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.

Surgical Agent Orchestration Platform for Voice-directed Patient Data Interaction

FinRpt: Dataset, Evaluation System and LLM-based Multi-agent Framework for Equity Research Report Generation

Hard vs. Noise: Resolving Hard-Noisy Sample Confusion in Recommender Systems via Large Language Models

Learning Quantized Continuous Controllers for Integer Hardware

Differentiated Directional Intervention A Framework for Evading LLM Safety Alignment

TiS-TSL: Image-Label Supervised Surgical Video Stereo Matching via Time-Switchable Teacher-Student Learning

Active Learning for Animal Re-Identification with Ambiguity-Aware Sampling

Transolver is a Linear Transformer: Revisiting Physics-Attention through the Lens of Linear Attention

Exploiting Inter-Session Information with Frequency-enhanced Dual-Path Networks for Sequential Recommendation

SWE-fficiency: Can Language Models Optimize Real-World Repositories on Real Workloads?

A Remarkably Efficient Paradigm to Multimodal Large Language Models for Sequential Recommendation

EndoIR: Degradation-Agnostic All-in-One Endoscopic Image Restoration via Noise-Aware Routing Diffusion

Enhancing Diffusion Model Guidance through Calibration and Regularization

DRAGON: Guard LLM Unlearning in Context via Negative Detection and Reasoning

Report from Workshop on Dialogue alongside Artificial Intelligence

Token Is All You Need: Cognitive Planning through Belief-Intent Co-Evolution

Selective Diabetic Retinopathy Screening with Accuracy-Weighted Deep Ensembles and Entropy-Guided Abstention

SWE-Compass: Towards Unified Evaluation of Agentic Coding Abilities for Large Language Models

Alternative Fairness and Accuracy Optimization in Criminal Justice

I Detect What I Don't Know: Incremental Anomaly Learning with Stochastic Weight Averaging-Gaussian for Oracle-Free Medical Imaging

OMPILOT: Harnessing Transformer Models for Auto Parallelization to Shared Memory Computing Paradigms

Federated Learning with Gramian Angular Fields for Privacy-Preserving ECG Classification on Heterogeneous IoT Devices

How to Evaluate Speech Translation with Source-Aware Neural MT Metrics

GUI-AIMA: Aligning Intrinsic Multimodal Attention with a Context Anchor for GUI Grounding

Normalization in Attention Dynamics

Preference-based Reinforcement Learning beyond Pairwise Comparisons: Benefits of Multiple Options

Language over Content: Tracing Cultural Understanding in Multilingual Large Language Models

TACL: Threshold-Adaptive Curriculum Learning Strategy for Enhancing Medical Text Understanding

TraceCoder: Towards Traceable ICD Coding via Multi-Source Knowledge Integration

Bee: A High-Quality Corpus and Full-Stack Suite to Unlock Advanced Fully Open MLLMs

Comparative Analysis of Large Language Models for the Machine-Assisted Resolution of User Intentions

Evolutionary Profiles for Protein Fitness Prediction

Epistemic Diversity and Knowledge Collapse in Large Language Models

MENLO: From Preferences to Proficiency -- Evaluating and Modeling Native-like Quality Across 47 Languages

Towards Foundation Models for Zero-Shot Time Series Anomaly Detection: Leveraging Synthetic Data and Relative Context Discrepancy

CyberSOCEval: Benchmarking LLMs Capabilities for Malware Analysis and Threat Intelligence Reasoning

A Realistic Evaluation of Cross-Frequency Transfer Learning and Foundation Forecasting Models

TimeMosaic: Temporal Heterogeneity Guided Time Series Forecasting via Adaptive Granularity Patch and Segment-wise Decoding

RSVG-ZeroOV: Exploring a Training-Free Framework for Zero-Shot Open-Vocabulary Visual Grounding in Remote Sensing Images

Instance Generation for Meta-Black-Box Optimization through Latent Space Reverse Engineering

Decoding Latent Attack Surfaces in LLMs: Prompt Injection via HTML in Web Summarization

Towards Methane Detection Onboard Satellites

OPERA: A Reinforcement Learning--Enhanced Orchestrated Planner-Executor Architecture for Reasoning-Oriented Multi-Hop Retrieval

Can LLM-Generated Textual Explanations Enhance Model Classification Performance? An Empirical Study

Towards Embodied Agentic AI: Review and Classification of LLM- and VLM-Driven Robot Autonomy and Interaction

CoCoLIT: ControlNet-Conditioned Latent Image Translation for MRI to Amyloid PET Synthesis

Beyond Algorethics: Addressing the Ethical and Anthropological Challenges of AI Recommender Systems

Imbalance in Balance: Online Concept Balancing in Generation Models

ReCode: Updating Code API Knowledge with Reinforcement Learning

Rethinking Losses for Diffusion Bridge Samplers

Zeroth-Order Optimization Finds Flat Minima

UniSite: The First Cross-Structure Dataset and Learning Framework for End-to-End Ligand Binding Site Detection

A Unified and Fast-Sampling Diffusion Bridge Framework via Stochastic Optimal Control

BroadGen: A Framework for Generating Effective and Efficient Advertiser Broad Match Keyphrase Recommendations

FB-RAG: Improving RAG with Forward and Backward Lookup

FedSEA-LLaMA: A Secure, Efficient and Adaptive Federated Splitting Framework for Large Language Models

Policy-Driven World Model Adaptation for Robust Offline Model-based Reinforcement Learning

RL in Name Only? Analyzing the Structural Assumptions in RL post-training for LLMs

Internal Causal Mechanisms Robustly Predict Language Model Out-of-Distribution Behaviors

