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.

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

MIMIC-MJX: Neuromechanical Emulation of Animal Behavior

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저자

Charles Y. Zhang (Harvard University), Yuanjia Yang (Salk Institute for Biological Studies), Aidan Sirbu (Mila), Elliott T. T. Abe (University of Washington), Emil Warnberg (Harvard University), Eric J. Leonardis (Salk Institute for Biological Studies), Diego E. Aldarondo (Harvard University), Adam Lee (Harvard University), Aaditya Prasad (Massachusetts Institute of Technology), Jason Foat (Salk Institute for Biological Studies), Kaiwen Bian (Salk Institute for Biological Studies), Joshua Park (Salk Institute for Biological Studies), Rusham Bhatt (Salk Institute for Biological Studies), Hutton Saunders (Salk Institute for Biological Studies), Akira Nagamori (Salk Institute for Biological Studies), Ayesha R. Thanawalla (Salk Institute for Biological Studies), Kee Wui Huang (Salk Institute for Biological Studies), Fabian Plum (Imperial College London), Hendrik K. Beck (Imperial College London), Steven W. Flavell (Massachusetts Institute of Technology), David Labonte (Imperial College London), Blake A. Richards (Mila), Bingni W. Brunton (University of Washington), Eiman Azim (Salk Institute for Biological Studies), Bence P. Olveczky (Harvard University), Talmo D. Pereira (Salk Institute for Biological Studies)

MIMIC-MJX: 생물학적으로 타당한 신경 제어 정책 학습 프레임워크

개요

신경계의 주요 출력은 움직임과 행동입니다. 복잡한 행동 동안 자세 추적 기술이 발전했지만, 운동학적 궤적만으로는 근본적인 제어 프로세스에 대한 간접적인 접근만 가능합니다. 본 논문에서는 운동학적 데이터로부터 생물학적으로 타당한 신경 제어 정책을 학습하는 프레임워크인 MIMIC-MJX를 제시합니다. MIMIC-MJX는 실제 운동학적 궤적을 재현하기 위해 물리 시뮬레이션에서 생체역학적으로 현실적인 신체 모델을 작동하도록 학습하는 신경 컨트롤러를 훈련하여 운동 제어의 생성 프로세스를 모델링합니다. 본 연구는 구현이 정확하고, 빠르며, 데이터 효율적이며, 다양한 동물 신체 모델에 일반화될 수 있음을 보여줍니다. MIMIC-MJX로 훈련된 정책은 신경 제어 전략 분석과 행동 실험 시뮬레이션에 모두 활용될 수 있으며, 신경 과학을 위한 통합 모델링 프레임워크로서의 잠재력을 보여줍니다.

시사점, 한계점

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생물학적으로 타당한 신경 제어 정책 학습을 위한 새로운 프레임워크 제시

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정확하고, 빠르며, 데이터 효율적인 구현

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다양한 동물 신체 모델에 일반화 가능

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신경 제어 전략 분석 및 행동 실험 시뮬레이션에 활용 가능

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논문의 구체적인 한계점은 제시되지 않음 (Abstract에서)

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