Daily Arxiv

전 세계에서 발간되는 인공지능 관련 논문을 정리하는 페이지 입니다.
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논문에 대한 저작권은 저자 및 해당 기관에 있으며, 공유 시 출처만 명기하면 됩니다.

Grasp Any Region: Towards Precise, Contextual Pixel Understanding for Multimodal LLMs

ScaleNet: Scaling up Pretrained Neural Networks with Incremental Parameters

Text or Pixels? It Takes Half: On the Token Efficiency of Visual Text Inputs in Multimodal LLMs

The Emergence of Complex Behavior in Large-Scale Ecological Environments

PLAGUE: Plug-and-play framework for Lifelong Adaptive Generation of Multi-turn Exploits

Multi-Agent Design Assistant for the Simulation of Inertial Fusion Energy

MUG-V 10B: High-efficiency Training Pipeline for Large Video Generation Models

Context-Aware Pseudo-Label Scoring for Zero-Shot Video Summarization

SparseWorld: A Flexible, Adaptive, and Efficient 4D Occupancy World Model Powered by Sparse and Dynamic Queries

Efficient Vision-Language-Action Models for Embodied Manipulation: A Systematic Survey

Agentic Inequality

Natural Language Processing for Cardiology: A Narrative Review

Can LLMs Correct Themselves? A Benchmark of Self-Correction in LLMs

VERA-MH Concept Paper

The Coverage Principle: How Pre-Training Enables Post-Training

LeapFactual: Reliable Visual Counterfactual Explanation Using Conditional Flow Matching

Mathematics with large language models as provers and verifiers

Coordinated Strategies in Realistic Air Combat by Hierarchical Multi-Agent Reinforcement Learning

Learning What Matters: Steering Diffusion via Spectrally Anisotropic Forward Noise

dInfer: An Efficient Inference Framework for Diffusion Language Models

gLSTM: Mitigating Over-Squashing by Increasing Storage Capacity

BlockGPT: Spatio-Temporal Modelling of Rainfall via Frame-Level Autoregression

Improving Metacognition and Uncertainty Communication in Language Models

ALHD: A Large-Scale and Multigenre Benchmark Dataset for Arabic LLM-Generated Text Detection

ACT: Agentic Classification Tree

Q-Palette: Fractional-Bit Quantizers Toward Optimal Bit Allocation for Efficient LLM Deployment

SPiDR: A Simple Approach for Zero-Shot Safety in Sim-to-Real Transfer

Training-Free Label Space Alignment for Universal Domain Adaptation

Conformal Prediction for Time-series Forecasting with Change Points

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning

Breaking the Exploration Bottleneck: Rubric-Scaffolded Reinforcement Learning for General LLM Reasoning

Who's Asking? Investigating Bias Through the Lens of Disability Framed Queries in LLMs

MeanAudio: Fast and Faithful Text-to-Audio Generation with Mean Flows

Explaining Time Series Classifiers with PHAR: Rule Extraction and Fusion from Post-hoc Attributions

RoboMemory: A Brain-inspired Multi-memory Agentic Framework for Interactive Environmental Learning in Physical Embodied Systems

Where are we with calibration under dataset shift in image classification?

On the Effectiveness of Methods and Metrics for Explainable AI in Remote Sensing Image Scene Classification

PRING: Rethinking Protein-Protein Interaction Prediction from Pairs to Graphs

PULSE: Practical Evaluation Scenarios for Large Multimodal Model Unlearning

Pay Attention to Small Weights

Understanding Reasoning in Thinking Language Models via Steering Vectors

Chiron-o1: Igniting Multimodal Large Language Models towards Generalizable Medical Reasoning via Mentor-Intern Collaborative Search

With Limited Data for Multimodal Alignment, Let the STRUCTURE Guide You

Improved Exploration in GFlownets via Enhanced Epistemic Neural Networks

One-Step Diffusion for Detail-Rich and Temporally Consistent Video Super-Resolution

Flexible-length Text Infilling for Discrete Diffusion Models

GUARD: Guided Unlearning and Retention via Data Attribution for Large Language Models

TimeWak: Temporal Chained-Hashing Watermark for Time Series Data

Horizon Reduction Makes RL Scalable

QoQ-Med: Building Multimodal Clinical Foundation Models with Domain-Aware GRPO Training

Learning from Videos for 3D World: Enhancing MLLMs with 3D Vision Geometry Priors

Unlearned but Not Forgotten: Data Extraction after Exact Unlearning in LLM

MLR-Bench: Evaluating AI Agents on Open-Ended Machine Learning Research

High-order Equivariant Flow Matching for Density Functional Theory Hamiltonian Prediction

Evaluating NLP Embedding Models for Handling Science-Specific Symbolic Expressions in Student Texts

An Analysis of Concept Bottleneck Models: Measuring, Understanding, and Mitigating the Impact of Noisy Annotations

Improving planning and MBRL with temporally-extended actions

CtrlDiff: Boosting Large Diffusion Language Models with Dynamic Block Prediction and Controllable Generation

Concept-Guided Interpretability via Neural Chunking

A Multi-Task Foundation Model for Wireless Channel Representation Using Contrastive and Masked Autoencoder Learning

Memorization-Compression Cycles Improve Generalization

LongCodeBench: Evaluating Coding LLMs at 1M Context Windows

Quantum Natural Language Processing: A Comprehensive Review of Models, Methods, and Applications

