Daily Arxiv

전 세계에서 발간되는 인공지능 관련 논문을 정리하는 페이지 입니다.
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OPTIC-ER: A Reinforcement Learning Framework for Real-Time Emergency Response and Equitable Resource Allocation in Underserved African Communities

BioAnalyst: A Foundation Model for Biodiversity

Scaling Towards the Information Boundary of Instruction Sets: The Infinity Instruct Subject Technical Report

Dual-Objective Reinforcement Learning with Novel Hamilton-Jacobi-Bellman Formulations

NeuroPhysNet: A FitzHugh-Nagumo-Based Physics-Informed Neural Network Framework for Electroencephalograph (EEG) Analysis and Motor Imagery Classification

PRO-V-R1: Reasoning Enhanced Programming Agent for RTL Verification

Large language models can learn and generalize steganographic chain-of-thought under process supervision

Turing Test 2.0: The General Intelligence Threshold

Consistency-based Abductive Reasoning over Perceptual Errors of Multiple Pre-trained Models in Novel Environments

Empowering Clients - Transformation of Design Processes Due to Generative AI

The Delusional Hedge Algorithm as a Model of Human Learning from Diverse Opinions

The Universal Weight Subspace Hypothesis

DraCo: Draft as CoT for Text-to-Image Preview and Rare Concept Generation

ShadowDraw: From Any Object to Shadow-Drawing Compositional Art

Semantic Soft Bootstrapping: Long Context Reasoning in LLMs without Reinforcement Learning

TV2TV: A Unified Framework for Interleaved Language and Video Generation

Structured Document Translation via Format Reinforcement Learning

SA-IQA: Redefining Image Quality Assessment for Spatial Aesthetics with Multi-Dimensional Rewards

David vs. Goliath: Can Small Models Win Big with Agentic AI in Hardware Design?

Multi-LLM Collaboration for Medication Recommendation

Meta-Learning for Quantum Optimization via Quantum Sequence Model

QKAN-LSTM: Quantum-inspired Kolmogorov-Arnold Long Short-term Memory

Arbitrage: Efficient Reasoning via Advantage-Aware Speculation

Model-Free Assessment of Simulator Fidelity via Quantile Curves

Reflection Removal through Efficient Adaptation of Diffusion Transformers

Evolutionary Architecture Search through Grammar-Based Sequence Alignment

Strategic Self-Improvement for Competitive Agents in AI Labour Markets

Balanced Few-Shot Episodic Learning for Accurate Retinal Disease Diagnosis

GeoPE:A Unified Geometric Positional Embedding for Structured Tensors

Realizable Abstractions: Near-Optimal Hierarchical Reinforcement Learning

LLMs Know More Than Words: A Genre Study with Syntax, Metaphor & Phonetics

CARL: Critical Action Focused Reinforcement Learning for Multi-Step Agent

Declarative Synthesis and Multi-Objective Optimization of Stripboard Circuit Layouts Using Answer Set Programming

ReflexFlow: Rethinking Learning Objective for Exposure Bias Alleviation in Flow Matching

Developing a General Personal Tutor for Education

SEAL: Self-Evolving Agentic Learning for Conversational Question Answering over Knowledge Graphs

Language Models as Semantic Teachers: Post-Training Alignment for Medical Audio Understanding

Mitigating Catastrophic Forgetting in Target Language Adaptation of LLMs via Source-Shielded Updates

From Symptoms to Systems: An Expert-Guided Approach to Understanding Risks of Generative AI for Eating Disorders

SoK: a Comprehensive Causality Analysis Framework for Large Language Model Security

Setting up for failure: automatic discovery of the neural mechanisms of cognitive errors

287,872 Supermassive Black Holes Masses: Deep Learning Approaching Reverberation Mapping Accuracy

YingMusic-SVC: Real-World Robust Zero-Shot Singing Voice Conversion with Flow-GRPO and Singing-Specific Inductive Biases

YingMusic-Singer: Zero-shot Singing Voice Synthesis and Editing with Annotation-free Melody Guidance

Using Machine Learning to Take Stay-or-Go Decisions in Data-driven Drone Missions

Embodied Co-Design for Rapidly Evolving Agents: Taxonomy, Frontiers, and Challenges

UnwrapDiff: Conditional Diffusion for Robust InSAR Phase Unwrapping

SignRoundV2: Closing the Performance Gap in Extremely Low-Bit Post-Training Quantization for LLMs

Neural Policy Composition from Free Energy Minimization

OsmT: Bridging OpenStreetMap Queries and Natural Language with Open-source Tag-aware Language Models

E3AD: An Emotion-Aware Vision-Language-Action Model for Human-Centric End-to-End Autonomous Driving

Measuring the Unspoken: A Disentanglement Model and Benchmark for Psychological Analysis in the Wild

Towards an AI Fluid Scientist: LLM-Powered Scientific Discovery in Experimental Fluid Mechanics

Large Speech Model Enabled Semantic Communication

TimesNet-Gen: Deep Learning-based Site Specific Strong Motion Generation

Generative AI for Self-Adaptive Systems: State of the Art and Research Roadmap

Topology Matters: Measuring Memory Leakage in Multi-Agent LLMs

Semi Centralized Training Decentralized Execution Architecture for Multi Agent Deep Reinforcement Learning in Traffic Signal Control

SEASON: Mitigating Temporal Hallucination in Video Large Language Models via Self-Diagnostic Contrastive Decoding

When GenAI Meets Fake News: Understanding Image Cascade Dynamics on Reddit

When Robots Should Say "I Don't Know": Benchmarking Abstention in Embodied Question Answering

