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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。
HoPE: Hyperbolic Rotary Positional Encoding for Stable Long-Range Dependency Modeling in Large Language Models
Comparative Analysis of Transformer Models in Disaster Tweet Classification for Public Safety
Emergent Social Dynamics of LLM Agents in the El Farol Bar Problem
The Good, the Bad and the Constructive: Automatically Measuring Peer Review's Utility for Authors
Energy Landscapes Enable Reliable Abstention in Retrieval-Augmented Large Language Models for Healthcare
DEXOP: A Device for Robotic Transfer of Dexterous Human Manipulation
Reinforcement Learning for Robust Ageing-Aware Control of Li-ion Battery Systems with Data-Driven Formal Verification
RepoDebug: Repository-Level Multi-Task and Multi-Language Debugging Evaluation of Large Language Models
Gravity Well Echo Chamber Modeling With An LLM-Based Confirmation Bias Model
Insights from Gradient Dynamics: Gradient Autoscaled Normalization
Efficient Virtuoso: A Latent Diffusion Transformer Model for Goal-Conditioned Trajectory Planning
MoSEs: Uncertainty-Aware AI-Generated Text Detection via Mixture of Stylistics Experts with Conditional Thresholds
DCPO: Dynamic Clipping Policy Optimization
DSDE: Dynamic Speculative Decoding with KLD Stability for Real-World Serving
Can AI be Auditable?
Robotic Fire Risk Detection based on Dynamic Knowledge Graph Reasoning: An LLM-Driven Approach with Graph Chain-of-Thought
Navigating the EU AI Act: Foreseeable Challenges in Qualifying Deep Learning-Based Automated Inspections of Class III Medical Devices
Complementary Learning System Empowers Online Continual Learning of Vehicle Motion Forecasting in Smart Cities
MultiPL-MoE: Multi-Programming-Lingual Extension of Large Language Models through Hybrid Mixture-of-Experts
QuadKAN: KAN-Enhanced Quadruped Motion Control via End-to-End Reinforcement Learning
MovieCORE: COgnitive REasoning in Movies
Automatic Prompt Optimization with Prompt Distillation
Membership Inference Attacks on LLM-based Recommender Systems
Leveraging Large Language Models for Accurate Sign Language Translation in Low-Resource Scenarios
Group Expectation Policy Optimization for Heterogeneous Reinforcement Learning
Convergence and Generalization of Anti-Regularization for Parametric Models
Jet-Nemotron: Efficient Language Model with Post Neural Architecture Search
CARFT: Boosting LLM Reasoning via Contrastive Learning with Annotated Chain-of-Thought-based Reinforced Fine-Tuning
Bridging Generalization and Personalization in Human Activity Recognition via On-Device Few-Shot Learning
FinAgentBench: A Benchmark Dataset for Agentic Retrieval in Financial Question Answering
Using Artificial Intuition in Distinct, Minimalist Classification of Scientific Abstracts for Management of Technology Portfolios
Semantic Discrepancy-aware Detector for Image Forgery Identification
Quantum-Efficient Reinforcement Learning Solutions for Last-Mile On-Demand Delivery
BadPromptFL: A Novel Backdoor Threat to Prompt-based Federated Learning in Multimodal Models
Uncertainty-Driven Reliability: Selective Prediction and Trustworthy Deployment in Modern Machine Learning
Real-Time Analysis of Unstructured Data with Machine Learning on Heterogeneous Architectures
VSI: Visual Subtitle Integration for Keyframe Selection to enhance Long Video Understanding
SGDFuse: SAM-Guided Diffusion for High-Fidelity Infrared and Visible Image Fusion
An Efficient Continuous-Time MILP for Integrated Aircraft Hangar Scheduling and Layout
