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.

Something's Fishy In The Data Lake: A Critical Re-evaluation of Table Union Search Benchmarks

Breaking the Ceiling: Exploring the Potential of Jailbreak Attacks through Expanding Strategy Space

SageAttention2++: A More Efficient Implementation of SageAttention2

FCKT: Fine-Grained Cross-Task Knowledge Transfer with Semantic Contrastive Learning for Targeted Sentiment Analysis

Towards Conversational Development Environments: Using Theory-of-Mind and Multi-Agent Architectures for Requirements Refinement

Cooperation of Experts: Fusing Heterogeneous Information with Large Margin

RSCF: Relation-Semantics Consistent Filter for Entity Embedding of Knowledge Graph

CogniBench: A Legal-inspired Framework and Dataset for Assessing Cognitive Faithfulness of Large Language Models

VLM Can Be a Good Assistant: Enhancing Embodied Visual Tracking with Self-Improving Vision-Language Models

In-context Language Learning for Endangered Languages in Speech Recognition

OpenS2V-Nexus: A Detailed Benchmark and Million-Scale Dataset for Subject-to-Video Generation

Inference-time Alignment in Continuous Space

Incentivizing Strong Reasoning from Weak Supervision

JEDI: Latent End-to-end Diffusion Mitigates Agent-Human Performance Asymmetry in Model-Based Reinforcement Learning

A Comprehensive Real-World Assessment of Audio Watermarking Algorithms: Will They Survive Neural Codecs?

AgentRecBench: Benchmarking LLM Agent-based Personalized Recommender Systems

Towards Large Reasoning Models for Agriculture

VTool-R1: VLMs Learn to Think with Images via Reinforcement Learning on Multimodal Tool Use

OptiMindTune: A Multi-Agent Framework for Intelligent Hyperparameter Optimization

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

Moderating Harm: Benchmarking Large Language Models for Cyberbullying Detection in YouTube Comments

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models

Deep Video Discovery: Agentic Search with Tool Use for Long-form Video Understanding

Towards Practical Defect-Focused Automated Code Review

EVM-Fusion: An Explainable Vision Mamba Architecture with Neural Algorithmic Fusion

PAEFF: Precise Alignment and Enhanced Gated Feature Fusion for Face-Voice Association

Text Generation Beyond Discrete Token Sampling

AKRMap: Adaptive Kernel Regression for Trustworthy Visualization of Cross-Modal Embeddings

Information Science Principles of Machine Learning: A Causal Chain Meta-Framework Based on Formalized Information Mapping

Advancing Sequential Numerical Prediction in Autoregressive Models

Towards Visuospatial Cognition via Hierarchical Fusion of Visual Experts

Visuospatial Cognitive Assistant

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning

Search and Refine During Think: Autonomous Retrieval-Augmented Reasoning of LLMs

Learning When to Think: Shaping Adaptive Reasoning in R1-Style Models via Multi-Stage RL

LibIQ: Toward Real-Time Spectrum Classification in O-RAN dApps

Continuous Thought Machines

Robust Localization, Mapping, and Navigation for Quadruped Robots

Addressing Concept Mislabeling in Concept Bottleneck Models Through Preference Optimization

Automating tumor-infiltrating lymphocyte assessment in breast cancer histopathology images using QuPath: a transparent and accessible machine learning pipeline

Progressive Language-guided Visual Learning for Multi-Task Visual Grounding

A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment

Generative Framework for Personalized Persuasion: Inferring Causal, Counterfactual, and Latent Knowledge

Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching

Layers at Similar Depths Generate Similar Activations Across LLM Architectures

A Knowledge-guided Adversarial Defense for Resisting Malicious Visual Manipulation

SynWorld: Virtual Scenario Synthesis for Agentic Action Knowledge Refinement

Token embeddings violate the manifold hypothesis

HumanAesExpert: Advancing a Multi-Modality Foundation Model for Human Image Aesthetic Assessment

Improving User Behavior Prediction: Leveraging Annotator Metadata in Supervised Machine Learning Models

Experience Retrieval-Augmentation with Electronic Health Records Enables Accurate Discharge QA

From Head to Tail: Towards Balanced Representation in Large Vision-Language Models through Adaptive Data Calibration

Language-Enhanced Representation Learning for Single-Cell Transcriptomics

TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecaster

When Trust Collides: Decoding Human-LLM Cooperation Dynamics through the Prisoner's Dilemma

WISE: A World Knowledge-Informed Semantic Evaluation for Text-to-Image Generation

Optimal Output Feedback Learning Control for Discrete-Time Linear Quadratic Regulation

Wanda++: Pruning Large Language Models via Regional Gradients

LINGOLY-TOO: Disentangling Reasoning from Knowledge with Templatised Orthographic Obfuscation

Deficient Excitation in Parameter Learning

Interpreting CLIP with Hierarchical Sparse Autoencoders

BatteryLife: A Comprehensive Dataset and Benchmark for Battery Life Prediction

Self-Taught Agentic Long Context Understanding

ReQFlow: Rectified Quaternion Flow for Efficient and High-Quality Protein Backbone Generation

How Do LLMs Perform Two-Hop Reasoning in Context?

