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.

Merge-of-Thought Distillation

OTESGN: Optimal Transport-Enhanced Syntactic-Semantic Graph Networks for Aspect-Based Sentiment Analysis

MESH -- Understanding Videos Like Human: Measuring Hallucinations in Large Video Models

Adapting Vision-Language Models for Neutrino Event Classification in High-Energy Physics

Symmetry-Guided Multi-Agent Inverse Reinforcement Learning

AU-Harness: An Open-Source Toolkit for Holistic Evaluation of Audio LLMs

Expert-Guided Explainable Few-Shot Learning for Medical Image Diagnosis

Towards Generalized Routing: Model and Agent Orchestration for Adaptive and Efficient Inference

MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining

Demo: Healthcare Agent Orchestrator (HAO) for Patient Summarization in Molecular Tumor Boards

Focusing by Contrastive Attention: Enhancing VLMs' Visual Reasoning

Beyond the Pre-Service Horizon: Infusing In-Service Behavior for Improved Financial Risk Forecasting

On Synthesis of Timed Regular Expressions

TinyDef-DETR: A DETR-based Framework for Defect Detection in Transmission Lines from UAV Imagery

LiDAR-BIND-T: Improved and Temporally Consistent Sensor Modality Translation and Fusion for Robotic Applications

From Vision to Validation: A Theory- and Data-Driven Construction of a GCC-Specific AI Adoption Index

A Comprehensive Guide to Differential Privacy: From Theory to User Expectations

The Architecture of AI Transformation: Four Strategic Patterns and an Emerging Frontier

FLM-Audio: Natural Monologues Improves Native Full-Duplex Chatbots via Dual Training

Deep Learning-Based Rock Particulate Classification Using Attention-Enhanced ConvNeXt

The Information Dynamics of Generative Diffusion

Data-Augmented Few-Shot Neural Stencil Emulation for System Identification of Computer Models

Group Expectation Policy Optimization for Heterogeneous Reinforcement Learning

Pretrained Conformers for Audio Fingerprinting and Retrieval

Towards Scalable Training for Handwritten Mathematical Expression Recognition

To Theoretically Understand Transformer-Based In-Context Learning for Optimizing CSMA

Klear-CodeTest: Scalable Test Case Generation for Code Reinforcement Learning

HiD-VAE: Interpretable Generative Recommendation via Hierarchical and Disentangled Semantic IDs

MagicGUI: A Foundational Mobile GUI Agent with Scalable Data Pipeline and Reinforcement Fine-tuning

villa-X: Enhancing Latent Action Modeling in Vision-Language-Action Models

New Kid in the Classroom: Exploring Student Perceptions of AI Coding Assistants

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test?

Uncertainty-aware Diffusion and Reinforcement Learning for Joint Plane Localization and Anomaly Diagnosis in 3D Ultrasound

Uncertainty Estimation by Human Perception versus Neural Models

Persistent Homology of Topic Networks for the Prediction of Reader Curiosity

Task Matters: Knowledge Requirements Shape LLM Responses to Context-Memory Conflict

Crack Path Prediction with Operator Learning using Discrete Particle System data Generation

Diffusion Graph Neural Networks for Robustness in Olfaction Sensors and Datasets

MM-Prompt: Cross-Modal Prompt Tuning for Continual Visual Question Answering

An Ontology-Driven Graph RAG for Legal Norms: A Structural, Temporal, and Deterministic Approach

Combating Falsification of Speech Videos with Live Optical Signatures (Extended Version)

Early Exit and Multi Stage Knowledge Distillation in VLMs for Video Summarization

Critical Challenges and Guidelines in Evaluating Synthetic Tabular Data: A Systematic Review

Parasite: A Steganography-based Backdoor Attack Framework for Diffusion Models

Towards Adaptive Memory-Based Optimization for Enhanced Retrieval-Augmented Generation

Entropy-Gated Branching for Efficient Test-Time Reasoning

SWI: Speaking with Intent in Large Language Models

Byzantine-Robust Federated Learning Using Generative Adversarial Networks

VeriSafe Agent: Safeguarding Mobile GUI Agent via Logic-based Action Verification

MIND: Towards Immersive Psychological Healing with Multi-agent Inner Dialogue

V-HOP: Visuo-Haptic 6D Object Pose Tracking

EgoAgent: A Joint Predictive Agent Model in Egocentric Worlds

Knowledge-Guided Biomarker Identification for Label-Free Single-Cell RNA-Seq Data: A Reinforcement Learning Perspective

MERaLiON-SpeechEncoder: Towards a Speech Foundation Model for Singapore and Beyond

RED: Unleashing Token-Level Rewards from Holistic Feedback via Reward Redistribution

IDEATOR: Jailbreaking and Benchmarking Large Vision-Language Models Using Themselves

DeepVoting: Learning and Fine-Tuning Voting Rules with Canonical Embeddings

Rethinking Disentanglement under Dependent Factors of Variation

Discovering physical laws with parallel symbolic enumeration

Semantic Augmentation in Images using Language

Algorithmic Collusion by Large Language Models

A minimal coalition logic

Deep Reinforcement Learning for Inventory Networks: Toward Reliable Policy Optimization

Inconsistency Handling in Prioritized Databases with Universal Constraints: Complexity Analysis and Links with Active Integrity Constraints

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference

CogGuide: Human-Like Guidance for Zero-Shot Omni-Modal Reasoning

TreeGPT: Pure TreeFFN Encoder-Decoder Architecture for Structured Reasoning Without Attention Mechanisms

