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.

Merge-of-Thought Distillation

OTESGN: Optimal Transport-Enhanced Syntactic-Semantic Graph Networks for Aspect-Based Sentiment Analysis

MESH -- Understanding Videos Like Human: Measuring Hallucinations in Large Video Models

Adapting Vision-Language Models for Neutrino Event Classification in High-Energy Physics

Symmetry-Guided Multi-Agent Inverse Reinforcement Learning

AU-Harness: An Open-Source Toolkit for Holistic Evaluation of Audio LLMs

Expert-Guided Explainable Few-Shot Learning for Medical Image Diagnosis

Towards Generalized Routing: Model and Agent Orchestration for Adaptive and Efficient Inference

MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining

Demo: Healthcare Agent Orchestrator (HAO) for Patient Summarization in Molecular Tumor Boards

Focusing by Contrastive Attention: Enhancing VLMs' Visual Reasoning

Beyond the Pre-Service Horizon: Infusing In-Service Behavior for Improved Financial Risk Forecasting

On Synthesis of Timed Regular Expressions

TinyDef-DETR: A DETR-based Framework for Defect Detection in Transmission Lines from UAV Imagery

LiDAR-BIND-T: Improved and Temporally Consistent Sensor Modality Translation and Fusion for Robotic Applications

From Vision to Validation: A Theory- and Data-Driven Construction of a GCC-Specific AI Adoption Index

A Comprehensive Guide to Differential Privacy: From Theory to User Expectations

The Architecture of AI Transformation: Four Strategic Patterns and an Emerging Frontier

FLM-Audio: Natural Monologues Improves Native Full-Duplex Chatbots via Dual Training

Deep Learning-Based Rock Particulate Classification Using Attention-Enhanced ConvNeXt

The Information Dynamics of Generative Diffusion

Data-Augmented Few-Shot Neural Stencil Emulation for System Identification of Computer Models

Group Expectation Policy Optimization for Heterogeneous Reinforcement Learning

Pretrained Conformers for Audio Fingerprinting and Retrieval

Towards Scalable Training for Handwritten Mathematical Expression Recognition

To Theoretically Understand Transformer-Based In-Context Learning for Optimizing CSMA

Klear-CodeTest: Scalable Test Case Generation for Code Reinforcement Learning

HiD-VAE: Interpretable Generative Recommendation via Hierarchical and Disentangled Semantic IDs

MagicGUI: A Foundational Mobile GUI Agent with Scalable Data Pipeline and Reinforcement Fine-tuning

villa-X: Enhancing Latent Action Modeling in Vision-Language-Action Models

New Kid in the Classroom: Exploring Student Perceptions of AI Coding Assistants

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test?

Uncertainty-aware Diffusion and Reinforcement Learning for Joint Plane Localization and Anomaly Diagnosis in 3D Ultrasound

Uncertainty Estimation by Human Perception versus Neural Models

Persistent Homology of Topic Networks for the Prediction of Reader Curiosity

Task Matters: Knowledge Requirements Shape LLM Responses to Context-Memory Conflict

Crack Path Prediction with Operator Learning using Discrete Particle System data Generation

Diffusion Graph Neural Networks for Robustness in Olfaction Sensors and Datasets

MM-Prompt: Cross-Modal Prompt Tuning for Continual Visual Question Answering

An Ontology-Driven Graph RAG for Legal Norms: A Structural, Temporal, and Deterministic Approach

Combating Falsification of Speech Videos with Live Optical Signatures (Extended Version)

Early Exit and Multi Stage Knowledge Distillation in VLMs for Video Summarization

Critical Challenges and Guidelines in Evaluating Synthetic Tabular Data: A Systematic Review

Parasite: A Steganography-based Backdoor Attack Framework for Diffusion Models

Towards Adaptive Memory-Based Optimization for Enhanced Retrieval-Augmented Generation

Entropy-Gated Branching for Efficient Test-Time Reasoning

SWI: Speaking with Intent in Large Language Models

Byzantine-Robust Federated Learning Using Generative Adversarial Networks

VeriSafe Agent: Safeguarding Mobile GUI Agent via Logic-based Action Verification

MIND: Towards Immersive Psychological Healing with Multi-agent Inner Dialogue

V-HOP: Visuo-Haptic 6D Object Pose Tracking

EgoAgent: A Joint Predictive Agent Model in Egocentric Worlds

Knowledge-Guided Biomarker Identification for Label-Free Single-Cell RNA-Seq Data: A Reinforcement Learning Perspective

MERaLiON-SpeechEncoder: Towards a Speech Foundation Model for Singapore and Beyond

RED: Unleashing Token-Level Rewards from Holistic Feedback via Reward Redistribution

IDEATOR: Jailbreaking and Benchmarking Large Vision-Language Models Using Themselves

DeepVoting: Learning and Fine-Tuning Voting Rules with Canonical Embeddings

Rethinking Disentanglement under Dependent Factors of Variation

Discovering physical laws with parallel symbolic enumeration

Semantic Augmentation in Images using Language

Algorithmic Collusion by Large Language Models

A minimal coalition logic

Deep Reinforcement Learning for Inventory Networks: Toward Reliable Policy Optimization

Inconsistency Handling in Prioritized Databases with Universal Constraints: Complexity Analysis and Links with Active Integrity Constraints

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference

CogGuide: Human-Like Guidance for Zero-Shot Omni-Modal Reasoning

TreeGPT: Pure TreeFFN Encoder-Decoder Architecture for Structured Reasoning Without Attention Mechanisms

