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.

Instella: Fully Open Language Models with Stellar Performance

STAGE: A Symbolic Tensor grAph GEnerator for distributed AI system co-design

Speech-Audio Compositional Attacks on Multimodal LLMs and Their Mitigation with SALMONN-Guard

PustakAI: Curriculum-Aligned and Interactive Textbooks Using Large Language Models

Harnessing Bounded-Support Evolution Strategies for Policy Refinement

Potent but Stealthy: Rethink Profile Pollution against Sequential Recommendation via Bi-level Constrained Reinforcement Paradigm

FAST-CAD: A Fairness-Aware Framework for Non-Contact Stroke Diagnosis

Learning Topology-Driven Multi-Subspace Fusion for Grassmannian Deep Network

LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics

Contrastive Integrated Gradients: A Feature Attribution-Based Method for Explaining Whole Slide Image Classification

SafeMIL: Learning Offline Safe Imitation Policy from Non-Preferred Trajectories

Dynamic Sparsity: Challenging Common Sparsity Assumptions for Learning World Models in Robotic Reinforcement Learning Benchmarks

Invisible Triggers, Visible Threats! Road-Style Adversarial Creation Attack for Visual 3D Detection in Autonomous Driving

Intelligence per Watt: Measuring Intelligence Efficiency of Local AI

SPUR: A Plug-and-Play Framework for Integrating Spatial Audio Understanding and Reasoning into Large Audio-Language Models

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

EMOD: A Unified EEG Emotion Representation Framework Leveraging V-A Guided Contrastive Learning

AI Assisted AR Assembly: Object Recognition and Computer Vision for Augmented Reality Assisted Assembly

Adaptive Detection of Software Aging under Workload Shift

Pelican-VL 1.0: A Foundation Brain Model for Embodied Intelligence

A filtering scheme for confocal laser endomicroscopy (CLE)-video sequences for self-supervised learning

Reflections on the Reproducibility of Commercial LLM Performance in Empirical Software Engineering Studies

Advantage Shaping as Surrogate Reward Maximization: Unifying Pass@K Policy Gradients

ICL-Router: In-Context Learned Model Representations for LLM Routing

Causal Digital Twins for Cyber-Physical Security: A Framework for Robust Anomaly Detection in Industrial Control Systems

Synthetic Object Compositions for Scalable and Accurate Learning in Detection, Segmentation, and Grounding

An Adaptive Multi Agent Bitcoin Trading System

BanglaTalk: Towards Real-Time Speech Assistance for Bengali Regional Dialects

SecInfer: Preventing Prompt Injection via Inference-time Scaling

Re-FRAME the Meeting Summarization SCOPE: Fact-Based Summarization and Personalization via Questions

Threat Modeling for Enhancing Security of IoT Audio Classification Devices under a Secure Protocols Framework

Efficient Learning-Based Control of a Legged Robot in Lunar Gravity

Adaptive Pareto-Optimal Token Merging for Edge Transformer Models in Semantic Communication

Advanced Torrential Loss Function for Precipitation Forecasting

TTF-VLA: Temporal Token Fusion via Pixel-Attention Integration for Vision-Language-Action Models

$\mathrm{TIME}[t]\subseteq \mathrm{SPACE}[O(\sqrt{t})]$ via Tree Height Compression

Visual Document Understanding and Reasoning: A Multi-Agent Collaboration Framework with Agent-Wise Adaptive Test-Time Scaling

FastDriveVLA: Efficient End-to-End Driving via Plug-and-Play Reconstruction-based Token Pruning

First-Order Error Matters: Accurate Compensation for Quantized Large Language Models

StreamDiT: Real-Time Streaming Text-to-Video Generation

NOCTIS: Novel Object Cyclic Threshold based Instance Segmentation

On the Necessity of Output Distribution Reweighting for Effective Class Unlearning

Orthogonal Soft Pruning for Efficient Class Unlearning

Optimization-Induced Dynamics of Lipschitz Continuity in Neural Networks

Self-supervised Learning of Echocardiographic Video Representations via Online Cluster Distillation

