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.

Segment Anything for Satellite Imagery: A Strong Baseline and a Regional Dataset for Automatic Field Delineation

VRAIL: Vectorized Reward-based Attribution for Interpretable Learning

LLM Web Dynamics: Tracing Model Collapse in a Network of LLMs

Accurate and scalable exchange-correlation with deep learning

Working Document -- Formalising Software Requirements with Large Language Models

AlphaDecay: Module-wise Weight Decay for Heavy-Tailed Balancing in LLMs

Bures-Wasserstein Flow Matching for Graph Generation

Distributional Training Data Attribution

AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning

Robust LLM Unlearning with MUDMAN: Meta-Unlearning with Disruption Masking And Normalization

Exploring the Secondary Risks of Large Language Models

Supernova Event Dataset: Interpreting Large Language Models' Personality through Critical Event Analysis

Agent-RLVR: Training Software Engineering Agents via Guidance and Environment Rewards

C-SEO Bench: Does Conversational SEO Work?

xInv: Explainable Optimization of Inverse Problems

Effective Red-Teaming of Policy-Adherent Agents

SLED: A Speculative LLM Decoding Framework for Efficient Edge Serving

PlantDeBERTa: An Open Source Language Model for Plant Science

Reinforcement Learning Teachers of Test Time Scaling

DriveSuprim: Towards Precise Trajectory Selection for End-to-End Planning

OWMM-Agent: Open World Mobile Manipulation With Multi-modal Agentic Data Synthesis

Optimizing Sensory Neurons: Nonlinear Attention Mechanisms for Accelerated Convergence in Permutation-Invariant Neural Networks for Reinforcement Learning

Dual Debiasing for Noisy In-Context Learning for Text Generation

Eye of Judgement: Dissecting the Evaluation of Russian-speaking LLMs with POLLUX

Maximizing Confidence Alone Improves Reasoning

Analysis and Evaluation of Synthetic Data Generation in Speech Dysfluency Detection

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering

Position is Power: System Prompts as a Mechanism of Bias in Large Language Models (LLMs)

Cross from Left to Right Brain: Adaptive Text Dreamer for Vision-and-Language Navigation

Pretraining Language Models to Ponder in Continuous Space

SIPDO: Closed-Loop Prompt Optimization via Synthetic Data Feedback

Learning to Insert for Constructive Neural Vehicle Routing Solver

LightRetriever: A LLM-based Hybrid Retrieval Architecture with 1000x Faster Query Inference

MIRAGE: A Multi-modal Benchmark for Spatial Perception, Reasoning, and Intelligence

Introducing voice timbre attribute detection

The Voice Timbre Attribute Detection 2025 Challenge Evaluation Plan

TrumorGPT: Graph-Based Retrieval-Augmented Large Language Model for Fact-Checking

Democracy of AI Numerical Weather Models: An Example of Global Forecasting with FourCastNetv2 Made by a University Research Lab Using GPU

Learning to Reason under Off-Policy Guidance

Protecting Your Voice: Temporal-aware Robust Watermarking

Personalized News Recommendation with Multi-granularity Candidate-aware User Modeling

Learning from Reference Answers: Versatile Language Model Alignment without Binary Human Preference Data

AutoPDL: Automatic Prompt Optimization for LLM Agents

PiCo: Jailbreaking Multimodal Large Language Models via $\textbf{Pi}$ctorial $\textbf{Co}$de Contextualization

From Easy to Hard: Building a Shortcut for Differentially Private Image Synthesis

Context-Aware Human Behavior Prediction Using Multimodal Large Language Models: Challenges and Insights

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

Shapley Revisited: Tractable Responsibility Measures for Query Answers

Simple and Critical Iterative Denoising: A Recasting of Discrete Diffusion in Graph Generation

Large Language Models powered Malicious Traffic Detection: Architecture, Opportunities and Case Study

TreeSynth: Synthesizing Diverse Data from Scratch via Tree-Guided Subspace Partitioning

A Dual-Directional Context-Aware Test-Time Learning for Text Classification

LED: LLM Enhanced Open-Vocabulary Object Detection without Human Curated Data Generation

HiRAG: Retrieval-Augmented Generation with Hierarchical Knowledge

Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions

POPGym Arcade: Parallel Pixelated POMDPs

BAnG: Bidirectional Anchored Generation for Conditional RNA Design

Directional Gradient Projection for Robust Fine-Tuning of Foundation Models

ParamMute: Suppressing Knowledge-Critical FFNs for Faithful Retrieval-Augmented Generation

Steering LLMs for Formal Theorem Proving

Exploring the Potential of Encoder-free Architectures in 3D LMMs

Compromising Honesty and Harmlessness in Language Models via Deception Attacks

Large Language Model Guided Self-Debugging Code Generation

ASCenD-BDS: Adaptable, Stochastic and Context-aware framework for Detection of Bias, Discrimination and Stereotyping

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

Segmentation-Aware Generative Reinforcement Network (GRN) for Tissue Layer Segmentation in 3-D Ultrasound Images for Chronic Low-back Pain (cLBP) Assessment

