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.

AI/ML in 3GPP 5G Advanced -- Services and Architecture

CookAnything: A Framework for Flexible and Consistent Multi-Step Recipe Image Generation

Dynamic Correction of Erroneous State Estimates via Diffusion Bayesian Exploration

Concept-Guided Backdoor Attack on Vision Language Models

On the Holographic Geometry of Deterministic Computation

Decoding inner speech with an end-to-end brain-to-text neural interface

Physics-informed Neural Operator Learning for Nonlinear Grad-Shafranov Equation

MindEval: Benchmarking Language Models on Multi-turn Mental Health Support

LAET: A Layer-wise Adaptive Ensemble Tuning Framework for Pretrained Language Models

FAST-CAD: A Fairness-Aware Framework for Non-Contact Stroke Diagnosis

Designing LLM-based Multi-Agent Systems for Software Engineering Tasks: Quality Attributes, Design Patterns and Rationale

SONIC: Supersizing Motion Tracking for Natural Humanoid Whole-Body Control

MOSS: Efficient and Accurate FP8 LLM Training with Microscaling and Automatic Scaling

Data-Augmented Deep Learning for Downhole Depth Sensing and Field Validation

Chinese Discharge Drug Recommendation in Metabolic Diseases with Large Language Models

Analysing Moral Bias in Finetuned LLMs through Mechanistic Interpretability

Orders in Chaos: Enhancing Large-Scale MoE LLM Serving with Data Movement Forecasting

Reinforce-Ada: An Adaptive Sampling Framework under Non-linear RL Objectives

TempoControl: Temporal Attention Guidance for Text-to-Video Models

AortaDiff: A Unified Multitask Diffusion Framework For Contrast-Free AAA Imaging

SoREX: Towards Self-Explainable Social Recommendation with Relevant Ego-Path Extraction

The AI Productivity Index (APEX)

Uncovering Grounding IDs: How External Cues Shape Multimodal Binding

Pushing Toward the Simplex Vertices: A Simple Remedy for Code Collapse in Smoothed Vector Quantization

V-CECE: Visual Counterfactual Explanations via Conceptual Edits

Momentum-constrained Hybrid Heuristic Trajectory Optimization Framework with Residual-enhanced DRL for Visually Impaired Scenarios

HARP: Hallucination Detection via Reasoning Subspace Projection

Rethinking Sparse Autoencoders: Select-and-Project for Fairness and Control from Encoder Features Alone

IPA: An Information-Reconstructive Input Projection Framework for Efficient Foundation Model Adaptation

Sparse but Wrong: Incorrect L0 Leads to Incorrect Features in Sparse Autoencoders

ORFuzz: Fuzzing the "Other Side" of LLM Safety -- Testing Over-Refusal

Retro-Expert: Collaborative Reasoning for Interpretable Retrosynthesis

A Survey on Diffusion Language Models

CodeNER: Code Prompting for Named Entity Recognition

ReSem3D: Refinable 3D Spatial Constraints via Fine-Grained Semantic Grounding for Generalizable Robotic Manipulation

VERIRAG: A Post-Retrieval Auditing of Scientific Study Summaries

SustainDiffusion: Optimising the Social and Environmental Sustainability of Stable Diffusion Models

LittleBit: Ultra Low-Bit Quantization via Latent Factorization

Real-Time Execution of Action Chunking Flow Policies

AURA: A Diagnostic Framework for Tracking User Satisfaction of Interactive Planning Agents

Enhancing SPARQL Query Rewriting for Complex Ontology Alignments

Exploring Ordinal Bias in Action Recognition for Instructional Videos

Robust Weight Imprinting: Insights from Neural Collapse and Proxy-Based Aggregation

Experiments with Large Language Models on Retrieval-Augmented Generation for Closed-Source Simulation Software

SAT: Dynamic Spatial Aptitude Training for Multimodal Language Models

Large Language Models: An Applied Econometric Framework

Edge-Only Universal Adversarial Attacks in Distributed Learning

Image-Guided Semantic Pseudo-LiDAR Point Generation for 3D Object Detection

Detecting the Future: All-at-Once Event Sequence Forecasting with Horizon Matching

Variational Learning of Gaussian Process Latent Variable Models through Stochastic Gradient Annealed Importance Sampling

SOAP: Enhancing Spatio-Temporal Relation and Motion Information Capturing for Few-Shot Action Recognition

A Scene-aware Models Adaptation Scheme for Cross-scene Online Inference on Mobile Devices

Towards Data-efficient Customer Intent Recognition with Prompt-based Learning Paradigm

GTM: Simulating the World of Tools for AI Agents

Self-Transparency Failures in Expert-Persona LLMs: How Instruction-Following Overrides Honesty

Learning the Value of Value Learning

OpenMMReasoner: Pushing the Frontiers for Multimodal Reasoning with an Open and General Recipe

ToolMind Technical Report: A Large-Scale, Reasoning-Enhanced Tool-Use Dataset

Debate over Mixed-knowledge: A Robust Multi-Agent Reasoning Framework for Incomplete Knowledge Graph Question Answering

Spilling the Beans: Teaching LLMs to Self-Report Their Hidden Objectives

Counterfactual Reasoning for Steerable Pluralistic Value Alignment of Large Language Models

Generalized Parallel Scaling with Interdependent Generations

SOCK: A Benchmark for Measuring Self-Replication in Large Language Models

KNARsack: Teaching Neural Algorithmic Reasoners to Solve Pseudo-Polynomial Problems

