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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.

SystolicAttention: Fusing FlashAttention within a Single Systolic Array

Automated Novelty Evaluation of Academic Paper: A Collaborative Approach Integrating Human and Large Language Model Knowledge

"Is it always watching? Is it always listening?" Exploring Contextual Privacy and Security Concerns Toward Domestic Social Robots

A Group Theoretic Analysis of the Symmetries Underlying Base Addition and Their Learnability by Neural Networks

GHPO: Adaptive Guidance for Stable and Efficient LLM Reinforcement Learning

Extension OL-MDISF: Online Learning from Mix-Typed, Drifted, and Incomplete Streaming Features

When and Where do Data Poisons Attack Textual Inversion?

Truth Sleuth and Trend Bender: AI Agents to fact-check YouTube videos and influence opinions

NLP Meets the World: Toward Improving Conversations With the Public About Natural Language Processing Research

Accurate generation of chemical reaction transition states by conditional flow matching

Benchmarking and Evaluation of AI Models in Biology: Outcomes and Recommendations from the CZI Virtual Cells Workshop

A PBN-RL-XAI Framework for Discovering a "Hit-and-Run" Therapeutic Strategy in Melanoma

NeuTSFlow: Modeling Continuous Functions Behind Time Series Forecasting

THOR: Transformer Heuristics for On-Demand Retrieval

Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs

Bridging Literature and the Universe Via A Multi-Agent Large Language Model System

Magneto-radiative modelling and artificial neural network optimization of biofluid flow in a stenosed arterial domain

Symbiosis: Multi-Adapter Inference and Fine-Tuning

Rethinking Data Protection in the (Generative) Artificial Intelligence Era

SoK: Semantic Privacy in Large Language Models

FedRef: Communication-Efficient Bayesian Fine Tuning with Reference Model

Predictable Scale: Part II, Farseer: A Refined Scaling Law in Large Language Models

Position Prediction Self-Supervised Learning for Multimodal Satellite Imagery Semantic Segmentation

ScaleRTL: Scaling LLMs with Reasoning Data and Test-Time Compute for Accurate RTL Code Generation

HueManity: Probing Fine-Grained Visual Perception in MLLMs

AKReF: An argumentative knowledge representation framework for structured argumentation

Large Language Models Often Know When They Are Being Evaluated

Dynamic Risk Assessments for Offensive Cybersecurity Agents

How Hungry is AI? Benchmarking Energy, Water, and Carbon Footprint of LLM Inference

Diffused Responsibility: Analyzing the Energy Consumption of Generative Text-to-Audio Diffusion Models

Flow-GRPO: Training Flow Matching Models via Online RL

On the Need for a Statistical Foundation in Scenario-Based Testing of Autonomous Vehicles

What's Pulling the Strings? Evaluating Integrity and Attribution in AI Training and Inference through Concept Shift

TD-EVAL: Revisiting Task-Oriented Dialogue Evaluation by Combining Turn-Level Precision with Dialogue-Level Comparisons

MobileCity: An Efficient Framework for Large-Scale Urban Behavior Simulation

Semantic Adapter for Universal Text Embeddings: Diagnosing and Mitigating Negation Blindness to Enhance Universality

Leveraging LLMs for User Stories in AI Systems: UStAI Dataset

Large Language Models are Unreliable for Cyber Threat Intelligence

AnnoPage Dataset: Dataset of Non-Textual Elements in Documents with Fine-Grained Categorization

A Thorough Assessment of the Non-IID Data Impact in Federated Learning

Visual Position Prompt for MLLM based Visual Grounding

Neurons: Emulating the Human Visual Cortex Improves Fidelity and Interpretability in fMRI-to-Video Reconstruction

FADE: Why Bad Descriptions Happen to Good Features

FlipConcept: Tuning-Free Multi-Concept Personalization for Text-to-Image Generation

LUMINA-Net: Low-light Upgrade through Multi-stage Illumination and Noise Adaptation Network for Image Enhancement

Towards Geo-Culturally Grounded LLM Generations

Learning to Reason at the Frontier of Learnability

Flexible and Efficient Grammar-Constrained Decoding

PATCH: a deep learning method to assess heterogeneity of artistic practice in historical paintings

The Impact of Modern AI in Metadata Management

Learning an Effective Premise Retrieval Model for Efficient Mathematical Formalization

ChipAlign: Instruction Alignment in Large Language Models for Chip Design via Geodesic Interpolation

Many Objective Problems Where Crossover is Provably Essential

Patherea: Cell Detection and Classification for the 2020s

ViTally Consistent: Scaling Biological Representation Learning for Cell Microscopy

TextDestroyer: A Training- and Annotation-Free Diffusion Method for Destroying Anomal Text from Images

Quantifying calibration error in modern neural networks through evidence based theory

Multi-view biomedical foundation models for molecule-target and property prediction

Reinforced Imitative Trajectory Planning for Urban Automated Driving

Distilling Invariant Representations with Dual Augmentation

Tackling the Abstraction and Reasoning Corpus with Vision Transformers: the Importance of 2D Representation, Positions, and Objects

