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.

Sekai: A Video Dataset towards World Exploration

Federated Learning for MRI-based BrainAGE: a multicenter study on post-stroke functional outcome prediction

One-Step Diffusion for Detail-Rich and Temporally Consistent Video Super-Resolution

Capturing Polysemanticity with PRISM: A Multi-Concept Feature Description Framework

Efficient Retail Video Annotation: A Robust Key Frame Generation Approach for Product and Customer Interaction Analysis

WebXAII: an open-source web framework to study human-XAI interaction

Refining music sample identification with a self-supervised graph neural network

Decoupled Classifier-Free Guidance for Counterfactual Diffusion Models

Essential-Web v1.0: 24T tokens of organized web data

Adaptive Guidance Accelerates Reinforcement Learning of Reasoning Models

LeVERB: Humanoid Whole-Body Control with Latent Vision-Language Instruction

Screen Hijack: Visual Poisoning of VLM Agents in Mobile Environments

SP-VLA: A Joint Model Scheduling and Token Pruning Approach for VLA Model Acceleration

Serving Large Language Models on Huawei CloudMatrix384

Machine Learning Methods for Small Data and Upstream Bioprocessing Applications: A Comprehensive Review

Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning

Two Heads Are Better than One: Simulating Large Transformers with Small Ones

BreastDCEDL: Curating a Comprehensive DCE-MRI Dataset and developing a Transformer Implementation for Breast Cancer Treatment Response Prediction

Semantic Preprocessing for LLM-based Malware Analysis

A Minimalist Method for Fine-tuning Text-to-Image Diffusion Models

Human-like Forgetting Curves in Deep Neural Networks

Convergent Linear Representations of Emergent Misalignment

LearnAlign: Reasoning Data Selection for Reinforcement Learning in Large Language Models Based on Improved Gradient Alignment

TARDIS STRIDE: A Spatio-Temporal Road Image Dataset and World Model for Autonomy

Autonomous Computer Vision Development with Agentic AI

The Memory Paradox: Why Our Brains Need Knowledge in an Age of AI

SWE-Factory: Your Automated Factory for Issue Resolution Training Data and Evaluation Benchmarks

Using Language and Road Manuals to Inform Map Reconstruction for Autonomous Driving

Reinforcing Spatial Reasoning in Vision-Language Models with Interwoven Thinking and Visual Drawing

PlantBert: An Open Source Language Model for Plant Science

Info-Coevolution: An Efficient Framework for Data Model Coevolution

SDE-SQL: Enhancing Text-to-SQL Generation in Large Language Models via Self-Driven Exploration with SQL Probes

SafeGenBench: A Benchmark Framework for Security Vulnerability Detection in LLM-Generated Code

UniWorld-V1: High-Resolution Semantic Encoders for Unified Visual Understanding and Generation

KVCache Cache in the Wild: Characterizing and Optimizing KVCache Cache at a Large Cloud Provider

GraphRAG-Bench: Challenging Domain-Specific Reasoning for Evaluating Graph Retrieval-Augmented Generation

Towards Efficient Few-shot Graph Neural Architecture Search via Partitioning Gradient Contribution

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

CryoCCD: Conditional Cycle-consistent Diffusion with Biophysical Modeling for Cryo-EM Synthesis

More Thinking, Less Seeing? Assessing Amplified Hallucination in Multimodal Reasoning Models

Calibrating Pre-trained Language Classifiers on LLM-generated Noisy Labels via Iterative Refinement

Dynamic Risk Assessments for Offensive Cybersecurity Agents

SSR-Zero: Simple Self-Rewarding Reinforcement Learning for Machine Translation

Robo2VLM: Visual Question Answering from Large-Scale In-the-Wild Robot Manipulation Datasets

Breaking the Compression Ceiling: Data-Free Pipeline for Ultra-Efficient Delta Compression

Representation Learning with Mutual Influence of Modalities for Node Classification in Multi-Modal Heterogeneous Networks