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

FedIFL: A federated cross-domain diagnostic framework for motor-driven systems with inconsistent fault modes

Enhancing Large Language Models through Neuro-Symbolic Integration and Ontological Reasoning

Rolling in the deep of cognitive and AI biases

Enhancing Retrieval-Augmented Generation with Entity Linking for Educational Platforms

Training-Time Action Conditioning for Efficient Real-Time Chunking

Whatever Remains Must Be True: Filtering Drives Reasoning in LLMs, Shaping Diversity

AQUA-Net: Adaptive Frequency Fusion and Illumination Aware Network for Underwater Image Enhancement

M4-RAG: A Massive-Scale Multilingual Multi-Cultural Multimodal RAG

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution

Trusted AI Agents in the Cloud

Impugan: Learning Conditional Generative Models for Robust Data Imputation

Zoom in, Click out: Unlocking and Evaluating the Potential of Zooming for GUI Grounding

Measuring the Effect of Background on Classification and Feature Importance in Deep Learning for AV Perception

World Models That Know When They Don't Know: Controllable Video Generation with Calibrated Uncertainty

Natural Language Summarization Enables Multi-Repository Bug Localization by LLMs in Microservice Architectures

Neural Coherence : Find higher performance to out-of-distribution tasks from few samples

Sparse Attention Post-Training for Mechanistic Interpretability

Optimizing Medical Question-Answering Systems: A Comparative Study of Fine-Tuned and Zero-Shot Large Language Models with RAG Framework

NEAT: Neighborhood-Guided, Efficient, Autoregressive Set Transformer for 3D Molecular Generation

Phase-OTDR Event Detection Using Image-Based Data Transformation and Deep Learning

Approximation of Box Decomposition Algorithm for Fast Hypervolume-Based Multi-Objective Optimization

Probing the effectiveness of World Models for Spatial Reasoning through Test-time Scaling

3D Path Planning for Robot-assisted Vertebroplasty from Arbitrary Bi-plane X-ray via Differentiable Rendering

Mechanistic Interpretability of Antibody Language Models Using SAEs

Active Video Perception: Iterative Evidence Seeking for Agentic Long Video Understanding

Efficient Text Classification with Conformal In-Context Learning

Big Tech-Funded AI Papers Have Higher Citation Impact, Greater Insularity, and Larger Recency Bias

Bayesian Active Inference for Intelligent UAV Anti-Jamming and Adaptive Trajectory Planning

Faithfulness metric fusion: Improving the evaluation of LLM trustworthiness across domains

Retrieving Semantically Similar Decisions under Noisy Institutional Labels: Robust Comparison of Embedding Methods

InverseCrafter: Efficient Video ReCapture as a Latent Domain Inverse Problem

Feasibility of AI-Assisted Programming for End-User Development

Grounded Multilingual Medical Reasoning for Question Answering with Large Language Models

Modular Jets for Supervised Pipelines: Diagnosing Mirage vs Identifiability

A Comprehensive Framework for Automated Quality Control in the Automotive Industry

Autonomous AI imitators increase diversity in homogeneous information ecosystems

Created by

Haebom

Category

Empty

저자

Emil Bakkensen Johansen, Oliver Baumann

개요

본 논문은 대규모 언어 모델(LLM) 기반 자율 AI 에이전트가 인간이 생성한 콘텐츠를 모방하는 능력이 정보 생태계의 다양성과 민주적 가치에 미치는 영향을 조사합니다. 뉴스라는 맥락에서 두 가지 AI 모방 전략을 다양한 정보 환경(초기 다양성 수준이 다름)에 적용하는 대규모 시뮬레이션 프레임워크를 사용하여 AI 생성 기사가 콘텐츠를 균일하게 동질화하지 않는다는 것을 보여줍니다. AI의 영향은 상황에 따라 크게 달라지며, 초기 정보 환경이 동질적인 경우 다양성을 증가시키는 반면, 이질적인 경우 다양성을 감소시킬 수 있습니다. 따라서 AI 기반 모방이 정보 다양성을 위협한다는 가정에 의문을 제기하며, 초기 정보 환경의 다양성이 AI의 영향에 중요한 역할을 한다는 것을 강조합니다. 특히 초기 정보 환경이 동질적인 뉴스 환경에서는 AI 모방이 관점, 스타일, 주제의 다양성을 확장하여 더 풍부한 공론을 가능하게 하고 민주주의를 강화할 수 있습니다.

시사점, 한계점

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시사점:

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AI 기반 콘텐츠 모방의 영향은 정보 환경의 초기 다양성에 크게 의존한다는 것을 밝힘.

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초기 정보 환경이 동질적인 경우, AI는 다양성을 증가시키는 역할을 할 수 있다는 것을 제시.

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AI가 정보 다양성에 미치는 영향에 대한 기존의 단순한 가정에 도전하고, 보다 뉘앙스 있는 이해를 제공.

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뉴스 환경에서 정보 다양성의 중요성과 AI의 잠재적 역할을 강조하며, 민주주의 강화에 대한 시사점 제시.

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한계점:

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시뮬레이션 기반 연구이므로 실제 세계의 복잡성을 완전히 반영하지 못할 수 있음.

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특정 AI 모델과 모방 전략에 국한된 결과일 수 있으며, 일반화에 제한이 있을 수 있음.

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뉴스 이외의 다른 정보 환경에서는 결과가 다를 수 있음.

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AI 모방의 장기적인 영향과 부정적 측면에 대한 심층적인 분석이 부족할 수 있음.

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