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.

Training-Free Text-Guided Color Editing with Multi-Modal Diffusion Transformer

SPARC: Soft Probabilistic Adaptive multi-interest Retrieval Model via Codebooks for recommender system

When Deepfakes Look Real: Detecting AI-Generated Faces with Unlabeled Data due to Annotation Challenges

TempOpt -- Unsupervised Alarm Relation Learning for Telecommunication Networks

A Survey on Parallel Text Generation: From Parallel Decoding to Diffusion Language Models

Transferable Model-agnostic Vision-Language Model Adaptation for Efficient Weak-to-Strong Generalization

Yan: Foundational Interactive Video Generation

MLLM-CBench:A Comprehensive Benchmark for Continual Instruction Tuning of Multimodal LLMs with Chain-of-Thought Reasoning Analysis

VGGSounder: Audio-Visual Evaluations for Foundation Models

Capabilities of GPT-5 on Multimodal Medical Reasoning

C-MAG: Cascade Multimodal Attributed Graphs for Supply Chain Link Prediction

Beyond Ten Turns: Unlocking Long-Horizon Agentic Search with Large-Scale Asynchronous RL

MIND: A Noise-Adaptive Denoising Framework for Medical Images Integrating Multi-Scale Transformer

FlexCTC: GPU-powered CTC Beam Decoding With Advanced Contextual Abilities

Fairness of Automatic Speech Recognition: Looking Through a Philosophical Lens

Generalizing Scaling Laws for Dense and Sparse Large Language Models

Memp: Exploring Agent Procedural Memory

InfoCausalQA:Can Models Perform Non-explicit Causal Reasoning Based on Infographic?

Benchmarking Pretrained Molecular Embedding Models For Molecular Representation Learning

Request-Only Optimization for Recommendation Systems

Chemist Eye: A Visual Language Model-Powered System for Safety Monitoring and Robot Decision-Making in Self-Driving Laboratories

GTPO and GRPO-S: Token and Sequence-Level Reward Shaping with Policy Entropy

FairPOT: Balancing AUC Performance and Fairness with Proportional Optimal Transport

GTPO: Trajectory-Based Policy Optimization in Large Language Models

Block: Balancing Load in LLM Serving with Context, Knowledge and Predictive Scheduling

Estimating Worst-Case Frontier Risks of Open-Weight LLMs

LiteFat: Lightweight Spatio-Temporal Graph Learning for Real-Time Driver Fatigue Detection

DRWKV: Focusing on Object Edges for Low-Light Image Enhancement

A multi-strategy improved snake optimizer for three-dimensional UAV path planning and engineering problems

Fragment size density estimator for shrinkage-induced fracture based on a physics-informed neural network

GLM-4.1V-Thinking and GLM-4.5V: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning

WebArXiv: Evaluating Multimodal Agents on Time-Invariant arXiv Tasks

Audio-3DVG: Unified Audio -- Point Cloud Fusion for 3D Visual Grounding

Beyond Autocomplete: Designing CopilotLens Towards Transparent and Explainable AI Coding Agents

OC-SOP: Enhancing Vision-Based 3D Semantic Occupancy Prediction by Object-Centric Awareness

SWA-SOP: Spatially-aware Window Attention for Semantic Occupancy Prediction in Autonomous Driving

The Importance of Being Lazy: Scaling Limits of Continual Learning

Human Motion Capture from Loose and Sparse Inertial Sensors with Garment-aware Diffusion Models

HVL: Semi-Supervised Segmentation leveraging Hierarchical Vision-Language Synergy with Dynamic Text-Spatial Query Alignment

Open-Set LiDAR Panoptic Segmentation Guided by Uncertainty-Aware Learning

Poison Once, Control Anywhere: Clean-Text Visual Backdoors in VLM-based Mobile Agents

MGDFIS: Multi-scale Global-detail Feature Integration Strategy for Small Object Detection

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems

ChineseHarm-Bench: A Chinese Harmful Content Detection Benchmark

Gradual Transition from Bellman Optimality Operator to Bellman Operator in Online Reinforcement Learning

Sarc7: Evaluating Sarcasm Detection and Generation with Seven Types and Emotion-Informed Techniques

Exploring Scaling Laws for EHR Foundation Models

MapStory: Prototyping Editable Map Animations with LLM Agents

Teaching Large Language Models to Maintain Contextual Faithfulness via Synthetic Tasks and Reinforcement Learning

Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind

Halting Recurrent GNNs and the Graded $\mu$-Calculus

Deep Learning Warm Starts for Trajectory Optimization on the International Space Station

EmoVoice: LLM-based Emotional Text-To-Speech Model with Freestyle Text Prompting

FedRecon: Missing Modality Reconstruction in Heterogeneous Distributed Environments

AI-Slop to AI-Polish? Aligning Language Models through Edit-Based Writing Rewards and Test-time Computation

GraspClutter6D: A Large-scale Real-world Dataset for Robust Perception and Grasping in Cluttered Scenes

Mosaic: Composite Projection Pruning for Resource-efficient LLMs

CO-Bench: Benchmarking Language Model Agents in Algorithm Search for Combinatorial Optimization

FT-Transformer: Resilient and Reliable Transformer with End-to-End Fault Tolerant Attention