Robix: A Unified Model for Robot Interaction, Reasoning and Planning

KROMA: Ontology Matching with Knowledge Retrieval and Large Language Models

Scaling LLM Planning: NL2FLOW for Parametric Problem Generation and Rigorous Evaluation

Optimizing Length Compression in Large Reasoning Models

LLMs for sensory-motor control: Combining in-context and iterative learning

Effort-aware Fairness: Incorporating a Philosophy-informed, Human-centered Notion of Effort into Algorithmic Fairness Metrics

Simulating Human-like Daily Activities with Desire-driven Autonomy

Enhancing Few-Shot Transfer Learning with Optimized Multi-Task Prompt Tuning through Modular Prompt Composition

ButterflyQuant: Ultra-low-bit LLM Quantization through Learnable Orthogonal Butterfly Transforms

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models

SimpleVLA-RL: Scaling VLA Training via Reinforcement Learning

Feasibility-Guided Fair Adaptive Offline Reinforcement Learning for Medicaid Care Management

Retrieval-Augmented Generation for Reliable Interpretation of Radio Regulations

Explaining Concept Drift through the Evolution of Group Counterfactuals

LoCoBench: A Benchmark for Long-Context Large Language Models in Complex Software Engineering

Mechanistic Learning with Guided Diffusion Models to Predict Spatio-Temporal Brain Tumor Growth

Graph Alignment via Dual-Pass Spectral Encoding and Latent Space Communication

ObjectReact: Learning Object-Relative Control for Visual Navigation

Fluent but Unfeeling: The Emotional Blind Spots of Language Models

Invisible Attributes, Visible Biases: Exploring Demographic Shortcuts in MRI-based Alzheimer's Disease Classification

An improved educational competition optimizer with multi-covariance learning operators for global optimization problems

Improving Video Diffusion Transformer Training by Multi-Feature Fusion and Alignment from Self-Supervised Vision Encoders

A modified RIME algorithm with covariance learning and diversity enhancement for numerical optimization

Towards Explainable Job Title Matching: Leveraging Semantic Textual Relatedness and Knowledge Graphs

Explainable AI for Accelerated Microstructure Imaging: A SHAP-Guided Protocol on the Connectome 2.0 scanner

Incorporating AI Incident Reporting into Telecommunications Law and Policy: Insights from India

OpenFake: An Open Dataset and Platform Toward Large-Scale Deepfake Detection

Prompt Pirates Need a Map: Stealing Seeds helps Stealing Prompts

Resource-Efficient Glioma Segmentation on Sub-Saharan MRI

ENSI: Efficient Non-Interactive Secure Inference for Large Language Models

We're Still Doing It (All) Wrong: Recommender Systems, Fifteen Years Later

LLMs Don't Know Their Own Decision Boundaries: The Unreliability of Self-Generated Counterfactual Explanations

MetaLLMix : An XAI Aided LLM-Meta-learning Based Approach for Hyper-parameters Optimization

Combating Falsification of Speech Videos with Live Optical Signatures (Extended Version)

Created by

Haebom

저자

Hadleigh Schwartz, Xiaofeng Yan, Charles J. Carver, Xia Zhou

개요

VeriLight는 고화질 음성 비디오의 위변조 방지를 위한 저오버헤드, 비침범적 시스템입니다. 기존의 디지털 위변조 탐지 방법과 달리, VeriLight는 행사 현장에서 동적인 물리적 서명을 생성하고 미세하게 조절된 빛을 통해 모든 비디오 녹화에 이를 삽입합니다. 이 물리적 서명은 화자의 신원과 얼굴 움직임을 포함한 음성 이벤트에 고유한 의미론적 특징을 암호화하여 스푸핑을 방지합니다. 서명은 어떤 비디오에서도 추출되어 묘사된 음성 콘텐츠와 검증되어 무결성을 확인할 수 있습니다. VeriLight의 핵심 요소는 (1) locality-sensitive hashing 기반의 매우 작은(150비트) 자세 불변 음성 비디오 특징 생성 프레임워크와 (2) 비디오 및 라이브 모두에서 인지할 수 없으면서 200bps 이상을 비디오에 삽입하는 광 변조 방식입니다. 광범위한 비디오 데이터 세트에 대한 실험 결과, VeriLight는 위변조 비디오 탐지에서 AUC ≥ 0.99 및 100%의 진양성률을 달성합니다. 또한, 녹화 조건, 비디오 후처리 기술 및 특징 추출 방법에 대한 화이트박스 적대적 공격에 대해 매우 강력합니다. VeriLight 데모는 https://mobilex.cs.columbia.edu/verilight 에서 확인할 수 있습니다.

Live optical signature protecting speech video integrity

mobilex.cs.columbia.edu

시사점, 한계점

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시사점:

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저오버헤드, 비침범적인 방식으로 고화질 음성 비디오의 위변조를 효과적으로 방지할 수 있는 새로운 시스템 제시.

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기존 디지털 방식의 한계를 극복하는 물리적 서명 기반의 혁신적인 접근 방식.

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높은 정확도 (AUC ≥ 0.99, 진양성률 100%)와 강력한 안정성을 실험적으로 검증.

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다양한 녹화 조건 및 후처리 과정에 대한 높은 강건성.

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한계점:

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시스템 구현에 필요한 특수 장비 및 기술의 복잡성 및 비용.

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물리적 서명 삽입 과정에서 발생할 수 있는 비디오 품질 저하 가능성 (비록 미세하다고 주장하지만).

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물리적 서명 삽입이 불가능한 상황(예: 기존 비디오에 대한 적용)에 대한 대응 방안 부재.

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광 변조 기술의 실제 환경 적용 가능성에 대한 추가적인 연구 필요.

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