Tool-Aided Evolutionary LLM for Generative Policy Toward Efficient Resource Management in Wireless Federated Learning

FALCON: False-Negative Aware Learning of Contrastive Negatives in Vision-Language Alignment

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

FaSDiff: Balancing Perception and Semantics in Face Compression via Stable Diffusion Priors

On the generalization of language models from in-context learning and finetuning: a controlled study

Enhancing Speech-to-Speech Dialogue Modeling with End-to-End Retrieval-Augmented Generation

A Multimodal Recaptioning Framework to Account for Perceptual Diversity Across Languages in Vision-Language Modeling

WildFireCan-MMD: A Multimodal Dataset for Classification of User-Generated Content During Wildfires in Canada

MULTI-LF: A Continuous Learning Framework for Real-Time Malicious Traffic Detection in Multi-Environment Networks

STAR-1: Safer Alignment of Reasoning LLMs with 1K Data

COPA: Comparing the incomparable in multi-objective model evaluation

CLEV: LLM-Based Evaluation Through Lightweight Efficient Voting for Free-Form Question-Answering

Towards Synthesizing High-Dimensional Tabular Data with Limited Samples

Explaining the Unexplainable: A Systematic Review of Explainable AI in Finance

Learning Vision-Based Neural Network Controllers with Semi-Probabilistic Safety Guarantees

MA-GTS: A Multi-Agent Framework for Solving Complex Graph Problems in Real-World Applications

On the Convergence and Stability of Upside-Down Reinforcement Learning, Goal-Conditioned Supervised Learning, and Online Decision Transformers

A Multi-Agent Conversational Bandit Approach to Online Evaluation and Selection of User-Aligned LLM Responses

Generalizing Weisfeiler-Lehman Kernels to Subgraphs

SCoTT: Strategic Chain-of-Thought Tasking for Wireless-Aware Robot Navigation in Digital Twins

GeMID: Generalizable Models for IoT Device Identification

Flash Inference: Near Linear Time Inference for Long Convolution Sequence Models and Beyond

Bayes Adaptive Monte Carlo Tree Search for Offline Model-based Reinforcement Learning

Selection of LLM Fine-Tuning Data based on Orthogonal Rules

Benchmarking Domain Generalization Algorithms in Computational Pathology

Reference-Guided Verdict: LLMs-as-Judges in Automatic Evaluation of Free-Form QA

Identifying treatment response subgroups in observational time-to-event data

Informed Correctors for Discrete Diffusion Models

Integrating Artificial Intelligence into Operating Systems: A Survey on Techniques, Applications, and Future Directions

Multimodal Adversarial Defense for Vision-Language Models by Leveraging One-To-Many Relationships

Spikingformer: A Key Foundation Model for Spiking Neural Networks

DeepPersona: A Generative Engine for Scaling Deep Synthetic Personas

Two Heads are Better than One: Distilling Large Language Model Features Into Small Models with Feature Decomposition and Mixture

A Theoretical Analysis of Detecting Large Model-Generated Time Series

Green AI: A systematic review and meta-analysis of its definitions, lifecycle models, hardware and measurement attempts

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

Reasoning with Confidence: Efficient Verification of LLM Reasoning Steps via Uncertainty Heads

When Object-Centric World Models Meet Policy Learning: From Pixels to Policies, and Where It Breaks

ScRPO: From Errors to Insights

Deep Value Benchmark: Measuring Whether Models Generalize Deep Values or Shallow Preferences

Glia: A Human-Inspired AI for Automated Systems Design and Optimization

Mutual Understanding between People and Systems via Neurosymbolic AI and Knowledge Graphs

Created by

Haebom

저자

Irene Celino, Mario Scrocca, Agnese Chiatti

개요

본 논문은 인간과 시스템 간의 상호 이해 개념을 조사하고, 데이터 기반 학습 모델과 명시적 기호적 지식 표현을 활용하는 신경 기호 인공지능(NeSy AI) 방법이 이러한 상호 이해를 크게 향상시킬 수 있다고 주장합니다. 상호 이해를 특징짓는 세 가지 중요한 차원(지식 공유, 지식 교환, 지식 관리)을 제시하고, NeSy AI와 지식 그래프를 활용하여 인간, 인공 및 로봇 에이전트 간의 의미있는 교류를 돕는 여러 가지 사용 사례 시나리오를 제시합니다. 상향식 신경 학습과 하향식 기호적 추론을 결합하는 것의 잠재력과 과제를 강조하며, 현재 솔루션이 지식 공유, 교환 및 관리 차원에서 제공하는 범위에 대한 논의를 안내합니다. 동시에 이 분석은 미래 연구에서 해결해야 할 상호 이해의 격차와 덜 개발된 측면을 파악하는 데 도움이 됩니다.

시사점, 한계점

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시사점:

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NeSy AI가 인간과 시스템 간 상호 이해 향상에 기여할 수 있음을 제시.

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상호 이해의 세 가지 중요한 차원(지식 공유, 교환, 관리)을 명확히 정의.

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지식 그래프와 NeSy AI의 다양한 활용 사례 제시.

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향후 연구 방향 제시를 통해 상호 이해 증진 위한 연구 발전에 기여.

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한계점:

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제시된 사용 사례 시나리오의 구체적인 내용과 평가가 부족할 수 있음.

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NeSy AI의 실제 구현 및 성능에 대한 자세한 분석이 부족할 수 있음.

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상호 이해의 세 가지 차원에 대한 정의가 더욱 명확하고 포괄적일 필요가 있을 수 있음.

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현재 솔루션의 범위와 한계에 대한 더욱 심도있는 분석이 필요할 수 있음.

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