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Daily Arxiv
Daily Arxiv
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。
このページはGoogle Geminiを活用して要約し、非営利で運営しています。
論文の著作権は著者および関連機関にあり、共有する際は出典を明記してください。
EgoVLA: Learning Vision-Language-Action Models from Egocentric Human Videos
Compositional Discrete Latent Code for High Fidelity, Productive Diffusion Models
MERA Code: A Unified Framework for Evaluating Code Generation Across Tasks
Site-Level Fine-Tuning with Progressive Layer Freezing: Towards Robust Prediction of Bronchopulmonary Dysplasia from Day-1 Chest Radiographs in Extremely Preterm Infants
A Roadmap for Climate-Relevant Robotics Research
Fairness Is Not Enough: Auditing Competence and Intersectional Bias in AI-powered Resume Screening
MMOne: Representing Multiple Modalities in One Scene
SWE-MERA: A Dynamic Benchmark for Agenticly Evaluating Large Language Models on Software Engineering Tasks
CodeAssistBench (CAB): Dataset & Benchmarking for Multi-turn Chat-Based Code Assistance
(Almost) Free Modality Stitching of Foundation Models
A Brain Tumor Segmentation Method Based on CLIP and 3D U-Net with Cross-Modal Semantic Guidance and Multi-Level Feature Fusion
KEN: Knowledge Augmentation and Emotion Guidance Network for Multimodal Fake News Detection
THOR: Transformer Heuristics for On-Demand Retrieval
SEALGuard: Safeguarding the Multilingual Conversations in Southeast Asian Languages for LLM Software Systems
KeyRe-ID: Keypoint-Guided Person Re-Identification using Part-Aware Representation in Videos
Prompt Perturbations Reveal Human-Like Biases in LLM Survey Responses
Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
Fast Bilateral Teleoperation and Imitation Learning Using Sensorless Force Control via Accurate Dynamics Model
Task-Specific Generative Dataset Distillation with Difficulty-Guided Sampling
VIDEE: Visual and Interactive Decomposition, Execution, and Evaluation of Text Analytics with Intelligent Agents
ReCode: Updating Code API Knowledge with Reinforcement Learning
Cross-Layer Discrete Concept Discovery for Interpreting Language Models
Semantic Structure-Aware Generative Attacks for Enhanced Adversarial Transferability
MEM1: Learning to Synergize Memory and Reasoning for Efficient Long-Horizon Agents
Multiple-Frequencies Population-Based Training
Critique-GRPO: Advancing LLM Reasoning with Natural Language and Numerical Feedback
Fine-Tune an SLM or Prompt an LLM? The Case of Generating Low-Code Workflows
ContextQFormer: A New Context Modeling Method for Multi-Turn Multi-Modal Conversations
GPU Performance Portability needs Autotuning
Generating Synthetic Data via Augmentations for Improved Facial Resemblance in DreamBooth and InstantID
Coral Protocol: Open Infrastructure Connecting The Internet of Agents
MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness
Federated Learning: A Survey on Privacy-Preserving Collaborative Intelligence
ConTextual: Improving Clinical Text Summarization in LLMs with Context-preserving Token Filtering and Knowledge Graphs
Task-Circuit Quantization: Leveraging Knowledge Localization and Interpretability for Compression
JailDAM: Jailbreak Detection with Adaptive Memory for Vision-Language Model
KP Quantum Neural Networks
VectorFit : Adaptive Singular & Bias Vector Fine-Tuning of Pre-trained Foundation Models
Data-Efficient Deep Operator Network for Unsteady Flow: A Multi-Fidelity Approach with Physics-Guided Subsampling
Learning Universal Human Mobility Patterns with a Foundation Model for Cross-domain Data Fusion
GeoFlow-SLAM: A Robust Tightly-Coupled RGBD-Inertial and Legged Odometry Fusion SLAM for Dynamic Legged Robotics
A Multi-Stage Framework with Taxonomy-Guided Reasoning for Occupation Classification Using Large Language Models
Multi-View Node Pruning for Accurate Graph Representation
V-Max: A Reinforcement Learning Framework for Autonomous Driving
Interpretable Transformation and Analysis of Timelines through Learning via Surprisability
AI Governance InternationaL Evaluation Index (AGILE Index) 2024
UPCORE: Utility-Preserving Coreset Selection for Balanced Unlearning
Improving Transformer World Models for Data-Efficient RL
LLM-RecG: A Semantic Bias-Aware Framework for Zero-Shot Sequential Recommendation
SIDDA: SInkhorn Dynamic Domain Adaptation for Image Classification with Equivariant Neural Networks
Determination of galaxy photometric redshifts using Conditional Generative Adversarial Networks (CGANs)
Speech-Forensics: Towards Comprehensive Synthetic Speech Dataset Establishment and Analysis
MRGen: Segmentation Data Engine for Underrepresented MRI Modalities
IOPO: Empowering LLMs with Complex Instruction Following via Input-Output Preference Optimization
Out-of-Distribution Recovery with Object-Centric Keypoint Inverse Policy for Visuomotor Imitation Learning
Dataset resulting from the user study on comprehensibility of explainable AI algorithms
Unified Triplet-Level Hallucination Evaluation for Large Vision-Language Models
