Daily Arxiv

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Bridging Kolmogorov Complexity and Deep Learning: Asymptotically Optimal Description Length Objectives for Transformers

Spectral Collapse Drives Loss of Plasticity in Deep Continual Learning

MimicDreamer: Aligning Human and Robot Demonstrations for Scalable VLA Training

R-Capsule: Compressing High-Level Plans for Efficient Large Language Model Reasoning

DiTraj: training-free trajectory control for video diffusion transformer

Agribot: agriculture-specific question answer system

$\mathbf{Li_2}$: A Framework on Dynamics of Feature Emergence and Delayed Generalization

Dual-Head Reasoning Distillation: Improving Classifier Accuracy with Train-Time-Only Reasoning

Do Sparse Subnetworks Exhibit Cognitively Aligned Attention? Effects of Pruning on Saliency Map Fidelity, Sparsity, and Concept Coherence

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

Can Less Precise Be More Reliable? A Systematic Evaluation of Quantization's Impact on CLIP Beyond Accuracy

SiNGER: A Clearer Voice Distills Vision Transformers Further

i-LAVA: Insights on Low Latency Voice-2-Voice Architecture for Agents

Experience Deploying Containerized GenAI Services at an HPC Center

EmbeddingGemma: Powerful and Lightweight Text Representations

Beyond Sharp Minima: Robust LLM Unlearning via Feedback-Guided Multi-Point Optimization

Embedding Domain Knowledge for Large Language Models via Reinforcement Learning from Augmented Generation

Responsible AI Technical Report

Diffusion-Based Impedance Learning for Contact-Rich Manipulation Tasks

Diversity Boosts AI-Generated Text Detection

SPiDR: A Simple Approach for Zero-Shot Safety in Sim-to-Real Transfer

APRIL: Active Partial Rollouts in Reinforcement Learning to Tame Long-tail Generation

Self-Evolving LLMs via Continual Instruction Tuning

Reinforced Generation of Combinatorial Structures: Applications to Complexity Theory

Joint Memory Frequency and Computing Frequency Scaling for Energy-efficient DNN Inference

StefaLand: An Efficient Geoscience Foundation Model That Improves Dynamic Land-Surface Predictions

Accurate and Efficient Low-Rank Model Merging in Core Space

Patterns in the Transition From Founder-Leadership to Community Governance of Open Source

Enhancing Generative Auto-bidding with Offline Reward Evaluation and Policy Search

Fast and Fluent Diffusion Language Models via Convolutional Decoding and Rejective Fine-tuning

WorldForge: Unlocking Emergent 3D/4D Generation in Video Diffusion Model via Training-Free Guidance

TreeIRL: Safe Urban Driving with Tree Search and Inverse Reinforcement Learning

Evaluating undergraduate mathematics examinations in the era of generative AI: a curriculum-level case study

Learning to Route: Per-Sample Adaptive Routing for Multimodal Multitask Prediction

MindVL: Towards Efficient and Effective Training of Multimodal Large Language Models on Ascend NPUs

FuseCodec: Semantic-Contextual Fusion and Supervision for Neural Codecs

TalkPlayData 2: An Agentic Synthetic Data Pipeline for Multimodal Conversational Music Recommendation

Graph Alignment via Dual-Pass Spectral Encoding and Latent Space Communication

A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving

DEPFusion: Dual-Domain Enhancement and Priority-Guided Mamba Fusion for UAV Multispectral Object Detection

COMPACT: Common-token Optimized Model Pruning Across Channels and Tokens

BranchGRPO: Stable and Efficient GRPO with Structured Branching in Diffusion Models

The Physical Basis of Prediction: World Model Formation in Neural Organoids via an LLM-Generated Curriculum

Diffusion Generative Models Meet Compressed Sensing, with Applications to Imaging and Finance

Co-Evolving Complexity: An Adversarial Framework for Automatic MARL Curricula

Grocery to General Merchandise: A Cross-Pollination Recommender using LLMs and Real-Time Cart Context

Do LLMs Adhere to Label Definitions? Examining Their Receptivity to External Label Definitions

GradES: Significantly Faster Training in Transformers with Gradient-Based Early Stopping

Can General-Purpose Omnimodels Compete with Specialists? A Case Study in Medical Image Segmentation

Multimodal Iterative RAG for Knowledge-Intensive Visual Question Answering

TReF-6: Inferring Task-Relevant Frames from a Single Demonstration for One-Shot Skill Generalization

Evaluating the Effectiveness of Transformer Layers in Wav2Vec 2.0, XLS-R, and Whisper for Speaker Identification Tasks

End-to-End On-Device Quantization-Aware Training for LLMs at Inference Cost

Automatic Question & Answer Generation Using Generative Large Language Model (LLM)

CORE-RAG: Lossless Compression for Retrieval-Augmented LLMs via Reinforcement Learning

What Matters in Data for DPO?

