025)

WebCompass: Towards Multimodal Web Coding Evaluation for Code Language Models
by: Lei, Xinping, et al.
Published: (2026)

Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation and Beyond
by: Xu, Fangzhi, et al.
Published: (2023)

Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions
by: Song, Shezheng
Published: (2024)

Multimodal Inconsistency Reasoning (MMIR): A New Benchmark for Multimodal Reasoning Models
by: Yan, Qianqi, et al.
Published: (2025)

Improving Multi-Step Reasoning Abilities of Large Language Models with Direct Advantage Policy Optimization
by: Liu, Jiacai, et al.
Published: (2024)

AlgBench: To What Extent Do Large Reasoning Models Understand Algorithms?
by: Sun, Henan, et al.
Published: (2026)

Understanding Financial Reasoning in AI: A Multimodal Benchmark and Error Learning Approach
by: Deng, Shuangyan, et al.
Published: (2025)

Do Audio-Visual Large Language Models Really See and Hear?
by: Selvakumar, Ramaneswaran, et al.
Published: (2026)

Do LLMs Really Think Step-by-step In Implicit Reasoning?
by: Yu, Yijiong
Published: (2024)

Perception, Understanding and Reasoning, A Multimodal Benchmark for Video Fake News Detection
by: Yakun, Cui, et al.
Published: (2025)

MMLU-Reason: Benchmarking Multi-Task Multi-modal Language Understanding and Reasoning
by: Tie, Guiyao, et al.
Published: (2025)

Stitch and Tell: A Structured Multimodal Data Augmentation Method for Spatial Understanding
by: Yin, Hang, et al.
Published: (2025)

How Well Do Multimodal Models Reason on ECG Signals?
by: Xu, Maxwell A., et al.
Published: (2026)

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

What Do Visual Tokens Really Encode? Uncovering Sparsity and Redundancy in Multimodal Large Language Models
by: Fan, Yingqi, et al.
Published: (2026)

Multimodal Chain-of-Thought Reasoning via ChatGPT to Protect Children from Age-Inappropriate Apps
by: Hu, Chuanbo, et al.
Published: (2024)

M$^3$-Med: A Benchmark for Multi-lingual, Multi-modal, and Multi-hop Reasoning in Medical Instructional Video Understanding
by: Liu, Shenxi, et al.
Published: (2025)

ConstraintBench: Benchmarking LLM Constraint Reasoning on Direct Optimization
by: Tso, Joseph, et al.
Published: (2026)

Attention Distance: A Novel Metric for Directed Fuzzing with Large Language Models
by: Bin, Wang, et al.
Published: (2025)

MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI
by: Yue, Xiang, et al.
Published: (2023)

Thinking in Directivity: Speech Large Language Model for Multi-Talker Directional Speech Recognition
by: Xie, Jiamin, et al.
Published: (2025)

Should We Really Edit Language Models? On the Evaluation of Edited Language Models
by: Li, Qi, et al.
Published: (2024)

TempCompass: Do Video LLMs Really Understand Videos?
by: Liu, Yuanxin, et al.
Published: (2024)

MV-MATH: Evaluating Multimodal Math Reasoning in Multi-Visual Contexts
by: Wang, Peijie, et al.
Published: (2025)

GGBench: A Geometric Generative Reasoning Benchmark for Unified Multimodal Models
by: Wei, Jingxuan, et al.
Published: (2025)

Adaptive Dual Reasoner: Large Reasoning Models Can Think Efficiently by Hybrid Reasoning
by: Zhang, Yujian, et al.
Published: (2025)

Direct Preference Optimization of Video Large Multimodal Models from Language Model Reward
by: Zhang, Ruohong, et al.
Published: (2024)

Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and Directions
by: Wang, Xin, et al.
Published: (2022)

Do We Really Need a Large Number of Visual Prompts?
by: Kim, Youngeun, et al.
Published: (2023)

Enhancing Protein Predictive Models via Proteins Data Augmentation: A Benchmark and New Directions
by: Sun, Rui, et al.
Published: (2024)

Benchmarking Multimodal LLMs on Recognition and Understanding over Chemical Tables
by: Zhou, Yitong, et al.
Published: (2025)

Do Multimodal Agents Really Benefit from Tool Use? A Systematic Study of Capability Gains
by: Guo, Garvin, et al.
Published: (2026)

Do They Understand Them? An Updated Evaluation on Nonbinary Pronoun Handling in Large Language Models
by: Tang, Xushuo, et al.
Published: (2025)

LongDocURL: a Comprehensive Multimodal Long Document Benchmark Integrating Understanding, Reasoning, and Locating
by: Deng, Chao, et al.
Published: (2024)

CLEAR: Can Language Models Really Understand Causal Graphs?
by: Chen, Sirui, et al.
Published: (2024)