Daily Arxiv

전 세계에서 발간되는 인공지능 관련 논문을 정리하는 페이지 입니다.
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GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning

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Reducing Variability of Multiple Instance Learning Methods for Digital Pathology

Positioning AI Tools to Support Online Harm Reduction Practice: Applications and Design Directions

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues

Visual Structures Helps Visual Reasoning: Addressing the Binding Problem in VLMs

BioPars: A Pretrained Biomedical Large Language Model for Persian Biomedical Text Mining

TRACED: Transition-aware Regret Approximation with Co-learnability for Environment Design

Towards Safety Evaluations of Theory of Mind in Large Language Models

Fair Algorithms with Probing for Multi-Agent Multi-Armed Bandits

GraphGSOcc: Semantic-Geometric Graph Transformer with Dynamic-Static Decoupling for 3D Gaussian Splatting-based Occupancy Prediction

Screen Hijack: Visual Poisoning of VLM Agents in Mobile Environments

15,500 Seconds: Lean UAV Classification Leveraging PEFT and Pre-Trained Networks

Tightly-Coupled LiDAR-IMU-Leg Odometry with Online Learned Leg Kinematics Incorporating Foot Tactile Information

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers

BIS Reasoning 1.0: The First Large-Scale Japanese Benchmark for Belief-Inconsistent Syllogistic Reasoning

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Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems

Real-Time Blind Defocus Deblurring for Earth Observation: The IMAGIN-e Mission Approach

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Self-reflective Uncertainties: Do LLMs Know Their Internal Answer Distribution?

FastMamba: A High-Speed and Efficient Mamba Accelerator on FPGA with Accurate Quantization

Pre-training Large Memory Language Models with Internal and External Knowledge

Towards Universal Semantics With Large Language Models

Aitomia: Your Intelligent Assistant for AI-Driven Atomistic and Quantum Chemical Simulations

Enhancing Robustness to Missing Modalities through Clustered Federated Learning

Perceiving Beyond Language Priors: Enhancing Visual Comprehension and Attention in Multimodal Models

LZ Penalty: An information-theoretic repetition penalty for autoregressive language models

Towards Cardiac MRI Foundation Models: Comprehensive Visual-Tabular Representations for Whole-Heart Assessment and Beyond

TARO: Timestep-Adaptive Representation Alignment with Onset-Aware Conditioning for Synchronized Video-to-Audio Synthesis

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Real-is-Sim: Bridging the Sim-to-Real Gap with a Dynamic Digital Twin

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SFO: Piloting VLM Feedback for Offline RL

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A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation

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GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning

Created by

Haebom

저자

V Team, Wenyi Hong, Wenmeng Yu, Xiaotao Gu, Guo Wang, Guobing Gan, Haomiao Tang, Jiale Cheng, Ji Qi, Junhui Ji, Lihang Pan, Shuaiqi Duan, Weihan Wang, Yan Wang, Yean Cheng, Zehai He, Zhe Su, Zhen Yang, Ziyang Pan, Aohan Zeng, Baoxu Wang, Boyan Shi, Changyu Pang, Chenhui Zhang, Da Yin, Fan Yang, Guoqing Chen, Jiazheng Xu, Jiali Chen, Jing Chen, Jinhao Chen, Jinghao Lin, Jinjiang Wang, Junjie Chen, Leqi Lei, Letian Gong, Leyi Pan, Mingzhi Zhang, Qinkai Zheng, Sheng Yang, Shi Zhong, Shiyu Huang, Shuyuan Zhao, Siyan Xue, Shangqin Tu, Shengbiao Meng, Tianshu Zhang, Tianwei Luo, Tianxiang Hao, Wenkai Li, Wei Jia, Xin Lyu, Xuancheng Huang, Yanling Wang, Yadong Xue, Yanfeng Wang, Yifan An, Yifan Du, Yiming Shi, Yiheng Huang, Yilin Niu, Yuan Wang, Yuanchang Yue, Yuchen Li, Yutao Zhang, Yuxuan Zhang, Zhanxiao Du, Zhenyu Hou, Zhao Xue, Zhengxiao Du, Zihan Wang, Peng Zhang, Debing Liu, Bin Xu, Juanzi Li, Minlie Huang, Yuxiao Dong, Jie Tang

개요

GLM-4.1V-Thinking은 범용적인 다중 모달 이해 및 추론을 향상시키기 위해 설계된 시각-언어 모델(VLM)입니다. 본 논문에서는 추론 중심 훈련 프레임워크 개발에 대한 주요 결과를 공유합니다. 대규모 사전 훈련을 통해 상당한 잠재력을 가진 강력한 시각 기반 모델을 개발하고, 이후 강화 학습과 커리큘럼 샘플링(RLCS)을 제안하여 다양한 작업(STEM 문제 해결, 비디오 이해, 콘텐츠 인식, 코딩, 그라운딩, GUI 기반 에이전트, 장문 이해 등)에서 모델의 잠재력을 최대한 발휘하도록 합니다. 본 연구에서는 90억 매개변수를 가진 GLM-4.1V-9B-Thinking을 오픈소스로 공개하며, 이는 유사한 크기의 모델 중 최첨단 성능을 달성합니다. 28개의 공개 벤치마크에 대한 종합적인 평가에서, 본 모델은 거의 모든 작업에서 Qwen2.5-VL-7B를 능가하며, 훨씬 큰 Qwen2.5-VL-72B와 비교하여 18개의 벤치마크에서 동등하거나 우수한 성능을 달성합니다. 특히 GLM-4.1V-9B-Thinking은 장문 이해 및 STEM 추론과 같은 어려운 작업에서 GPT-4o와 같은 비공개 모델과 비교하여 경쟁력 있거나 우수한 성능을 보여줍니다. 코드, 모델 및 자세한 정보는 https://github.com/THUDM/GLM-4.1V-Thinking 에서 확인할 수 있습니다.

GitHub - THUDM/GLM-4.1V-Thinking: GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning.

GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning. - THUDM/GLM-4.1V-Thinking

시사점, 한계점

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시사점:

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대규모 사전 훈련과 RLCS를 통해 다양한 작업에서 우수한 성능을 달성하는 경량화된 VLM을 개발했습니다.

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유사 규모의 모델 중 최첨단 성능을 달성하며, 훨씬 큰 모델과 비교해도 경쟁력 있는 성능을 보입니다.

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장문 이해 및 STEM 추론과 같은 어려운 작업에서도 우수한 성능을 보여줍니다.

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모델과 코드를 오픈소스로 공개하여 연구 발전에 기여합니다.

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한계점:

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본 논문에서 제시된 벤치마크의 종류 및 수가 한계일 수 있습니다. 더욱 다양하고 광범위한 벤치마크 평가가 필요합니다.

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특정 작업에 대한 성능 향상의 이유에 대한 자세한 분석이 부족할 수 있습니다. 내부 메커니즘에 대한 더 깊이 있는 연구가 필요합니다.

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모델의 에너지 효율성, 혹은 환경적 영향에 대한 논의가 부족합니다.

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