A Light-Weight Large Language Model File Format for Highly-Secure Model Distribution

Diffusion Fine-Tuning via Reparameterized Policy Gradient of the Soft Q-Function

RRPO: Robust Reward Policy Optimization for LLM-based Emotional TTS

Multi-Loss Learning for Speech Emotion Recognition with Energy-Adaptive Mixup and Frame-Level Attention

AdmTree: Compressing Lengthy Context with Adaptive Semantic Trees

Detection of Intoxicated Individuals from Facial Video Sequences via a Recurrent Fusion Model

PhyVLLM: Physics-Guided Video Language Model with Motion-Appearance Disentanglement

Prototype-Based Semantic Consistency Alignment for Domain Adaptive Retrieval

UW-BioNLP at ChemoTimelines 2025: Thinking, Fine-Tuning, and Dictionary-Enhanced LLM Systems for Chemotherapy Timeline Extraction

GraphBench: Next-generation graph learning benchmarking

GuidNoise: Single-Pair Guided Diffusion for Generalized Noise Synthesis

Open-Ended Goal Inference through Actions and Language for Human-Robot Collaboration

NORi: An ML-Augmented Ocean Boundary Layer Parameterization

Automating Complex Document Workflows via Stepwise and Rollback-Enabled Operation Orchestration

Explainable Parkinsons Disease Gait Recognition Using Multimodal RGB-D Fusion and Large Language Models

Dual-Stream Spectral Decoupling Distillation for Remote Sensing Object Detection

Towards 6G Native-AI Edge Networks: A Semantic-Aware and Agentic Intelligence Paradigm

Adversarial Limits of Quantum Certification: When Eve Defeats Detection

FMA-Net++: Motion- and Exposure-Aware Real-World Joint Video Super-Resolution and Deblurring

MASE: Interpretable NLP Models via Model-Agnostic Saliency Estimation

STeP-Diff: Spatio-Temporal Physics-Informed Diffusion Models for Mobile Fine-Grained Pollution Forecasting

AutoGuard: A Self-Healing Proactive Security Layer for DevSecOps Pipelines Using Reinforcement Learning

Mitigating Object and Action Hallucinations in Multimodal LLMs via Self-Augmented Contrastive Alignment

Counting Without Running: Evaluating LLMs' Reasoning About Code Complexity

RGE-GCN: Recursive Gene Elimination with Graph Convolutional Networks for RNA-seq based Early Cancer Detection

DAComp: Benchmarking Data Agents across the Full Data Intelligence Lifecycle

Bayes-DIC Net: Estimating Digital Image Correlation Uncertainty with Bayesian Neural Networks

MANTRA: a Framework for Multi-stage Adaptive Noise TReAtment During Training

Evaluating Long-Context Reasoning in LLM-Based WebAgents

Gamma-from-Mono: Road-Relative, Metric, Self-Supervised Monocular Geometry for Vehicular Applications

Learning Single-Image Super-Resolution in the JPEG Compressed Domain

Bootstrapped Mixed Rewards for RL Post-Training: Injecting Canonical Action Order

Quantitative Analysis of Technical Debt and Pattern Violation in Large Language Model Architectures

The Initialization Determines Whether In-Context Learning Is Gradient Descent

Catching UX Flaws in Code: Leveraging LLMs to Identify Usability Flaws at the Development Stage

Hey GPT-OSS, Looks Like You Got It - Now Walk Me Through It! An Assessment of the Reasoning Language Models Chain of Thought Mechanism for Digital Forensics

Fine-Tuning ChemBERTa for Predicting Inhibitory Activity Against TDP1 Using Deep Learning

MVRoom: Controllable 3D Indoor Scene Generation with Multi-View Diffusion Models

CRAFT-E: A Neuro-Symbolic Framework for Embodied Affordance Grounding

Rewiring Human Brain Networks via Lightweight Dynamic Connectivity Framework: An EEG-Based Stress Validation

Created by

Haebom

Category

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저자

Sayantan Acharya, Abbas Khosravi, Douglas Creighton, Roohallah Alizadehsani, U. Rajendra Acharya

개요

AI 및 머신러닝 모델과 결합된 뇌파 분석이 스트레스 연구에서 중요성을 얻고 있다. 본 연구에서는 시간 가변 방향 전달 함수(TV DTF)를 기반으로 한 경량 동적 뇌 연결 프레임워크를 제안하고, 머신러닝 기반 스트레스 분류를 통해 TV DTF 특징을 검증했다. 32 채널 SAM 40 데이터셋의 뇌파 기록을 활용하여, 정신 산술 과제 실험을 진행했다. 알파-TV DTF가 가장 강력한 변별력을 보였으며, SVM은 3-class 분류에서 89.73% 정확도를, XGBoost는 2-class 분류에서 93.69% 정확도를 달성했다.

시사점, 한계점

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시사점:

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TV-DTF는 뇌의 시공간적 역학 및 스트레스 수준에 따른 방향적 영향을 밝혀내는 강력한 프레임워크로 검증됨.

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알파 및 베타-TV DTF는 절대 전력 및 위상 고정 기반 기능적 연결성 특징보다 높은 성능을 보임, 동적 측정의 장점을 강조.

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특징 중요도 분석은 장거리 전두-두정엽 및 전두-후두엽 정보적 영향력을 강조하며, 스트레스 하에서 전두엽 영역의 조절 역할을 강조.

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한계점:

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논문 내용에 명시된 한계점은 없음. (단, 연구 범위는 SAM 40 데이터셋에 국한됨)

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