LoRA Done RITE: Robust Invariant Transformation Equilibration for LoRA Optimization
Identifying Task Groupings for Multi-Task Learning Using Pointwise V-Usable Information
DeFine: Decision-Making with Analogical Reasoning over Factor Profiles
Benchmarking Sub-Genre Classification For Mainstage Dance Music
Risks of ignoring uncertainty propagation in AI-augmented security pipelines
MedPix 2.0: A Comprehensive Multimodal Biomedical Data set for Advanced AI Applications with Retrieval Augmented Generation and Knowledge Graphs
Leveraging Quantum Superposition to Infer the Dynamic Behavior of a Spatial-Temporal Neural Network Signaling Model
Bounding the Worst-class Error: A Boosting Approach
TBDetector:Transformer-Based Detector for Advanced Persistent Threats with Provenance Graph
Machine Learning Systems: A Survey from a Data-Oriented Perspective
Aime: Towards Fully-Autonomous Multi-Agent Framework
SmartThinker: Learning to Compress and Preserve Reasoning by Step-Level Length Control
Ready Jurist One: Benchmarking Language Agents for Legal Intelligence in Dynamic Environments
NTRL: Encounter Generation via Reinforcement Learning for Dynamic Difficulty Adjustment in Dungeons and Dragons
Judging with Many Minds: Do More Perspectives Mean Less Prejudice? On Bias Amplifications and Resistance in Multi-Agent Based LLM-as-Judge
ActionStudio: A Lightweight Framework for Data and Training of Large Action Models
BEARCUBS: A benchmark for computer-using web agents
Demystifying MuZero Planning: Interpreting the Learned Model
LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendations
VideoITG: Multimodal Video Understanding with Instructed Temporal Grounding
VisionThink: Smart and Efficient Vision Language Model via Reinforcement Learning
Imbalance in Balance: Online Concept Balancing in Generation Models
Latent Policy Steering with Embodiment-Agnostic Pretrained World Models
Vision-and-Language Training Helps Deploy Taxonomic Knowledge but Does Not Fundamentally Alter It
Revisiting Reliability in the Reasoning-based Pose Estimation Benchmark
AbGen: Evaluating Large Language Models in Ablation Study Design and Evaluation for Scientific Research
Towards Formal Verification of LLM-Generated Code from Natural Language Prompts
Evaluating Reinforcement Learning Algorithms for Navigation in Simulated Robotic Quadrupeds: A Comparative Study Inspired by Guide Dog Behaviour
Overview of the TalentCLEF 2025: Skill and Job Title Intelligence for Human Capital Management
QuestA: Expanding Reasoning Capacity in LLMs via Question Augmentation
Voxtral
Merge Kernel for Bayesian Optimization on Permutation Space
Efficient Adaptation of Pre-trained Vision Transformer underpinned by Approximately Orthogonal Fine-Tuning Strategy
Automating Steering for Safe Multimodal Large Language Models
HATS: Hindi Analogy Test Set for Evaluating Reasoning in Large Language Models
VITA: Vision-to-Action Flow Matching Policy
$S^2M^2$: Scalable Stereo Matching Model for Reliable Depth Estimation
Synthesizing Reality: Leveraging the Generative AI-Powered Platform Midjourney for Construction Worker Detection
Aligning Humans and Robots via Reinforcement Learning from Implicit Human Feedback
SHIELD: A Secure and Highly Enhanced Integrated Learning for Robust Deepfake Detection against Adversarial Attacks
Prompt Injection 2.0: Hybrid AI Threats
Orbis: Overcoming Challenges of Long-Horizon Prediction in Driving World Models
Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities
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NTRL: Encounter Generation via Reinforcement Learning for Dynamic Difficulty Adjustment in Dungeons and Dragons
Created by
Haebom
作者
Carlo Romeo, Andrew D. Bagdanov
概要
本論文では、ダンジョン＆ドラゴン（D&D）でダンジョンマスター（DM）の受動的なバランス調整作業を自動化する新しいアプローチである「強化学習によるジョー生成（NTRL）」を提案します。 NTRLは状況に応じたバンディット問題にフレーミングし、リアルタイムパーティーメンバー属性に基づいたジョーを生成します。従来のDMヒューリスティックと比較して戦闘持続時間増加(+200%)、パーティーメンバーに与えるダメージ増加、戦闘後の体力減少(-16.67%)、プレイヤー死者数増加(全パーティー全滅は低く維持)を通じて戦闘強度を高めます。高い勝率（70％）を維持しながら戦略的深度を向上させ、困難を増加させてゲームの公平性を維持し、人間DMが設計した遭遇より優れた性能を示します。
Takeaways、Limitations
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Takeaways：
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D&DでDMの負担を軽減する自動化された難易度調整システムを提供。
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戦闘継続時間の増加と戦略的な深さの向上により、より没入性の高いゲーム体験を提供します。
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強化学習ベースの自動化難易度調整システムの有効性検証
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人間DMに対する優れた性能による自動化システムの実用性の提示
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Limitations：
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NTRLのパフォーマンス評価が特定のD&Dシステムまたはゲームセッションに限定される可能性。
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さまざまなプレイヤータイプと戦闘スタイルを考慮した追加の研究が必要です。
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複雑なゲーム状況とルールを完全に反映する上で限界が存在する可能性。
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長期間のゲームプレイのためのNTRLの性能と安定性のための追加の検証が必要です。
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