DIRF: A Framework for Digital Identity Protection and Clone Governance in Agentic AI Systems
COLLAGE: Adaptive Fusion-based Retrieval for Augmented Policy Learning
Dynamically Adaptive Reasoning via LLM-Guided MCTS for Efficient and Context-Aware KGQA
Nested Graph Pseudo-Label Refinement for Noisy Label Domain Adaptation Learning
LanternNet: A Hub-and-Spoke System to Seek and Suppress Spotted Lanternfly Populations
RecPS: Privacy Risk Scoring for Recommender Systems
Supervised Fine Tuning on Curated Data is Reinforcement Learning (and can be improved)
Role-Playing LLM-Based Multi-Agent Support Framework for Detecting and Addressing Family Communication Bias
PLAME: Lightweight MSA Design Advances Protein Folding From Evolutionary Embeddings
Driver-Net: Multi-Camera Fusion for Assessing Driver Take-Over Readiness in Automated Vehicles
Leveraging Out-of-Distribution Unlabeled Images: Semi-Supervised Semantic Segmentation with an Open-Vocabulary Model
Visual Structures Helps Visual Reasoning: Addressing the Binding Problem in VLMs
Precise Bayesian Neural Networks
Transit for All: Mapping Equitable Bike2Subway Connection using Region Representation Learning
Scaling Intelligence: Designing Data Centers for Next-Gen Language Models
Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems
SAIL: Faster-than-Demonstration Execution of Imitation Learning Policies
Persona-driven Simulation of Voting Behavior in the European Parliament with Large Language Models
Bipedal Balance Control with Whole-body Musculoskeletal Standing and Falling Simulations
Scaling Laws of Motion Forecasting and Planning - Technical Report
Efficient $Q$-Learning and Actor-Critic Methods for Robust Average Reward Reinforcement Learning
Who Gets Credit or Blame? Attributing Accountability in Modern AI Systems
Unsupervised Evolutionary Cell Type Matching via Entropy-Minimized Optimal Transport
Multi-output Classification using a Cross-talk Architecture for Compound Fault Diagnosis of Motors in Partially Labeled Condition
SoloSpeech: Enhancing Intelligibility and Quality in Target Speech Extraction through a Cascaded Generative Pipeline
Steering LLM Reasoning Through Bias-Only Adaptation
MetaSTH-Sleep: Towards Effective Few-Shot Sleep Stage Classification for Health Management with Spatial-Temporal Hypergraph Enhanced Meta-Learning
InterFeat: A Pipeline for Finding Interesting Scientific Features
HumaniBench: A Human-Centric Framework for Large Multimodal Models Evaluation
Advancing Scientific Text Classification: Fine-Tuned Models with Dataset Expansion and Hard-Voting
Test It Before You Trust It: Applying Software Testing for Trustworthy In-context Learning
Action Flow Matching for Continual Robot Learning
Addressing Concept Mislabeling in Concept Bottleneck Models Through Preference Optimization
Nemotron-H: A Family of Accurate and Efficient Hybrid Mamba-Transformer Models
Byzantine-Robust Federated Learning Using Generative Adversarial Networks
Beyond SHAP and Anchors: A large-scale experiment on how developers struggle to design meaningful end-user explanations
VIPER: Visual Perception and Explainable Reasoning for Sequential Decision-Making
DistJoin: A Decoupled Join Cardinality Estimator based on Adaptive Neural Predicate Modulation
Low-Confidence Gold: Refining Low-Confidence Samples for Efficient Instruction Tuning
Assistance or Disruption? Exploring and Evaluating the Design and Trade-offs of Proactive AI Programming Support
Soft Token Attacks Cannot Reliably Audit Unlearning in Large Language Models
