Daily Arxiv

전 세계에서 발간되는 인공지능 관련 논문을 정리하는 페이지 입니다.
본 페이지는 Google Gemini를 활용해 요약 정리하며, 비영리로 운영 됩니다.
논문에 대한 저작권은 저자 및 해당 기관에 있으며, 공유 시 출처만 명기하면 됩니다.

Self-Questioning Language Models

Beyond risk: A proto-framework for assessing the societal impact of AI systems

Supervised Dynamic Dimension Reduction with Deep Neural Network

EmoSteer-TTS: Fine-Grained and Training-Free Emotion-Controllable Text-to-Speech via Activation Steering

LLMs Have a Heart of Stone: Demystifying the Soft Thinking Ability of Large Reasoning Models

Industrial LLM-based Code Optimization under Regulation: A Mixture-of-Agents Approach

Reliable Evaluation Protocol for Low-Precision Retrieval

Landsat30-AU: A Vision-Language Dataset for Australian Landsat Imagery

Tool-integrated Reinforcement Learning for Repo Deep Search

CauKer: classification time series foundation models can be pretrained on synthetic data only

Context-Adaptive Multi-Prompt Embedding with Large Language Models for Vision-Language Alignment

DMSC: Dynamic Multi-Scale Coordination Framework for Time Series Forecasting

HyCodePolicy: Hybrid Language Controllers for Multimodal Monitoring and Decision in Embodied Agents

Entity Representation Learning Through Onsite-Offsite Graph for Pinterest Ads

Evaluating User Experience in Conversational Recommender Systems: A Systematic Review Across Classical and LLM-Powered Approaches

Spatial-Frequency Aware for Object Detection in RAW Image

Learning Pivoting Manipulation with Force and Vision Feedback Using Optimization-based Demonstrations

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples

ChartM$^3$: Benchmarking Chart Editing with Multimodal Instructions

From Entanglement to Alignment: Representation Space Decomposition for Unsupervised Time Series Domain Adaptation

EcoTransformer: Attention without Multiplication

Bob's Confetti: Phonetic Memorization Attacks in Music and Video Generation

SDBench: A Comprehensive Benchmark Suite for Speaker Diarization

True Multimodal In-Context Learning Needs Attention to the Visual Context

Gauge Flow Models

Zero-Shot Neural Architecture Search with Weighted Response Correlation

The Dark Side of LLMs: Agent-based Attacks for Complete Computer Takeover

CAVGAN: Unifying Jailbreak and Defense of LLMs via Generative Adversarial Attacks on their Internal Representations

VOTE: Vision-Language-Action Optimization with Trajectory Ensemble Voting

A Comparative Study of Specialized LLMs as Dense Retrievers

Sign Spotting Disambiguation using Large Language Models

UnMix-NeRF: Spectral Unmixing Meets Neural Radiance Fields

Thought Anchors: Which LLM Reasoning Steps Matter?

UITron-Speech: Towards Automated GUI Agents Based on Speech Instructions

15,500 Seconds: Lean UAV Classification Using EfficientNet and Lightweight Fine-Tuning

AtmosMJ: Revisiting Gating Mechanism for AI Weather Forecasting Beyond the Year Scale

On the Fundamental Impossibility of Hallucination Control in Large Language Models

Multi-Modal Multi-Task Federated Foundation Models for Next-Generation Extended Reality Systems: Towards Privacy-Preserving Distributed Intelligence in AR/VR/MR

Text-Only Reasoning Unleashes Zero-Shot Multimodal Evaluators

CAIN: Hijacking LLM-Humans Conversations via Malicious System Prompts

Explain Less, Understand More: Jargon Detection via Personalized Parameter-Efficient Fine-tuning

What Lives? A meta-analysis of diverse opinions on the definition of life

A Generative Neural Annealer for Black-Box Combinatorial Optimization

GRILL: Gradient Signal Restoration in Ill-Conditioned Layers to Enhance Adversarial Attacks on Autoencoders

CostFilter-AD: Enhancing Anomaly Detection through Matching Cost Filtering

Mj\"olnir: A Deep Learning Parametrization Framework for Global Lightning Flash Density

RGB-Event based Pedestrian Attribute Recognition: A Benchmark Dataset and An Asymmetric RWKV Fusion Framework

ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning

Beyond Wide-Angle Images: Structure-to-Detail Video Portrait Correction via Unsupervised Spatiotemporal Adaptation

CITRAS: Covariate-Informed Transformer for Time Series Forecasting

Rubric Is All You Need: Enhancing LLM-based Code Evaluation With Question-Specific Rubrics

Empirical Analysis of Sim-and-Real Cotraining of Diffusion Policies for Planar Pushing from Pixels

SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild

NuPlanQA: A Large-Scale Dataset and Benchmark for Multi-View Driving Scene Understanding in Multi-Modal Large Language Models

The Impact of Item-Writing Flaws on Difficulty and Discrimination in Item Response Theory

Through the Magnifying Glass: Adaptive Perception Magnification for Hallucination-Free VLM Decoding

Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning

Pull-Based Query Scheduling for Goal-Oriented Semantic Communication

Accelerating Focal Search in Multi-Agent Path Finding with Tighter Lower Bounds

RAILGUN: A Unified Convolutional Policy for Multi-Agent Path Finding Across Different Environments and Tasks

UltraSTF: Ultra-Compact Model for Large-Scale Spatio-Temporal Forecasting

PTQ1.61: Push the Real Limit of Extremely Low-Bit Post-Training Quantization Methods for Large Language Models