Merging Embedded Topics with Optimal Transport for Online Topic Modeling on Data Streams

NAACL2025 Tutorial: Adaptation of Large Language Models

GeoBenchX: Benchmarking LLMs in Agent Solving Multistep Geospatial Tasks

Provably Efficient Reward Transfer in Reinforcement Learning with Discrete Markov Decision Processes

TinySQL: A Progressive Text-to-SQL Dataset for Mechanistic Interpretability Research

PTFA: An LLM-based Agent that Facilitates Online Consensus Building through Parallel Thinking

EgoBlind: Towards Egocentric Visual Assistance for the Blind

Merge then Realign: Simple and Effective Modality-Incremental Continual Learning for Multimodal LLMs

FairGen: Controlling Sensitive Attributes for Fair Generations in Diffusion Models via Adaptive Latent Guidance

Towards Enhanced Image Generation Via Multi-modal Chain of Thought in Unified Generative Models

Probing Perceptual Constancy in Large Vision-Language Models

An Efficient Local Search Approach for Polarized Community Discovery in Signed Networks

ModServe: Modality- and Stage-Aware Resource Disaggregation for Scalable Multimodal Model Serving

Graph Representation Learning with Diffusion Generative Models

Demystifying Domain-adaptive Post-training for Financial LLMs

Explainable fault and severity classification for rolling element bearings using Kolmogorov-Arnold networks

Open-World Drone Active Tracking with Goal-Centered Rewards

Fast MRI for All: Bridging Access Gaps by Training without Raw Data

Benchmarking Large Language Models with Integer Sequence Generation Tasks

Model-based Large Language Model Customization as Service

Learning Linear Attention in Polynomial Time

Unveiling Transformer Perception by Exploring Input Manifolds

PARCO: Parallel AutoRegressive Models for Multi-Agent Combinatorial Optimization

Estimating Long-term Heterogeneous Dose-response Curve: Generalization Bound Leveraging Optimal Transport Weights

LICO: Large Language Models for In-Context Molecular Optimization

Towards Context-Aware Domain Generalization: Understanding the Benefits and Limits of Marginal Transfer Learning

Embedding in Recommender Systems: A Survey

FP-IRL: Fokker-Planck Inverse Reinforcement Learning -- A Physics-Constrained Approach to Markov Decision Processes

FST.ai 2.0: An Explainable AI Ecosystem for Fair, Fast, and Inclusive Decision-Making in Olympic and Paralympic Taekwondo

A Brain Cell Type Resource Created by Large Language Models and a Multi-Agent AI System for Collaborative Community Annotation

MedRule-KG: A Knowledge-Graph--Steered Scaffold for Mathematical Reasoning with a Lightweight Verifier

The Right to Be Remembered: Preserving Maximally Truthful Digital Memory in the Age of AI

Invoice Information Extraction: Methods and Performance Evaluation

RoboGPT-R1: Enhancing Robot Planning with Reinforcement Learning

ARM-FM: Automated Reward Machines via Foundation Models for Compositional Reinforcement Learning

Base Models Know How to Reason, Thinking Models Learn When

The Open Syndrome Definition

With Limited Data for Multimodal Alignment, Let the STRUCTURE Guide You

Created by

Haebom

저자

Fabian Groger, Shuo Wen, Huyen Le, Maria Brbic

개요

본 논문은 사전 훈련된 단일 모달리티 기반 모델을 정렬하여 제한된 양의 페어링된 데이터를 사용하여 멀티모달 모델을 구축하는 가능성을 탐구합니다. STRUCTURE라는 효과적인 정규화 기법을 도입하여 단일 모달리티 인코더의 잠재 공간의 인접 기하학을 보존합니다. 또한, 마지막 레이어를 정렬하는 것이 최적이 아닐 수 있음을 보여주고, 모달리티 간 표현 유사성이 가장 높은 레이어를 정렬하는 것이 유리함을 입증합니다. 이러한 기술들을 통해 적은 양의 데이터 (일반적으로 사용되는 데이터의 1% 미만)로 24개의 제로샷 이미지 분류 및 검색 벤치마크에서 상당한 성능 향상을 달성했습니다.

시사점, 한계점

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제한된 페어링된 데이터를 사용하여 고품질 멀티모달 모델을 구축하는 데 성공했습니다.

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STRUCTURE 정규화 기법을 통해 단일 모달리티 인코더의 잠재 공간 구조를 보존하여 성능을 향상시켰습니다.

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모달리티 간 표현 유사성이 높은 레이어를 정렬하는 것이 효과적임을 입증했습니다.

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평균 51.6%의 분류 성능 향상 및 91.8%의 검색 성능 향상을 달성했습니다.

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자원 제약적인 환경에서의 멀티모달 학습에 대한 유망한 해결책을 제시합니다.

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논문에서 사용한 데이터셋 및 설정에 대한 구체적인 설명이 부족할 수 있습니다.

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다른 유형의 멀티모달 데이터 (예: 비디오-텍스트)에 대한 일반화 가능성에 대한 추가 연구가 필요할 수 있습니다.

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모델의 확장성 및 대규모 데이터셋에서의 성능에 대한 추가적인 실험이 필요할 수 있습니다.

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