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世界中で発行される人工知能関連の論文をまとめるページです。
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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 3-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

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

Created by

Haebom

作者

モトキオムラ、カズキオタ、タカユキオサ、ユスケムクタ、タツヤハラダ

概要

この論文は、連続的な行動空間での強化学習のためのActor-critic方法に焦点を当てています。既存の連続行動空間強化学習アルゴリズムは、Bellman operatorを使用して現在のポリシーのQ値をモデル化しますが、最適値関数をモデル化しません。これはサンプル効率の低下につながる。この研究では、Bellman optimality operatorをactor-criticフレームワークに統合する効果を調べます。単純な環境での実験により,最適値モデリングは学習速度を高めるが,過大推定偏向を引き起こすことを示した。これに対処するために、Bellman optimality operatorからBellman operatorに徐々に切り替えるアニーリング技術を提案する。 TD3およびSACと組み合わせたこの方法は、さまざまな移動および操作作業における従来の方法よりも優れており、最適性関連のハイパーパラメータに対する堅牢性を示しています。コードはhttps://github.com/motokiomura/annealed-q-learningで確認できます。

GitHub - motokiomura/annealed-q-learning: [ICML 2025] Official code repository for "Gradual Transition from Bellman Optimality Operator to Bellman Operator in Online Reinforcement Learning"

[ICML 2025] Official code repository for "Gradual Transition from Bellman Optimality Operator to Bellman Operator in Online Reinforcement Learning" - motokiomura/annealed-q-learning

Takeaways、Limitations

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Takeaways： Bellman optimality operatorを利用したアニーリング技術は、連続行動空間強化学習のサンプル効率を向上させ、TD3やSACなどの既存のアルゴリズムのパフォーマンスを向上させることを示しています。最適性関連ハイパーパラメータの堅牢性の向上

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Limitations：単純な環境での実験結果に基づいて提案された方法の効果を検証したので、より複雑で多様な環境でのさらなる実験が必要です。 Bellman optimality operatorの使用による過大推定偏向問題をアニーリング技術で完全に解決したかどうかに関する追加の分析の必要性

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