Linearly-Interpretable Concept Embedding Models for Text Analysis

Towards Understanding Link Predictor Generalizability Under Distribution Shifts

StreakNet-Arch: An Anti-scattering Network-based Architecture for Underwater Carrier LiDAR-Radar Imaging

Enhancing Trust in Autonomous Agents: An Architecture for Accountability and Explainability through Blockchain and Large Language Models

On the Statistical Properties of Generative Adversarial Models for Low Intrinsic Data Dimension

Programming Distributed Collective Processes in the eXchange Calculus

Holistic analysis on the sustainability of Federated Learning across AI product lifecycle

Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory

Epic-Sounds: A Large-scale Dataset of Actions That Sound

From Semantic Web and MAS to Agentic AI: A Unified Narrative of the Web of Agents

On Gradual Semantics for Assumption-Based Argumentation

The Challenge of Teaching Reasoning to LLMs Without RL or Distillation

Continuous Classification Aggregation

Can Prompt Difficulty be Online Predicted for Accelerating RL Finetuning of Reasoning Models?

macOSWorld: A Multilingual Interactive Benchmark for GUI Agents

GeoChain: Multimodal Chain-of-Thought for Geographic Reasoning

Lost in Transmission: When and Why LLMs Fail to Reason Globally

A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems

System 0/1/2/3: Quad-process theory for multi-timescale embodied collective cognitive systems

Practical Principles for AI Cost and Compute Accounting

Generative Emergent Communication: Large Language Model is a Collective World Model

Proactive Agents for Multi-Turn Text-to-Image Generation Under Uncertainty

Learning Lifted STRIPS Models from Action Traces Alone: A Simple, General, and Scalable Solution

Hallucination Detox: Sensitivity Dropout (SenD) for Large Language Model Training

Life, uh, Finds a Way: Hyperadaptability by Behavioral Search

Governance of Generative Artificial Intelligence for Companies

RACER: Rational Artificial Intelligence Car-following-model Enhanced by Reality

Artificial Intelligence Governance for Businesses

Interpreting Radiologist's Intention from Eye Movements in Chest X-ray Diagnosis

S2WTM: Spherical Sliced-Wasserstein Autoencoder for Topic Modeling

LLM-Based Config Synthesis requires Disambiguation

Characterizing State Space Model (SSM) and SSM-Transformer Hybrid Language Model Performance with Long Context Length

EgoVLA: Learning Vision-Language-Action Models from Egocentric Human Videos

Can We Predict Alignment Before Models Finish Thinking? Towards Monitoring Misaligned Reasoning Models

Unit-Based Histopathology Tissue Segmentation via Multi-Level Feature Representation

Advancing Retrieval-Augmented Generation for Structured Enterprise and Internal Data

Mixture of Raytraced Experts

QuRe: Query-Relevant Retrieval through Hard Negative Sampling in Composed Image Retrieval

AutoVDC: Automated Vision Data Cleaning Using Vision-Language Models

Quantifying calibration error in modern neural networks through evidence based theory

Created by

Haebom

저자

Koffi Ismael Ouattara

개요

본 논문은 신뢰성, 확신도, 불확실성이 의사결정에 중요한 역할을 하는 중요한 응용 분야에서 신경망의 신뢰성을 정량화하는 새로운 프레임워크를 제시합니다. 기존의 정확도 및 정밀도와 같은 성능 지표는 특히 모델이 과신을 보이는 경우 이러한 측면을 포착하지 못합니다. 이를 위해 기대 교정 오차(ECE) 평가에 주관적 논리를 통합하여 신경망의 신뢰성을 정량화하는 프레임워크를 제안합니다. 예측 확률을 클러스터링하고 적절한 융합 연산자를 사용하여 의견을 융합함으로써 신뢰, 불신 및 불확실성에 대한 포괄적인 척도를 제공합니다. MNIST 및 CIFAR-10 데이터셋에 대한 실험을 통해 이 방법의 효과를 보여주며, 교정 후 결과는 신뢰성이 향상됨을 나타냅니다. 제안된 프레임워크는 의료 및 자율 시스템과 같은 민감한 영역에서 잠재적인 응용 프로그램을 가진 AI 모델에 대한 더 해석 가능하고 세분화된 평가를 제공합니다.

시사점, 한계점

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시사점:

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주관적 논리를 활용하여 신경망의 신뢰성을 더욱 정교하게 평가할 수 있는 새로운 프레임워크 제시.

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기존 지표의 한계를 극복하고, 신뢰, 불신, 불확실성을 종합적으로 고려.

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MNIST 및 CIFAR-10 데이터셋 실험을 통해 효과 검증 및 교정 후 신뢰성 향상 확인.

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의료, 자율주행 시스템 등 민감한 분야에서의 AI 모델 신뢰도 향상에 기여.

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한계점:

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제안된 프레임워크의 일반화 성능에 대한 추가적인 연구 필요.

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다양한 데이터셋 및 모델에 대한 추가적인 실험이 필요.

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주관적 논리의 매개변수 설정 및 최적화에 대한 추가적인 연구가 필요.

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실제 응용 분야에서의 적용 가능성 및 효과에 대한 추가적인 검증 필요.

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