Learning Dynamics in Continual Pre-Training for Large Language Models

Mask-PINNs: Regulating Feature Distributions in Physics-Informed Neural Networks

Assessing Tenstorrent's RISC-V MatMul Acceleration Capabilities

SPIN-ODE: Stiff Physics-Informed Neural ODE for Chemical Reaction Rate Estimation

PerceptionLM: Open-Access Data and Models for Detailed Visual Understanding

DeepSelective: Interpretable Prognosis Prediction via Feature Selection and Compression in EHR Data

Boosting multi-demographic federated learning for chest radiograph analysis using general-purpose self-supervised representations

AerialVG: A Challenging Benchmark for Aerial Visual Grounding by Exploring Positional Relations

PR-Attack: Coordinated Prompt-RAG Attacks on Retrieval-Augmented Generation in Large Language Models via Bilevel Optimization

TALE: A Tool-Augmented Framework for Reference-Free Evaluation of Large Language Models

Decentralized Collective World Model for Emergent Communication and Coordination

RL2Grid: Benchmarking Reinforcement Learning in Power Grid Operations

Efficient but Vulnerable: Benchmarking and Defending LLM Batch Prompting Attack

LLMs in Disease Diagnosis: A Comparative Study of DeepSeek-R1 and O3 Mini Across Chronic Health Conditions

Can We Detect Failures Without Failure Data? Uncertainty-Aware Runtime Failure Detection for Imitation Learning Policies

QG-SMS: Enhancing Test Item Analysis via Student Modeling and Simulation

Dynamic Knowledge Integration for Evidence-Driven Counter-Argument Generation with Large Language Models

AlignDistil: Token-Level Language Model Alignment as Adaptive Policy Distillation

Eau De $Q$-Network: Adaptive Distillation of Neural Networks in Deep Reinforcement Learning

Hierarchical and Modular Network on Non-prehensile Manipulation in General Environments

Selective Use of Yannakakis' Algorithm to Improve Query Performance: Machine Learning to the Rescue

FRIDA to the Rescue! Analyzing Synthetic Data Effectiveness in Object-Based Common Sense Reasoning for Disaster Response

Batayan: A Filipino NLP benchmark for evaluating Large Language Models

From RAG to Memory: Non-Parametric Continual Learning for Large Language Models

Conformal Inference under High-Dimensional Covariate Shifts via Likelihood-Ratio Regularization

ShapeLib: Designing a library of programmatic 3D shape abstractions with Large Language Models

Rethinking Fine-Tuning when Scaling Test-Time Compute: Limiting Confidence Improves Mathematical Reasoning

Guaranteed prediction sets for functional surrogate models

FDLLM: A Dedicated Detector for Black-Box LLMs Fingerprinting

Each Rank Could be an Expert: Single-Ranked Mixture of Experts LoRA for Multi-Task Learning

MonoSOWA: Scalable monocular 3D Object detector Without human Annotations

Representation Learning of Point Cloud Upsampling in Global and Local Inputs

Multi-Preference Optimization: Generalizing DPO via Set-Level Contrasts

Incivility and Rigidity: The Risks of Fine-Tuning LLMs for Political Argumentation

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

Learning Multi-Branch Cooperation for Enhanced Click-Through Rate Prediction at Taobao

On the Limits of Language Generation: Trade-Offs Between Hallucination and Mode Collapse

Web Archives Metadata Generation with GPT-4o: Challenges and Insights

Cyclic Vision-Language Manipulator: Towards Reliable and Fine-Grained Image Interpretation for Automated Report Generation

A Implies B: Circuit Analysis in LLMs for Propositional Logical Reasoning

Adapting While Learning: Grounding LLMs for Scientific Problems with Intelligent Tool Usage Adaptation

FALCON: Feedback-driven Adaptive Long/short-term memory reinforced Coding Optimization system

Efficient Mixture-of-Expert for Video-based Driver State and Physiological Multi-task Estimation in Conditional Autonomous Driving