Robix: A Unified Model for Robot Interaction, Reasoning and Planning

KROMA: Ontology Matching with Knowledge Retrieval and Large Language Models

Scaling LLM Planning: NL2FLOW for Parametric Problem Generation and Rigorous Evaluation

Optimizing Length Compression in Large Reasoning Models

LLMs for sensory-motor control: Combining in-context and iterative learning

Effort-aware Fairness: Incorporating a Philosophy-informed, Human-centered Notion of Effort into Algorithmic Fairness Metrics

Simulating Human-like Daily Activities with Desire-driven Autonomy

Enhancing Few-Shot Transfer Learning with Optimized Multi-Task Prompt Tuning through Modular Prompt Composition

ButterflyQuant: Ultra-low-bit LLM Quantization through Learnable Orthogonal Butterfly Transforms

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models

SimpleVLA-RL: Scaling VLA Training via Reinforcement Learning

Feasibility-Guided Fair Adaptive Offline Reinforcement Learning for Medicaid Care Management

Retrieval-Augmented Generation for Reliable Interpretation of Radio Regulations

Explaining Concept Drift through the Evolution of Group Counterfactuals

LoCoBench: A Benchmark for Long-Context Large Language Models in Complex Software Engineering

Mechanistic Learning with Guided Diffusion Models to Predict Spatio-Temporal Brain Tumor Growth

Graph Alignment via Dual-Pass Spectral Encoding and Latent Space Communication

ObjectReact: Learning Object-Relative Control for Visual Navigation

Fluent but Unfeeling: The Emotional Blind Spots of Language Models

Invisible Attributes, Visible Biases: Exploring Demographic Shortcuts in MRI-based Alzheimer's Disease Classification

An improved educational competition optimizer with multi-covariance learning operators for global optimization problems

Improving Video Diffusion Transformer Training by Multi-Feature Fusion and Alignment from Self-Supervised Vision Encoders

A modified RIME algorithm with covariance learning and diversity enhancement for numerical optimization

Towards Explainable Job Title Matching: Leveraging Semantic Textual Relatedness and Knowledge Graphs

Explainable AI for Accelerated Microstructure Imaging: A SHAP-Guided Protocol on the Connectome 2.0 scanner

Incorporating AI Incident Reporting into Telecommunications Law and Policy: Insights from India

OpenFake: An Open Dataset and Platform Toward Large-Scale Deepfake Detection

Prompt Pirates Need a Map: Stealing Seeds helps Stealing Prompts

Resource-Efficient Glioma Segmentation on Sub-Saharan MRI

ENSI: Efficient Non-Interactive Secure Inference for Large Language Models

We're Still Doing It (All) Wrong: Recommender Systems, Fifteen Years Later

LLMs Don't Know Their Own Decision Boundaries: The Unreliability of Self-Generated Counterfactual Explanations

MetaLLMix : An XAI Aided LLM-Meta-learning Based Approach for Hyper-parameters Optimization

Diffusion Graph Neural Networks for Robustness in Olfaction Sensors and Datasets

Created by

Haebom

저자

Kordel K. France, Ovidiu Daescu

개요

본 논문은 복잡한 환경에서 작동하는 자율 시스템을 위한 핵심 기능인 로봇 후각 기반 냄새원 위치 추정(OSL)의 정확도 향상을 위한 새로운 머신러닝 방법을 제시합니다. 기존 OSL 방법들은 후각 데이터셋과 센서 해상도의 한계로 인해 로봇이 냄새를 잘못된 물체에 귀속시키는 모호성 문제를 겪습니다. 이를 해결하기 위해 확산 기반 분자 생성을 이용한 새로운 머신러닝 방법을 제안합니다. 이 방법은 기존 후각 데이터셋과 훈련 방법의 한계를 넘어 화학적 공간을 확장하여 이전에는 기록되지 않았던 잠재적인 냄새 분자를 식별할 수 있게 합니다. 생성된 분자는 고급 후각 센서를 사용하여 더 정확하게 검증될 수 있으며, 더 많은 화합물을 감지하고 더 나은 하드웨어 설계를 위한 정보를 제공합니다. 시각 분석, 언어 처리, 분자 생성을 통합하여 로봇의 후각-시각 모델이 냄새를 정확한 원천과 연결하는 능력을 향상시켜 폭발물 탐지, 마약 검사, 수색 및 구조와 같은 중요한 응용 분야에서 더 나은 센서 선택을 통한 내비게이션 및 의사 결정을 개선합니다. 제한된 후각 데이터와 센서 모호성으로 인한 문제에 대한 확장 가능한 솔루션을 제공하며, 인공 후각 분야의 기초적인 발전을 나타냅니다. 코드와 데이터는 공개적으로 제공됩니다.

시사점, 한계점

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시사점:

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확산 기반 분자 생성을 이용한 새로운 OSL 방법 제시

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기존 OSL 방법의 모호성 문제 해결에 기여

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제한된 후각 데이터셋의 한계 극복

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고급 후각 센서 설계 및 활용 개선

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폭발물 탐지, 마약 검사, 수색 및 구조 등 다양한 응용 분야에 적용 가능

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인공 후각 분야의 발전에 기여

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코드 및 데이터 공개를 통한 연구 확장 가능성 제시

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한계점:

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제안된 방법의 실제 환경 적용 및 성능 검증에 대한 추가 연구 필요

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고급 후각 센서의 가용성 및 비용 문제

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다양한 환경 조건에 대한 일반화 성능 평가 필요

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생성된 분자의 정확성 및 신뢰성에 대한 추가적인 검증 필요

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