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.

Dually Hierarchical Drift Adaptation for Online Configuration Performance Learning

Token-based Audio Inpainting via Discrete Diffusion

Mechanistic Indicators of Understanding in Large Language Models

PyVision: Agentic Vision with Dynamic Tooling

Low Resource Reconstruction Attacks Through Benign Prompts

DTECT: Dynamic Topic Explorer & Context Tracker

Your Absorbing Discrete Diffusion Secretly Models the Bayesian Posterior

Bias-Aware Mislabeling Detection via Decoupled Confident Learning

Bridging the Last Mile of Prediction: Enhancing Time Series Forecasting with Conditional Guided Flow Matching

Planted in Pretraining, Swayed by Finetuning: A Case Study on the Origins of Cognitive Biases in LLMs

FlexOlmo: Open Language Models for Flexible Data Use

Democratizing High-Fidelity Co-Speech Gesture Video Generation

GR-LLMs: Recent Advances in Generative Recommendation Based on Large Language Models

LIRA: Inferring Segmentation in Large Multi-modal Models with Local Interleaved Region Assistance

BayesSDF: Surface-Based Laplacian Uncertainty Estimation for 3D Geometry with Neural Signed Distance Fields

Fast Bilateral Teleoperation and Imitation Learning Using Sensorless Force Control via Accurate Dynamics Model

DESIGN: Encrypted GNN Inference via Server-Side Input Graph Pruning

Beyond classical and contemporary models: a transformative AI framework for student dropout prediction in distance learning using RAG, Prompt engineering, and Cross-modal fusion

Hear-Your-Click: Interactive Object-Specific Video-to-Audio Generation

PRIME: Large Language Model Personalization with Cognitive Memory and Thought Processes

LearnLens: LLM-Enabled Personalised, Curriculum-Grounded Feedback with Educators in the Loop

Zero-Shot Cyclic Peptide Design via Composable Geometric Constraints

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

SymbolicThought: Integrating Language Models and Symbolic Reasoning for Consistent and Interpretable Human Relationship Understanding

From Video to EEG: Adapting Joint Embedding Predictive Architecture to Uncover Visual Concepts in Brain Signal Analysis

An Efficient Deep Learning Framework for Brain Stroke Diagnosis Using Computed Tomography (CT) Images

De-Fake: Style based Anomaly Deepfake Detection

ForgeHLS: A Large-Scale, Open-Source Dataset for High-Level Synthesis

Expert-level validation of AI-generated medical text with scalable language models

DriveMRP: Enhancing Vision-Language Models with Synthetic Motion Data for Motion Risk Prediction

FlowSpec: Continuous Pipelined Speculative Decoding for Efficient Distributed LLM Inference

DeltaSHAP: Explaining Prediction Evolutions in Online Patient Monitoring with Shapley Values

Following the Clues: Experiments on Person Re-ID using Cross-Modal Intelligence

Description of the Training Process of Neural Networks via Ergodic Theorem : Ghost nodes

Quality over Quantity: An Effective Large-Scale Data Reduction Strategy Based on Pointwise V-Information

Imagine for Me: Creative Conceptual Blending of Real Images and Text via Blended Attention

When Small Guides Large: Cross-Model Co-Learning for Test-Time Adaptation

Perspective Dial: Measuring Perspective of Text and Guiding LLM Outputs

CRISP-SAM2: SAM2 with Cross-Modal Interaction and Semantic Prompting for Multi-Organ Segmentation

Positioning AI Tools to Support Online Harm Reduction Practice: Applications and Design Directions

Interpretable Time Series Autoregression for Periodicity Quantification

Teaching Models to Verbalize Reward Hacking in Chain-of-Thought Reasoning

Exploration Behavior of Untrained Policies

Ark: An Open-source Python-based Framework for Robot Learning

Hyperspherical Variational Autoencoders Using Efficient Spherical Cauchy Distribution

TReB: A Comprehensive Benchmark for Evaluating Table Reasoning Capabilities of Large Language Models

The Debugging Decay Index: Rethinking Debugging Strategies for Code LLMs

Towards Effective Complementary Security Analysis using Large Language Models

Vision-Guided Chunking Is All You Need: Enhancing RAG with Multimodal Document Understanding

Learning from M-Tuple Dominant Positive and Unlabeled Data

BreastDCEDL: A Comprehensive Breast Cancer DCE-MRI Dataset and Transformer Implementation for Treatment Response Prediction

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation

SLED: A Speculative LLM Decoding Framework for Efficient Edge Serving

BIS Reasoning 1.0: The First Large-Scale Japanese Benchmark for Belief-Inconsistent Syllogistic Reasoning

FuseUNet: A Multi-Scale Feature Fusion Method for U-like Networks

Subgroups Matter for Robust Bias Mitigation

Robust Stability Analysis of Positive Lure System with Neural Network Feedback

Next-token pretraining implies in-context learning

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents

Learning Flexible Forward Trajectories for Masked Molecular Diffusion

LEXam: Benchmarking Legal Reasoning on 340 Law Exams

Large Language Model Psychometrics: A Systematic Review of Evaluation, Validation, and Enhancement

Quantum Observers: A NISQ Hardware Demonstration of Chaotic State Prediction Using Quantum Echo-state Networks

Visual Test-time Scaling for GUI Agent Grounding

Consistency in Language Models: Current Landscape, Challenges, and Future Directions

Avoiding Leakage Poisoning: Concept Interventions Under Distribution Shifts

Roll the dice & look before you leap: Going beyond the creative limits of next-token prediction