ALTA: Compiler-Based Analysis of Transformers

Learning to Route LLMs with Confidence Tokens

Core Knowledge Deficits in Multi-Modal Language Models

AssistantX: An LLM-Powered Proactive Assistant in Collaborative Human-Populated Environment

MaPPER: Multimodal Prior-guided Parameter Efficient Tuning for Referring Expression Comprehension

Can Large Language Models Replace Human Subjects? A Large-Scale Replication of Scenario-Based Experiments in Psychology and Management

LogProber: Disentangling confidence from contamination in LLM responses

V2X-VLM: End-to-End V2X Cooperative Autonomous Driving Through Large Vision-Language Models

xGen-MM (BLIP-3): A Family of Open Large Multimodal Models

Understanding and Reducing the Class-Dependent Effects of Data Augmentation with A Two-Player Game Approach

PromptDSI: Prompt-based Rehearsal-free Instance-wise Incremental Learning for Document Retrieval

Autonomous Computer Vision Development with Agentic AI

Created by

Haebom

저자

Jin Kim, Muhammad Wahi-Anwa, Sangyun Park, Shawn Shin, John M. Hoffman, Matthew S. Brown

개요

본 논문은 대규모 언어 모델(LLM)을 활용한 에이전트 인공지능(AI) 시스템이 복잡한 추론, 계획 및 도구 활용에 상당한 잠재력을 가지고 있음을 보여줍니다. 특히, 자연어 프롬프트만으로 의료 영상 분석을 위한 오픈소스 인지 AI 환경인 SimpleMind(SM)에 LLM 기반 에이전트(OpenManus 사용)를 확장하여 컴퓨터 비전 시스템을 자동으로 구축하는 것을 시연합니다. 에이전트는 사용자의 자연어 프롬프트("provide sm (SimpleMind) config for lungs, heart, and ribs segmentation for cxr (chest x-ray)")를 해석하여, 작업을 분해하고 적절한 도구를 구성하여 SimpleMind 워크플로우 계획(YAML 형식의 도구 구성 파일)을 생성하고, SM-Learn(학습) 및 SM-Think(추론) 스크립트를 자동으로 실행합니다. 50장의 흉부 X선 사진을 사용하여 자동으로 구성, 학습 및 테스트한 결과, 폐, 심장, 갈비뼈에 대한 평균 Dice 점수가 각각 0.96, 0.82, 0.83을 달성했습니다. 이는 데이터 과학자가 수행해왔던 컴퓨터 비전 애플리케이션 개발 과정에서의 자율적 계획 및 도구 구성의 가능성을 보여줍니다.

시사점, 한계점

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시사점:

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자연어 프롬프트를 통해 컴퓨터 비전 시스템을 자동으로 구축하는 새로운 방법을 제시합니다.

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LLM 기반 에이전트 AI를 활용하여 데이터 과학자의 작업을 자동화할 수 있는 가능성을 보여줍니다.

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의료 영상 분석 분야에서의 효율성 향상 및 전문가의 부담 경감에 기여할 수 있습니다.

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LLM의 계획 및 도구 구성 능력을 실제 응용 분야에 적용한 성공적인 사례를 제시합니다.

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한계점:

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현재는 특정 의료 영상 분석(흉부 X선) 및 SimpleMind 환경에 국한된 데모입니다. 다른 영상 유형이나 다른 플랫폼으로의 일반화 가능성은 추가 연구가 필요합니다.

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LLM의 출력 결과에 대한 신뢰성 및 오류 처리 메커니즘에 대한 추가 연구가 필요합니다.

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복잡하고 다양한 작업에 대한 에이전트의 적응력 및 일반화 성능에 대한 추가적인 평가가 필요합니다.

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사용된 데이터셋의 크기와 다양성이 제한적일 수 있으며, 이로 인한 성능의 일반화 한계가 존재할 수 있습니다.

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