Foundation Model of Electronic Medical Records for Adaptive Risk Estimation

Tool Unlearning for Tool-Augmented LLMs

Vision without Images: End-to-End Computer Vision from Single Compressive Measurements

How Do Generative Models Draw a Software Engineer? A Case Study on Stable Diffusion Bias

3DTTNet: Multimodal Fusion-Based 3D Traversable Terrain Modeling for Off-Road Environments

DOGR: Towards Versatile Visual Document Grounding and Referring

Real-World Offline Reinforcement Learning from Vision Language Model Feedback

Causality-Driven Audits of Model Robustness

AUTALIC: A Dataset for Anti-AUTistic Ableist Language In Context

Beyond Adapter Retrieval: Latent Geometry-Preserving Composition via Sparse Task Projection

pyhgf: A neural network library for predictive coding

Human Bias in the Face of AI: Examining Human Judgment Against Text Labeled as AI Generated

AVG-LLaVA: An Efficient Large Multimodal Model with Adaptive Visual Granularity

Parse Trees Guided LLM Prompt Compression

One Model, Any Conjunctive Query: Graph Neural Networks for Answering Queries over Incomplete Knowledge Graphs

A Value Based Parallel Update MCTS Method for Multi-Agent Cooperative Decision Making of Connected and Automated Vehicles

Fairness Definitions in Language Models Explained

CityLight: A Neighborhood-inclusive Universal Model for Coordinated City-scale Traffic Signal Control

Time Evidence Fusion Network: Multi-source View in Long-Term Time Series Forecasting

Long-Term Visual Object Tracking with Event Cameras: An Associative Memory Augmented Tracker and A Benchmark Dataset

Hulk: A Universal Knowledge Translator for Human-Centric Tasks

From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited

Environmental Sound Classification on An Embedded Hardware Platform

Data Dependency Inference for Industrial Code Generation Based on UML Sequence Diagrams

InqEduAgent: Adaptive AI Learning Partners with Gaussian Process Augmentation

SE-Agent: Self-Evolution Trajectory Optimization in Multi-Step Reasoning with LLM-Based Agents

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization

Higher Gauge Flow Models

Think How to Think: Mitigating Overthinking with Autonomous Difficulty Cognition in Large Reasoning Models

IS-Bench: Evaluating Interactive Safety of VLM-Driven Embodied Agents in Daily Household Tasks

SLR: Automated Synthesis for Scalable Logical Reasoning

The SWE-Bench Illusion: When State-of-the-Art LLMs Remember Instead of Reason

APOLLO: Automated LLM and Lean Collaboration for Advanced Formal Reasoning

Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets

Learning to Inference Adaptively for Multimodal Large Language Models

Efficient rule induction by ignoring pointless rules

Why the Agent Made that Decision: Contrastive Explanation Learning for Reinforcement Learning

Evaluating Detection Thresholds: The Impact of False Positives and Negatives on Super-Resolution Ultrasound Localization Microscopy

Context-Adaptive Multi-Prompt Embedding with Large Language Models for Vision-Language Alignment

Created by

Haebom

저자

Dahun Kim, Anelia Angelova

개요

본 논문은 시각-언어 대조 학습에서 의미 표현을 풍부하게 하기 위한 새로운 방법인 Context-Adaptive Multi-Prompt Embedding을 제안합니다. 기존 CLIP 스타일 모델이 단일 텍스트 임베딩에 의존하는 것과 달리, 본 연구는 입력 텍스트의 다양한 의미적 측면을 포착하는 고유한 적응형 토큰을 각각 포함하는 여러 개의 구조화된 프롬프트를 도입합니다. CLIP 프레임워크 내에서 사전 훈련된 LLM을 텍스트 인코더로 활용하여 모든 프롬프트를 단일 전달 과정에서 공동으로 처리합니다. 결과적으로 생성된 프롬프트 임베딩은 통합된 텍스트 표현으로 결합되어 시각적 특징과의 의미적으로 더 풍부한 정렬을 가능하게 합니다. 의미적 다양성과 표현 품질을 더욱 향상시키기 위해 다양성 규제 손실과 부정 인식 손실을 통합하여 프롬프트 간의 전문화를 장려하고 대조적 차별을 개선합니다. 본 방법은 이미지-텍스트 및 비디오-텍스트 검색 벤치마크에서 일관된 성능 향상을 달성합니다.

시사점, 한계점

•

시사점:

◦

다중 프롬프트를 활용하여 시각-언어 대조 학습에서 의미 표현의 풍부함을 향상시킬 수 있음을 보여줍니다.

◦

사전 훈련된 LLM을 효과적으로 활용하여 다양한 의미적 측면을 포착하는 방법을 제시합니다.

◦

다양성 규제 손실과 부정 인식 손실을 통해 성능 향상을 이끌어냅니다.

◦

이미지-텍스트 및 비디오-텍스트 검색 과제에서 성능 향상을 실험적으로 검증합니다.

•

한계점:

◦

제안된 방법의 계산 비용이 기존 방법보다 높을 수 있습니다. (다중 프롬프트 처리)

◦

특정 LLM에 의존적인 부분이 존재할 수 있습니다.

◦

다양성 규제 손실 및 부정 인식 손실의 최적 하이퍼파라미터 설정에 대한 추가적인 연구가 필요할 수 있습니다.

◦

사용된 벤치마크의 한계로 인해 일반화 성능에 대한 추가적인 검증이 필요할 수 있습니다.

Made with Slashpage