Symmetrical Flow Matching: Unified Image Generation, Segmentation, and Classification with Score-Based Generative Models

Zero-Shot Temporal Interaction Localization for Egocentric Videos

Latent Principle Discovery for Language Model Self-Improvement

Transformer Copilot: Learning from The Mistake Log in LLM Fine-tuning

Towards Effective Federated Graph Foundation Model via Mitigating Knowledge Entanglement

$\textit{New News}$: System-2 Fine-tuning for Robust Integration of New Knowledge

Representation Meets Optimization: Training PINNs and PIKANs for Gray-Box Discovery in Systems Pharmacology

Walk Before You Dance: High-fidelity and Editable Dance Synthesis via Generative Masked Motion Prior

Mining--Gym: A Configurable RL Benchmarking Environment for Truck Dispatch Scheduling

The Empty Chair: Using LLMs to Raise Missing Perspectives in Policy Deliberations

Concept-as-Tree: A Controllable Synthetic Data Framework Makes Stronger Personalized VLMs

FedALT: Federated Fine-Tuning through Adaptive Local Training with Rest-of-World LoRA

RAG-Enhanced Collaborative LLM Agents for Drug Discovery

Text-to-SQL Domain Adaptation via Human-LLM Collaborative Data Annotation

FQ-PETR: Fully Quantized Position Embedding Transformation for Multi-View 3D Object Detection

Enhanced Structured Lasso Pruning with Class-wise Information

Efficient Image Restoration via Latent Consistency Flow Matching

Adapt-Pruner: Adaptive Structural Pruning for Efficient Small Language Model Training

The Temporal Trap: Entanglement in Pre-Trained Visual Representations for Visuomotor Policy Learning

LDC: Learning to Generate Research Idea with Dynamic Control

MOSABench: Multi-Object Sentiment Analysis Benchmark for Evaluating Multimodal Large Language Models Understanding of Complex Image

DreamRunner: Fine-Grained Compositional Story-to-Video Generation with Retrieval-Augmented Motion Adaptation

Strada-LLM: Graph LLM for traffic prediction

Towards Formalizing Spuriousness of Biased Datasets Using Partial Information Decomposition

Are language models rational? The case of coherence norms and belief revision

Posterior Label Smoothing for Node Classification

Partial Information Decomposition for Data Interpretability and Feature Selection

Fractured Glass, Failing Cameras: Simulating Physics-Based Adversarial Samples for Autonomous Driving Systems

MoPE: Mixture of Prompt Experts for Parameter-Efficient and Scalable Multimodal Fusion

Survey in Characterization of Semantic Change

GreatSplicing: A Semantically Rich Splicing Dataset

Bi-Level Contextual Bandits for Individualized Resource Allocation under Delayed Feedback

Strategic Opponent Modeling with Graph Neural Networks, Deep Reinforcement Learning and Probabilistic Topic Modeling

Intelligence Foundation Model: A New Perspective to Approach Artificial General Intelligence

Enhancing the Medical Context-Awareness Ability of LLMs via Multifaceted Self-Refinement Learning

OIDA-QA: A Multimodal Benchmark for Analyzing the Opioid Industry Documents Archive

Proceedings of the Second International Workshop on Next-Generation Language Models for Knowledge Representation and Reasoning (NeLaMKRR 2025)

An Efficient Training Pipeline for Reasoning Graphical User Interface Agents

Thinker: Training LLMs in Hierarchical Thinking for Deep Search via Multi-Turn Interaction

Data Complexity of Querying Description Logic Knowledge Bases under Cost-Based Semantics

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

Synthetic Data-Driven Prompt Tuning for Financial QA over Tables and Documents

Large Language Model-assisted Autonomous Vehicle Recovery from Immobilization

VoiceAgentEval: A Dual-Dimensional Benchmark for Expert-Level Intelligent Voice-Agent Evaluation of Xbench's Professional-Aligned Series

Practical, Utilitarian Algorithm Configuration

Clutch Control: An Attention-based Combinatorial Bandit for Efficient Mutation in JavaScript Engine Fuzzing

The Carbon Footprint Wizard: A Knowledge-Augmented AI Interface for Streamlining Food Carbon Footprint Analysis