Type-Compliant Adaptation Cascades: Adapting Programmatic LM Workflows to Data

Speculative Safety-Aware Decoding

Jet-Nemotron: Efficient Language Model with Post Neural Architecture Search

Coarse-to-Fine Personalized LLM Impressions for Streamlined Radiology Reports

ECHO: Frequency-aware Hierarchical Encoding for Variable-length Signals

Hard Examples Are All You Need: Maximizing GRPO Post-Training Under Annotation Budgets

Depth-Breadth Synergy in RLVR: Unlocking LLM Reasoning Gains with Adaptive Exploration

Contrastive Representations for Temporal Reasoning

Semantic Discrepancy-aware Detector for Image Forgery Identification

G-CUT3R: Guided 3D Reconstruction with Camera and Depth Prior Integration

BLADE: Block-Sparse Attention Meets Step Distillation for Efficient Video Generation

PakBBQ: A Culturally Adapted Bias Benchmark for QA

MoQE: Improve Quantization Model performance via Mixture of Quantization Experts

Discerning minds or generic tutors? Evaluating instructional guidance capabilities in Socratic LLMs

Beyond Prompt-Induced Lies: Investigating LLM Deception on Benign Prompts

AttriLens-Mol: Attribute Guided Reinforcement Learning for Molecular Property Prediction with Large Language Models

Sculptor: Empowering LLMs with Cognitive Agency via Active Context Management

CTTS: Collective Test-Time Scaling

The Geometry of Cortical Computation: Manifold Disentanglement and Predictive Dynamics in VCNet

Communicating Plans, Not Percepts: Scalable Multi-Agent Coordination with Embodied World Models

Can Language Models Discover Scaling Laws?

When Engineering Outruns Intelligence: Rethinking Instruction-Guided Navigation

A Markov Categorical Framework for Language Modeling

Moving Out: Physically-grounded Human-AI Collaboration

GLANCE: Graph Logic Attention Network with Cluster Enhancement for Heterophilous Graph Representation Learning

The Ever-Evolving Science Exam

Omni-Thinker: Scaling Multi-Task RL in LLMs with Hybrid Reward and Task Scheduling

GRID: Scalable Task-Agnostic Prompt-Based Continual Learning for Language Models

Learning to summarize user information for personalized reinforcement learning from human feedback

Making Language Model a Hierarchical Classifier

Vidar: Embodied Video Diffusion Model for Generalist Manipulation

BenchRL-QAS: Benchmarking reinforcement learning algorithms for quantum architecture search

Function Induction and Task Generalization: An Interpretability Study with Off-by-One Addition

Mitigating Watermark Forgery in Generative Models via Randomized Key Selection

Entropy-Memorization Law: Evaluating Memorization Difficulty of Data in LLMs

CoSteer: Collaborative Decoding-Time Personalization via Local Delta Steering

PRIME: Large Language Model Personalization with Cognitive Dual-Memory and Personalized Thought Process

Model Collapse Is Not a Bug but a Feature in Machine Unlearning for LLMs

Latent Chain-of-Thought? Decoding the Depth-Recurrent Transformer

Empirical Analysis Of Heuristic and Approximation Algorithms for the The Mutual-Visibility Problem

Learning to Segment for Vehicle Routing Problems

Theoretical Modeling of LLM Self-Improvement Training Dynamics Through Solver-Verifier Gap

Data Uniformity Improves Training Efficiency and More, with a Convergence Framework Beyond the NTK Regime

Semantic-guided Diverse Decoding for Large Language Model

Learning to Segment for Vehicle Routing Problems

Created by

Haebom

저자

Wenbin Ouyang, Sirui Li, Yining Ma, Cathy Wu

개요

본 연구는 차량 경로 문제(VRP) 해결을 위한 반복적 휴리스틱의 속도를 향상시키기 위해 First-Segment-Then-Aggregate (FSTA) 분해 기술을 최초로 정식 연구했습니다. 특히, 안정적인 솔루션 부분을 유지하고 불안정한 부분에만 집중하여 불필요한 계산을 줄이는 것을 목표로 합니다. 안정적인 세그먼트를 식별하기 위해, Learning-to-Segment (L2Seg)라는 새로운 신경망 프레임워크를 도입했습니다. L2Seg는 세 가지 변형(비자동 회귀, 자동 회귀, 시너지)으로 제공되며, CVRP 및 VRPTW 문제에 대한 실험 결과는 L2Seg가 최첨단 솔버의 속도를 2~7배 향상시켰음을 보여줍니다.

시사점, 한계점

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시사점:

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VRP 해결을 위한 반복적 휴리스틱의 속도 향상.

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FSTA 분해 기술의 도입과 L2Seg 프레임워크 개발.

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L2Seg의 세 가지 변형(비자동 회귀, 자동 회귀, 시너지) 제시 및 성능 비교.

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CVRP 및 VRPTW 문제에 대한 실험을 통해 성능 입증.

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전통적, 학습 기반 및 하이브리드 솔버와의 호환성.

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한계점:

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논문에서 구체적인 한계점에 대한 언급 없음.

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