The Illusory Normativity of Rights-Based AI Regulation

Shifting Perspectives: Steering Vectors for Robust Bias Mitigation in LLMs

Simulating the Real World: A Unified Survey of Multimodal Generative Models

RIZE: Regularized Imitation Learning via Distributional Reinforcement Learning

One-shot Optimized Steering Vectors Mediate Safety-relevant Behaviors in LLMs

EvoP: Robust LLM Inference via Evolutionary Pruning

Conformal Prediction of Classifiers with Many Classes based on Noisy Labels

Benchmarking LLMs' Mathematical Reasoning with Unseen Random Variables Questions

GenAI Confessions: Black-box Membership Inference for Generative Image Models

Beyond Memorization: Assessing Semantic Generalization in Large Language Models Using Phrasal Constructions

Evaluation of Bio-Inspired Models under Different Learning Settings For Energy Efficiency in Network Traffic Prediction

SLTNet: Efficient Event-based Semantic Segmentation with Spike-driven Lightweight Transformer-based Networks

Leveraging Audio and Text Modalities in Mental Health: A Study of LLMs Performance

Learning Characteristics of Reverse Quaternion Neural Network

Depth-Guided Self-Supervised Human Keypoint Detection via Cross-Modal Distillation

Retrieval-Augmented Decision Transformer: External Memory for In-context RL

Downscaling Extreme Precipitation with Wasserstein Regularized Diffusion

Episodic Memory Verbalization using Hierarchical Representations of Life-Long Robot Experience

Return Prediction for Mean-Variance Portfolio Selection: How Decision-Focused Learning Shapes Forecasting Models

Pediatric brain tumor classification using digital histopathology and deep learning: evaluation of SOTA methods on a multi-center Swedish cohort

CTRQNets & LQNets: Continuous Time Recurrent and Liquid Quantum Neural Networks

Explaining Caption-Image Interactions in CLIP Models with Second-Order Attributions

SpectralEarth: Training Hyperspectral Foundation Models at Scale

Towards flexible perception with visual memory

Integrating Clinical Knowledge Graphs and Gradient-Based Neural Systems for Enhanced Melanoma Diagnosis via the 7-Point Checklist

LUMA: A Benchmark Dataset for Learning from Uncertain and Multimodal Data

Towards Black-Box Membership Inference Attack for Diffusion Models

Robo-Instruct: Simulator-Augmented Instruction Alignment For Finetuning Code LLMs

From Model Performance to Claim: How a Change of Focus in Machine Learning Replicability Can Help Bridge the Responsibility Gap

Learning to Defer in Congested Systems: The AI-Human Interplay

LEAVES: Learning Views for Time-Series Biobehavioral Data in Contrastive Learning

Game-Theoretic Multiagent Reinforcement Learning

SMA: Who Said That? Auditing Membership Leakage in Semi-Black-box RAG Controlling

Aryabhata: An exam-focused language model for JEE Math

Rethinking Domain-Specific LLM Benchmark Construction: A Comprehensiveness-Compactness Approach

Large Language Models Do Not Simulate Human Psychology

LLM Robustness Leaderboard v1 --Technical report

One Subgoal at a Time: Zero-Shot Generalization to Arbitrary Linear Temporal Logic Requirements in Multi-Task Reinforcement Learning

Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens

StepFun-Prover Preview: Let's Think and Verify Step by Step

MoSE: Skill-by-Skill Mixture-of-Experts Learning for Embodied Autonomous Machines

GraspClutter6D: A Large-scale Real-world Dataset for Robust Perception and Grasping in Cluttered Scenes

Created by

Haebom

저자

Seunghyeok Back, Joosoon Lee, Kangmin Kim, Heeseon Rho, Geonhyup Lee, Raeyoung Kang, Sangbeom Lee, Sangjun Noh, Youngjin Lee, Taeyeop Lee, Kyoobin Lee

개요

GraspClutter6D는 로봇의 복잡한 환경에서의 강력한 물체 파지 문제를 해결하기 위해 제작된 대규모 실제 세계 파지 데이터셋입니다. 기존 데이터셋의 단순한 장면과 부족한 다양성을 극복하고자, 1,000개의 고밀도(14.1개 물체/장면, 62.6% 가림) 복잡한 장면, 75가지 환경 구성(상자, 선반, 테이블)에서 200개의 물체를 다양한 각도로 촬영한 52,000개의 RGB-D 이미지, 736,000개의 6D 물체 자세 및 93억 개의 가능한 로봇 파지 정보를 포함합니다. 본 논문에서는 이 데이터셋을 이용하여 최첨단 분할, 물체 자세 추정, 파지 검출 방법들의 성능을 평가하고, GraspClutter6D로 학습된 파지 네트워크가 기존 데이터셋으로 학습된 네트워크보다 시뮬레이션 및 실제 실험 모두에서 뛰어난 성능을 보임을 보여줍니다. 데이터셋, 툴킷, 주석 도구는 공개적으로 이용 가능합니다.

시사점, 한계점

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시사점:

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기존 데이터셋의 한계를 극복하는 대규모 실제 세계 복잡한 환경 파지 데이터셋 제공.

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다양한 환경과 물체, 높은 가림 비율을 포함하여 현실적인 파지 문제 연구에 기여.

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GraspClutter6D를 이용한 학습이 기존 데이터셋보다 우수한 파지 성능을 보임을 실험적으로 증명.

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공개된 데이터셋, 툴킷, 주석 도구를 통해 연구의 재현성 및 확장성 증대.

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한계점:

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데이터셋의 규모에도 불구하고, 실제 세계의 모든 복잡한 환경을 완벽하게 반영하지 못할 수 있음.

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특정 유형의 물체나 환경에 편향되어 있을 가능성 존재.

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데이터 수집 과정에서 발생할 수 있는 노이즈나 오류에 대한 고려 필요.

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