NetGent: Agent-Based Automation of Network Application Workflows

CAMA: Enhancing Mathematical Reasoning in Large Language Models with Causal Knowledge

Optimizing Multi-Tier Supply Chain Ordering with LNN+XGBoost: Mitigating the Bullwhip Effect

Efficient Story Point Estimation With Comparative Learning

Comprehension Without Competence: Architectural Limits of LLMs in Symbolic Computation and Reasoning

MUDAS: Mote-scale Unsupervised Domain Adaptation in Multi-label Sound Classification

Modeling the Diachronic Evolution of Legal Norms: An LRMoo-Based, Component-Level, Event-Centric Approach to Legal Knowledge Graphs

Sensory-Motor Control with Large Language Models via Iterative Policy Refinement

Offline Dynamic Inventory and Pricing Strategy: Addressing Censored and Dependent Demand

Created by

Haebom

저자

Korel Gundem, Zhengling Qi

개요

본 논문은 과거 수요 레벨에 의존하며 재고 초과 수요는 손실되는 오프라인 순차적 특징 기반 가격 및 재고 관리 문제를 연구합니다. 과거 가격, 주문 수량, 재고 레벨, 공변량 및 검열된 판매 레벨로 구성된 오프라인 데이터셋을 활용하여 장기적인 이윤을 극대화하는 최적의 가격 및 재고 관리 정책을 추정하는 것을 목표로 합니다. 검열이 없는 기본 동역학은 마르코프 의사결정 과정(MDP)으로 모델링될 수 있지만, 수요 검열이 존재하는 관측된 과정에서는 누락된 이윤 정보, 마르코프 특성의 실패, 비정상적인 최적 정책이라는 주요 장애물이 발생합니다. 이러한 과제를 극복하기 위해, 먼저 연속적인 검열 인스턴스의 수로 특징지어지는 고차 MDP를 풀어 최적 정책을 근사합니다. 이는 궁극적으로 이 문제에 맞게 조정된 특수한 벨만 방정식을 푸는 것으로 귀결됩니다. 오프라인 강화 학습과 생존 분석에서 영감을 얻어, 이러한 벨만 방정식을 풀고 최적 정책을 추정하기 위한 두 가지 새로운 데이터 기반 알고리즘을 제안합니다. 또한, 이러한 알고리즘의 효과를 검증하기 위해 유한 샘플 후회 경계를 설정합니다. 마지막으로, 최적 정책을 추정하는 알고리즘의 효능을 보여주는 수치 실험을 수행합니다. 저자의 지식에 따르면, 검열되고 의존적인 수요를 특징으로 하는 순차적 의사결정 환경에서 최적의 가격 및 재고 관리 정책을 학습하는 최초의 데이터 기반 접근 방식입니다. 제안된 알고리즘의 구현은 https://github.com/gundemkorel/Inventory_Pricing_Control 에서 확인할 수 있습니다.

GitHub - gundemkorel/Inventory_Pricing_Control

Contribute to gundemkorel/Inventory_Pricing_Control development by creating an account on GitHub.

시사점, 한계점

•

시사점: 검열되고 의존적인 수요를 가진 순차적 의사결정 환경에서 최적의 가격 및 재고 관리 정책을 학습하는 최초의 데이터 기반 접근 방식을 제시합니다. 고차 MDP와 새로운 데이터 기반 알고리즘을 통해 검열 문제를 효과적으로 해결합니다. 유한 샘플 후회 경계를 통해 알고리즘의 효과를 이론적으로 뒷받침합니다.

•

한계점: 알고리즘의 성능은 고차 MDP의 차수 및 데이터의 질에 의존적일 수 있습니다. 실제 환경에서의 적용 가능성을 더욱 검증하기 위한 추가적인 실험이 필요합니다. 특히, 고차원 공변량이나 매우 복잡한 수요 패턴에 대한 알고리즘의 일반화 성능에 대한 추가 연구가 필요합니다. 현실적인 데이터셋의 크기와 복잡성을 고려했을 때 계산 비용이 상당할 수 있습니다.

Made with Slashpage