ThinkGuard: Deliberative Slow Thinking Leads to Cautious Guardrails

MUDDFormer: Breaking Residual Bottlenecks in Transformers via Multiway Dynamic Dense Connections

ReLearn: Unlearning via Learning for Large Language Models

Revisiting Weak-to-Strong Generalization in Theory and Practice: Reverse KL vs. Forward KL

A Physics-Informed Machine Learning Framework for Safe and Optimal Control of Autonomous Systems

Non-Markovian Discrete Diffusion with Causal Language Models

CoSER: Coordinating LLM-Based Persona Simulation of Established Roles

TransMLA: Migrating GQA Models to MLA with Full DeepSeek Compatibility and Speedup

UniDB: A Unified Diffusion Bridge Framework via Stochastic Optimal Control

Beyond External Monitors: Enhancing Transparency of Large Language Models for Easier Monitoring

ExpProof : Operationalizing Explanations for Confidential Models with ZKPs

Advancing Reasoning in Large Language Models: Promising Methods and Approaches

Path Planning for Masked Diffusion Model Sampling

LV-XAttn: Distributed Cross-Attention for Long Visual Inputs in Multimodal Large Language Models

Robust LLM Alignment via Distributionally Robust Direct Preference Optimization

Improving Rule-based Reasoning in LLMs using Neurosymbolic Representations

Message-Passing GNNs Fail to Approximate Sparse Triangular Factorizations

LoRA-One: One-Step Full Gradient Could Suffice for Fine-Tuning Large Language Models, Provably and Efficiently

Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective

A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment

Risk-Informed Diffusion Transformer for Long-Tail Trajectory Prediction in the Crash Scenario

Can Large Language Models Be Trusted as Evolutionary Optimizers for Network-Structured Combinatorial Problems?

Redundancy Principles for MLLMs Benchmarks

K-COMP: Retrieval-Augmented Medical Domain Question Answering With Knowledge-Injected Compressor

An Innovative Data-Driven and Adaptive Reinforcement Learning Approach for Context-Aware Prescriptive Process Monitoring

Diffusion Adversarial Post-Training for One-Step Video Generation

Gender-Neutral Large Language Models for Medical Applications: Reducing Bias in PubMed Abstracts

PRMBench: A Fine-grained and Challenging Benchmark for Process-Level Reward Models

Constraint-Adaptive Policy Switching for Offline Safe Reinforcement Learning

Revisiting In-Context Learning with Long Context Language Models

Energy and polarization based on-line interference mitigation in radio interferometry

How to Synthesize Text Data without Model Collapse?

Preference Adaptive and Sequential Text-to-Image Generation

Sample Efficient Robot Learning in Supervised Effect Prediction Tasks

Federated Continual Graph Learning

Debate-to-Detect: Reformulating Misinformation Detection as a Real-World Debate with Large Language Models

Created by

Haebom

저자

Chen Han, Wenzhen Zheng, Xijin Tang

개요

본 논문은 기존의 정적 분류 방식에 한계가 있는 디지털 플랫폼의 가짜 뉴스 탐지 문제를 해결하기 위해, 다중 에이전트 논쟁(MAD) 프레임워크인 Debate-to-Detect (D2D)를 제안합니다. D2D는 가짜 뉴스 탐지를 구조화된 적대적 논쟁으로 재구성하여, 도메인 특정 프로필을 가진 에이전트들이 개방 발언, 반박, 자유 논쟁, 폐쇄 발언, 판단의 다섯 단계를 거치는 논쟁을 진행합니다. 기존의 이진 분류를 넘어, 사실성, 출처 신뢰도, 추론 품질, 명확성, 윤리의 다섯 가지 차원으로 주장을 평가하는 다차원 평가 메커니즘을 도입했습니다. GPT-4o를 이용한 실험 결과, 기존 방식보다 성능이 향상되었으며, 증거를 반복적으로 개선하고 의사 결정 투명성을 높이는 D2D의 능력을 보여주는 사례 연구를 제시합니다.

시사점, 한계점

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시사점:

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다중 에이전트 논쟁(MAD) 프레임워크를 활용하여 가짜 뉴스 탐지의 정확도와 해석력을 향상시켰습니다.

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다차원 평가 메커니즘을 통해 기존의 이진 분류 방식의 한계를 극복했습니다.

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증거 기반의 반복적인 논쟁 과정을 통해 의사결정의 투명성을 높였습니다.

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GPT-4o와 같은 LLM의 추론 능력을 가짜 뉴스 탐지에 효과적으로 활용했습니다.

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한계점:

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현재는 GPT-4o를 사용한 실험 결과만 제시되어 있으며, 다른 LLM이나 데이터셋에 대한 일반화 가능성은 추가 연구가 필요합니다.

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코드 공개가 미래에 예정되어 있어, 접근성과 재현성 검증에 시간이 소요될 수 있습니다.

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실제 현실 세계의 복잡한 가짜 뉴스 유포 양상을 완벽하게 반영하지 못할 수 있습니다.

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