CHIRLA: Comprehensive High-resolution Identification and Re-identification for Large-scale Analysis
Kolmogorov-Arnold Fourier Networks
Position: LLMs Can be Good Tutors in English Education
Predicting Steady-State Behavior in Complex Networks with Graph Neural Networks
Separate Motion from Appearance: Customizing Motion via Customizing Text-to-Video Diffusion Models
Motion-enhanced Cardiac Anatomy Segmentation via an Insertable Temporal Attention Module
Bias in Decision-Making for AI's Ethical Dilemmas: A Comparative Study of ChatGPT and Claude
OmniThink: Expanding Knowledge Boundaries in Machine Writing through Thinking
DispFormer: A Pretrained Transformer Incorporating Physical Constraints for Dispersion Curve Inversion
Integrating Evidence into the Design of XAI and AI-based Decision Support Systems: A Means-End Framework for End-users in Construction
Revealing the impact of synthetic native samples and multi-tasking strategies in Hindi-English code-mixed humour and sarcasm detection
Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks
Sequential Controlled Langevin Diffusions
Privacy-Preserving Federated Learning via Homomorphic Adversarial Networks
CAREL: Instruction-guided reinforcement learning with cross-modal auxiliary objectives
Lessons from Studying Two-Hop Latent Reasoning
HierTOD: A Task-Oriented Dialogue System Driven by Hierarchical Goals
Flexible Coded Distributed Convolution Computing for Enhanced Straggler Resilience and Numerical Stability in Distributed CNNs
FACEGroup: Feasible and Actionable Counterfactual Explanations for Group Fairness
ETF: An Entity Tracing Framework for Hallucination Detection in Code Summaries
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COLLAGE: Adaptive Fusion-based Retrieval for Augmented Policy Learning
Created by
Haebom
作者
Sateesh Kumar, Shivin Dass, Georgios Pavlakos, Roberto Mart in-Mart in
概要
本論文では、少数ショットの模倣学習におけるデータ検索の問題について説明します。従来の方法では、視覚、意味、または動作空間で目標デモと最も類似したデモが最良のデモであると仮定して単一の特徴距離ヒューリスティックを使用してデータを取得しますが、これは関連情報の一部のみをキャプチャし、類似のシーンレイアウトによって関連性のないタスクのデータを取得したり、目標が異なるタスクから同様の動作を選択するなど有害なデモを導入することがあります。本論文では、多重手がかりの作業固有の組み合わせに基づいて、関連するデモの選択を導く適応型後期融合メカニズムを使用する少数ショット模倣学習における集合データ集約（COLLAGE）方法を提示します。 COLLAGEは、単一の特徴（外観、外観、言語の類似性など）を使用して、事前に選択されたデータセットのサブセットに重みを割り当て、各サブセットでトレーニングされたポリシーが目標デモでタスクを予測する方法に基づいて重みを指定します。その後、これらの重みを使用してポリシートレーニング中の重要度サンプリングを実行し、推定された関連性に基づいてデータをより緻密またはまれにサンプリングします。 COLLAGEは一般的であり、特徴に拘束されていないため、どの検索ヒューリスティックとして選択されたサブセットの数を組み合わせて、どのサブセットがターゲット操作に最大の利点を提供するかを識別できます。広範な実験では、COLLAGEは、シミュレーションで10種類の作業にわたって最先端の検索およびマルチタスク学習方法より5.1％向上し、大規模なDROIDデータセットで検索を行った6つの実際の作業で16.6％向上しました。
Takeaways、Limitations
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Takeaways：
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複数の特徴を活用した適応型後期融合メカニズムにより、少数ショット模倣学習におけるデータ検索性能を向上させた。
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単一の特徴ベースの既存の方法の限界を克服し、無関係なデータ選択による問題を解決しました。
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一般的で特徴に縛られない方法で、さまざまなデータセットやタスクに適用可能性を高めました。
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シミュレーションと実際の環境の両方で、従来の方法と比較してパフォーマンスの向上を実証しました。
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Limitations：
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適応型後期融合メカニズムの複雑さにより、計算コストが増加する可能性があります。
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使用される単一の特徴の選択は性能に影響を及ぼし得る。最適な特徴の組み合わせを見つけることは依然として課題です。
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大規模なデータセットでの効率に関する追加の研究が必要になる場合があります。
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