AnyEnhance: A Unified Generative Model with Prompt-Guidance and Self-Critic for Voice Enhancement

SEAL: Scaling to Emphasize Attention for Long-Context Retrieval

CAD-GPT: Synthesising CAD Construction Sequence with Spatial Reasoning-Enhanced Multimodal LLMs

The Impact of Input Order Bias on Large Language Models for Software Fault Localization

GeAR: Graph-enhanced Agent for Retrieval-augmented Generation

Rethinking Cancer Gene Identification through Graph Anomaly Analysis

SurgSora: Object-Aware Diffusion Model for Controllable Surgical Video Generation

DSGram: Dynamic Weighting Sub-Metrics for Grammatical Error Correction in the Era of Large Language Models

Human Action CLIPs: Detecting AI-generated Human Motion

FLARE: Toward Universal Dataset Purification against Backdoor Attacks

G3Flow: Generative 3D Semantic Flow for Pose-aware and Generalizable Object Manipulation

Cross-Camera Distracted Driver Classification through Feature Disentanglement and Contrastive Learning

Song Form-aware Full-Song Text-to-Lyrics Generation with Multi-Level Granularity Syllable Count Control

Generating Energy-efficient code with LLMs

DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models

How Far is Video Generation from World Model: A Physical Law Perspective

One-Step is Enough: Sparse Autoencoders for Text-to-Image Diffusion Models

The Hive Mind is a Single Reinforcement Learning Agent

How Numerical Precision Affects Arithmetical Reasoning Capabilities of LLMs

FutureFill: Fast Generation from Convolutional Sequence Models

Leveraging Model Guidance to Extract Training Data from Personalized Diffusion Models

Machine-learning based high-bandwidth magnetic sensing

MOST: MR reconstruction Optimization for multiple downStream Tasks via continual learning

Bridging Geometric Diffusion and Energy Minimization: A Unified Framework for Neural Message Passing

Large Language Models for Disease Diagnosis: A Scoping Review

RePST: Language Model Empowered Spatio-Temporal Forecasting via Semantic-Oriented Reprogramming

Smooth InfoMax -- Towards Easier Post-Hoc Interpretability

PREMAP: A Unifying PREiMage APproximation Framework for Neural Networks

Reasoning Circuits in Language Models: A Mechanistic Interpretation of Syllogistic Inference

UniMoT: Unified Molecule-Text Language Model with Discrete Token Representation

Handling Numeric Expressions in Automatic Speech Recognition

"I understand why I got this grade": Automatic Short Answer Grading with Feedback

DART: An Automated End-to-End Object Detection Pipeline with Data Diversification, Open-Vocabulary Bounding Box Annotation, Pseudo-Label Review, and Model Training

Rich Interoperable Metadata for Cultural Heritage Projects at Jagiellonian University

Systematic Hazard Analysis for Frontier AI using STPA

Created by

Haebom

저자

Simon Mylius

개요

본 논문은 최첨단 AI 기업들이 안전한 모델 개발 및 배포를 위한 역량 한계와 위험 완화를 정의하는 안전 프레임워크를 발표했지만, 체계적인 위험 모델링 접근 방식에 대한 자세한 설명은 부족하다는 점을 지적합니다. 따라서, 시스템 이론적 프로세스 분석(STPA)을 활용하여 복잡한 시스템의 안전하지 않은 상태를 식별하고, 'AI 제어 안전 사례 스케치'의 위협 모델 및 시나리오에 STPA를 적용하여 안전하지 않은 제어 행위 목록을 도출합니다. 이를 통해 기존의 비체계적인 위험 분석 방법론으로는 놓칠 수 있는 인과 요인을 식별하여 안전성을 강화하고, STPA가 역량 한계, 모델 평가 및 비상 절차와 같은 기존 AI 거버넌스 기법을 보완하여 최첨단 AI의 안전성을 높일 수 있음을 보여줍니다. 또한, STPA의 체계적인 방법론은 LLM을 활용하여 분석의 상당 부분을 자동화하여 전문가의 부담을 줄이고 확장성을 높일 수 있다는 점을 제시합니다.

시사점, 한계점

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시사점:

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STPA를 활용하여 최첨단 AI 시스템의 안전성 확보를 위한 체계적인 접근 방식을 제시합니다.

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기존의 비체계적인 위험 분석 방법론이 놓칠 수 있는 인과 요인을 식별하여 안전성을 강화합니다.

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LLM을 활용하여 분석의 자동화 및 확장성을 높일 수 있는 가능성을 제시합니다.

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기존 AI 거버넌스 기법을 보완하여 더욱 강력한 안전성을 확보할 수 있습니다.

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한계점:

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STPA의 적용 결과가 특정 사례 연구에 국한되어 일반화 가능성에 대한 추가 연구가 필요합니다.

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LLM을 활용한 자동화의 실제 효율성 및 신뢰성에 대한 추가적인 검증이 필요합니다.

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STPA를 실제 최첨단 AI 시스템에 적용하는 데 있어 발생할 수 있는 어려움이나 제약에 대한 논의가 부족합니다.

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