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Daily Arxiv
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.
Segment Anything for Satellite Imagery: A Strong Baseline and a Regional Dataset for Automatic Field Delineation
VRAIL: Vectorized Reward-based Attribution for Interpretable Learning
LLM Web Dynamics: Tracing Model Collapse in a Network of LLMs
Accurate and scalable exchange-correlation with deep learning
Working Document -- Formalising Software Requirements with Large Language Models
AlphaDecay: Module-wise Weight Decay for Heavy-Tailed Balancing in LLMs
Bures-Wasserstein Flow Matching for Graph Generation
Distributional Training Data Attribution
AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning
Robust LLM Unlearning with MUDMAN: Meta-Unlearning with Disruption Masking And Normalization
Exploring the Secondary Risks of Large Language Models
Supernova Event Dataset: Interpreting Large Language Models' Personality through Critical Event Analysis
Agent-RLVR: Training Software Engineering Agents via Guidance and Environment Rewards
C-SEO Bench: Does Conversational SEO Work?
xInv: Explainable Optimization of Inverse Problems
Effective Red-Teaming of Policy-Adherent Agents
SLED: A Speculative LLM Decoding Framework for Efficient Edge Serving
PlantDeBERTa: An Open Source Language Model for Plant Science
Reinforcement Learning Teachers of Test Time Scaling
DriveSuprim: Towards Precise Trajectory Selection for End-to-End Planning
OWMM-Agent: Open World Mobile Manipulation With Multi-modal Agentic Data Synthesis
Optimizing Sensory Neurons: Nonlinear Attention Mechanisms for Accelerated Convergence in Permutation-Invariant Neural Networks for Reinforcement Learning
Dual Debiasing for Noisy In-Context Learning for Text Generation
Eye of Judgement: Dissecting the Evaluation of Russian-speaking LLMs with POLLUX
Maximizing Confidence Alone Improves Reasoning
Analysis and Evaluation of Synthetic Data Generation in Speech Dysfluency Detection
FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering
Position is Power: System Prompts as a Mechanism of Bias in Large Language Models (LLMs)
Cross from Left to Right Brain: Adaptive Text Dreamer for Vision-and-Language Navigation
Pretraining Language Models to Ponder in Continuous Space
SIPDO: Closed-Loop Prompt Optimization via Synthetic Data Feedback
Learning to Insert for Constructive Neural Vehicle Routing Solver
LightRetriever: A LLM-based Hybrid Retrieval Architecture with 1000x Faster Query Inference
MIRAGE: A Multi-modal Benchmark for Spatial Perception, Reasoning, and Intelligence
Introducing voice timbre attribute detection
The Voice Timbre Attribute Detection 2025 Challenge Evaluation Plan
TrumorGPT: Graph-Based Retrieval-Augmented Large Language Model for Fact-Checking
Democracy of AI Numerical Weather Models: An Example of Global Forecasting with FourCastNetv2 Made by a University Research Lab Using GPU
Learning to Reason under Off-Policy Guidance
Protecting Your Voice: Temporal-aware Robust Watermarking
Personalized News Recommendation with Multi-granularity Candidate-aware User Modeling
Learning from Reference Answers: Versatile Language Model Alignment without Binary Human Preference Data
AutoPDL: Automatic Prompt Optimization for LLM Agents
PiCo: Jailbreaking Multimodal Large Language Models via $\textbf{Pi}$ctorial $\textbf{Co}$de Contextualization
From Easy to Hard: Building a Shortcut for Differentially Private Image Synthesis
Context-Aware Human Behavior Prediction Using Multimodal Large Language Models: Challenges and Insights
Rubric Is All You Need: Enhancing LLM-based Code Evaluation With Question-Specific Rubrics
Shapley Revisited: Tractable Responsibility Measures for Query Answers
Simple and Critical Iterative Denoising: A Recasting of Discrete Diffusion in Graph Generation
Large Language Models powered Malicious Traffic Detection: Architecture, Opportunities and Case Study
TreeSynth: Synthesizing Diverse Data from Scratch via Tree-Guided Subspace Partitioning
A Dual-Directional Context-Aware Test-Time Learning for Text Classification
LED: LLM Enhanced Open-Vocabulary Object Detection without Human Curated Data Generation
HiRAG: Retrieval-Augmented Generation with Hierarchical Knowledge
Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions
POPGym Arcade: Parallel Pixelated POMDPs
BAnG: Bidirectional Anchored Generation for Conditional RNA Design
Directional Gradient Projection for Robust Fine-Tuning of Foundation Models
ParamMute: Suppressing Knowledge-Critical FFNs for Faithful Retrieval-Augmented Generation
Steering LLMs for Formal Theorem Proving
Exploring the Potential of Encoder-free Architectures in 3D LMMs
Compromising Honesty and Harmlessness in Language Models via Deception Attacks
Large Language Model Guided Self-Debugging Code Generation
ASCenD-BDS: Adaptable, Stochastic and Context-aware framework for Detection of Bias, Discrimination and Stereotyping
LoRA-One: One-Step Full Gradient Could Suffice for Fine-Tuning Large Language Models, Provably and Efficiently
Segmentation-Aware Generative Reinforcement Network (GRN) for Tissue Layer Segmentation in 3-D Ultrasound Images for Chronic Low-back Pain (cLBP) Assessment
AnyEnhance: A Unified Generative Model with Prompt-Guidance and Self-Critic for Voice Enhancement
SEAL: Scaling to Emphasize Attention for Long-Context Retrieval
CAD-GPT: Synthesising CAD Construction Sequence with Spatial Reasoning-Enhanced Multimodal LLMs
The Impact of Input Order Bias on Large Language Models for Software Fault Localization
GeAR: Graph-enhanced Agent for Retrieval-augmented Generation
Rethinking Cancer Gene Identification through Graph Anomaly Analysis
SurgSora: Object-Aware Diffusion Model for Controllable Surgical Video Generation
DSGram: Dynamic Weighting Sub-Metrics for Grammatical Error Correction in the Era of Large Language Models
Human Action CLIPs: Detecting AI-generated Human Motion
FLARE: Toward Universal Dataset Purification against Backdoor Attacks
G3Flow: Generative 3D Semantic Flow for Pose-aware and Generalizable Object Manipulation
Cross-Camera Distracted Driver Classification through Feature Disentanglement and Contrastive Learning
Song Form-aware Full-Song Text-to-Lyrics Generation with Multi-Level Granularity Syllable Count Control
Generating Energy-efficient code with LLMs
DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models
How Far is Video Generation from World Model: A Physical Law Perspective
One-Step is Enough: Sparse Autoencoders for Text-to-Image Diffusion Models
The Hive Mind is a Single Reinforcement Learning Agent
How Numerical Precision Affects Arithmetical Reasoning Capabilities of LLMs
FutureFill: Fast Generation from Convolutional Sequence Models
Leveraging Model Guidance to Extract Training Data from Personalized Diffusion Models
Machine-learning based high-bandwidth magnetic sensing
MOST: MR reconstruction Optimization for multiple downStream Tasks via continual learning
Bridging Geometric Diffusion and Energy Minimization: A Unified Framework for Neural Message Passing
Large Language Models for Disease Diagnosis: A Scoping Review
RePST: Language Model Empowered Spatio-Temporal Forecasting via Semantic-Oriented Reprogramming
Smooth InfoMax -- Towards Easier Post-Hoc Interpretability
PREMAP: A Unifying PREiMage APproximation Framework for Neural Networks
Reasoning Circuits in Language Models: A Mechanistic Interpretation of Syllogistic Inference
UniMoT: Unified Molecule-Text Language Model with Discrete Token Representation
Handling Numeric Expressions in Automatic Speech Recognition
"I understand why I got this grade": Automatic Short Answer Grading with Feedback
DART: An Automated End-to-End Object Detection Pipeline with Data Diversification, Open-Vocabulary Bounding Box Annotation, Pseudo-Label Review, and Model Training
Rich Interoperable Metadata for Cultural Heritage Projects at Jagiellonian University
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Designing an efficient and equitable humanitarian supply chain dynamically via reinforcement learning
Created by
Haebom
저자
Weijia Jin
개요
본 연구는 강화학습(PPO)을 이용하여 효율적이고 공평한 인도적 지원 공급망을 동적으로 설계하고, 휴리스틱 알고리즘과 비교 분석했습니다. PPO 모델은 평균 만족도를 최우선으로 고려하는 것을 보여줍니다.
시사점, 한계점
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시사점:
강화학습 기반 PPO 알고리즘을 활용하여 인도적 지원 공급망의 효율성 및 공평성을 동시에 향상시킬 수 있는 가능성을 제시합니다. 평균 만족도를 중시하는 PPO 모델의 특징을 확인했습니다.
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한계점:
구체적인 휴리스틱 알고리즘의 종류 및 성능 비교에 대한 자세한 설명이 부족합니다. PPO 모델의 실제 적용 가능성 및 일반화 성능에 대한 추가적인 검증이 필요합니다. 다양한 상황과 제약 조건을 고려한 실험 설계의 부족이 존재할 수 있습니다.
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