Adaptive Non-local Observable on Quantum Neural Networks

Leveraging Large Language Models for Multi-Class and Multi-Label Detection of Drug Use and Overdose Symptoms on Social Media

Bypassing LLM Guardrails: An Empirical Analysis of Evasion Attacks against Prompt Injection and Jailbreak Detection Systems

Divergence of Empirical Neural Tangent Kernel in Classification Problems

Rethinking the Foundations for Continual Reinforcement Learning

Predictive Modeling: BIM Command Recommendation Based on Large-scale Usage Logs

Unfair Learning: GenAI Exceptionalism and Copyright Law

OS-Kairos: Adaptive Interaction for MLLM-Powered GUI Agents

LLMs' Leaning in European Elections

COVID-19 Pneumonia Diagnosis Using Medical Images: Deep Learning-Based Transfer Learning Approach

Can A Society of Generative Agents Simulate Human Behavior and Inform Public Health Policy? A Case Study on Vaccine Hesitancy

Learning-Order Autoregressive Models with Application to Molecular Graph Generation

KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for Coding

Class-Aware PillarMix: Can Mixed Sample Data Augmentation Enhance 3D Object Detection with Radar Point Clouds?

No, of Course I Can! Deeper Fine-Tuning Attacks That Bypass Token-Level Safety Mechanisms

Disambiguate First, Parse Later: Generating Interpretations for Ambiguity Resolution in Semantic Parsing

IPAD: Inverse Prompt for AI Detection -- A Robust and Explainable LLM-Generated Text Detector

B-cos LM: Efficiently Transforming Pre-trained Language Models for Improved Explainability

Brain Latent Progression: Individual-based Spatiotemporal Disease Progression on 3D Brain MRIs via Latent Diffusion

Integrating Generative Artificial Intelligence in ADRD: A Roadmap for Streamlining Diagnosis and Care in Neurodegenerative Diseases

Logits are All We Need to Adapt Closed Models

Oracular Programming: A Modular Foundation for Building LLM-Enabled Software

Learning Traffic Anomalies from Generative Models on Real-Time Observations

BiDepth: A Bidirectional-Depth Neural Network for Spatio-Temporal Prediction

Not all tokens are created equal: Perplexity Attention Weighted Networks for AI generated text detection

Prune 'n Predict: Optimizing LLM Decision-making with Conformal Prediction

Relation-aware Hierarchical Prompt for Open-vocabulary Scene Graph Generation

An Interoperable Machine Learning Pipeline for Pediatric Obesity Risk Estimation

BEExformer: A Fast Inferencing Binarized Transformer with Early Exits

VIVID-10M: A Dataset and Baseline for Versatile and Interactive Video Local Editing

Tiny-Align: Bridging Automatic Speech Recognition and Large Language Model on the Edge

DuSEGO: Dual Second-order Equivariant Graph Ordinary Differential Equation

Knowledge-Augmented Multimodal Clinical Rationale Generation for Disease Diagnosis with Small Language Models

Object-Centric Representations Improve Policy Generalization in Robot Manipulation

Created by

Haebom

저자

Alexandre Chapin (imagine), Bruno Machado (imagine), Emmanuel Dellandrea (imagine), Liming Chen (imagine)

개요

본 논문은 로봇 조작 정책의 학습 및 일반화 능력에 중추적인 역할을 하는 시각적 표현에 대해 연구합니다. 기존 방법들이 전역적 또는 밀집된 특징에 의존하는 반면, 이러한 표현은 종종 작업 관련 및 무관한 장면 정보를 뒤섞어 분포 변화 시 강건성이 제한됩니다. 본 연구는 시각적 입력을 완성된 개체 집합으로 분할하는 구조화된 대안으로 개체 중심 표현(OCR)을 조사하여 조작 작업과 더 자연스럽게 정렬되는 귀납적 편향을 도입합니다. 단순한 것부터 복잡한 것까지 다양한 시뮬레이션 및 실제 조작 작업에서 다양한 시각적 인코더(개체 중심, 전역 및 밀집 방법)를 벤치마킹하고, 조명, 질감 및 방해 요소의 변화를 포함한 다양한 시각적 조건에서 일반화 능력을 평가합니다. 연구 결과, OCR 기반 정책은 작업 특정 사전 훈련 없이도 일반화 설정에서 밀집 및 전역 표현보다 우수한 성능을 보입니다. 이러한 통찰력은 OCR이 동적이고 실제 로봇 환경에서 효과적으로 일반화되는 시각 시스템을 설계하기 위한 유망한 방향임을 시사합니다.

시사점, 한계점

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시사점:

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개체 중심 표현(OCR)이 로봇 조작 정책의 일반화 능력을 향상시킨다는 것을 실험적으로 입증.

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작업 특정 사전 훈련 없이도 OCR 기반 정책이 밀집 및 전역 표현보다 우수한 일반화 성능을 보임.

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OCR은 동적이고 실제 로봇 환경에서 효과적으로 일반화되는 시각 시스템을 설계하기 위한 유망한 접근법임을 제시.

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한계점:

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본 연구는 시뮬레이션 및 제한된 실제 환경에서만 평가되었으므로, 더욱 다양하고 복잡한 실제 환경에서의 일반화 성능은 추가 연구가 필요함.

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OCR의 효율성은 사용된 시각적 인코더 및 작업의 복잡성에 따라 달라질 수 있음. 다양한 인코더와 작업에 대한 더욱 포괄적인 연구가 필요함.

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개체 분할의 정확성이 OCR 성능에 직접적인 영향을 미치므로, 개체 분할 알고리즘의 개선이 필요할 수 있음.

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