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.

Defining and Quantifying Creative Behavior in Popular Image Generators

TS-SNN: Temporal Shift Module for Spiking Neural Networks

IntelliCardiac: An Intelligent Platform for Cardiac Image Segmentation and Classification

AI-Powered Agile Analog Circuit Design and Optimization

Demonstrating ViSafe: Vision-enabled Safety for High-speed Detect and Avoid

Motion-compensated cardiac MRI using low-rank diffeomorphic flow (DMoCo)

RADLADS: Rapid Attention Distillation to Linear Attention Decoders at Scale

T2S: High-resolution Time Series Generation with Text-to-Series Diffusion Models

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques

Humans can learn to detect AI-generated texts, or at least learn when they can't

Large Language Models Understanding: an Inherent Ambiguity Barrier

Data Therapist: Eliciting Domain Knowledge from Subject Matter Experts Using Large Language Models

SeriesBench: A Benchmark for Narrative-Driven Drama Series Understanding

Nexus-Gen: A Unified Model for Image Understanding, Generation, and Editing

PINN-MEP: Continuous Neural Representations for Minimum-Energy Path Discovery in Molecular Systems

Building Trustworthy Multimodal AI: A Review of Fairness, Transparency, and Ethics in Vision-Language Tasks

GeoUni: A Unified Model for Generating Geometry Diagrams, Problems and Problem Solutions

A highly maneuverable flying squirrel drone with agility-improving foldable wings

VL-Rethinker: Incentivizing Self-Reflection of Vision-Language Models with Reinforcement Learning

FindAnything: Open-Vocabulary and Object-Centric Mapping for Robot Exploration in Any Environment

Perils of Label Indeterminacy: A Case Study on Prediction of Neurological Recovery After Cardiac Arrest

CodeIF-Bench: Evaluating Instruction-Following Capabilities of Large Language Models in Interactive Code Generation

Novel Deep Neural OFDM Receiver Architectures for LLR Estimation

NaFM: Pre-training a Foundation Model for Small-Molecule Natural Products

Benchmarking Open-Source Large Language Models on Healthcare Text Classification Tasks

Atyaephyra at SemEval-2025 Task 4: Low-Rank Negative Preference Optimization

Integrating AI for Human-Centric Breast Cancer Diagnostics: A Multi-Scale and Multi-View Swin Transformer Framework

Negotiative Alignment: Embracing Disagreement to Achieve Fairer Outcomes -- Insights from Urban Studies

Large Language Models for Outpatient Referral: Problem Definition, Benchmarking and Challenges

Semantic Shift Estimation via Dual-Projection and Classifier Reconstruction for Exemplar-Free Class-Incremental Learning

LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces

Faster, Cheaper, Better: Multi-Objective Hyperparameter Optimization for LLM and RAG Systems

FLARE: A Framework for Stellar Flare Forecasting using Stellar Physical Properties and Historical Records

TLOB: A Novel Transformer Model with Dual Attention for Price Trend Prediction with Limit Order Book Data

Correcting Noisy Multilabel Predictions: Modeling Label Noise through Latent Space Shifts

Safety Evaluation of DeepSeek Models in Chinese Contexts

DejAIvu: Identifying and Explaining AI Art on the Web in Real-Time with Saliency Maps

Texture Image Synthesis Using Spatial GAN Based on Vision Transformers

Toward Task Generalization via Memory Augmentation in Meta-Reinforcement Learning

The Right to AI

Communicating Activations Between Language Model Agents

Guaranteed Recovery of Unambiguous Clusters

ValuesRAG: Enhancing Cultural Alignment Through Retrieval-Augmented Contextual Learning

Vision Transformers for Efficient Indoor Pathloss Radio Map Prediction

Quantifying Risk Propensities of Large Language Models: Ethical Focus and Bias Detection through Role-Play

E2E-AFG: An End-to-End Model with Adaptive Filtering for Retrieval-Augmented Generation

Jailbreaking and Mitigation of Vulnerabilities in Large Language Models

WorldCuisines: A Massive-Scale Benchmark for Multilingual and Multicultural Visual Question Answering on Global Cuisines

CATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency Patching

Learning to Compare Hardware Designs for High-Level Synthesis

To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning

Automated detection of underdiagnosed medical conditions via opportunistic imaging

Exploring the Trade-Offs: Quantization Methods, Task Difficulty, and Model Size in Large Language Models From Edge to Giant

On Synthetic Texture Datasets: Challenges, Creation, and Curation

Lexicon3D: Probing Visual Foundation Models for Complex 3D Scene Understanding

XG-NID: Dual-Modality Network Intrusion Detection using a Heterogeneous Graph Neural Network and Large Language Model

Recite, Reconstruct, Recollect: Memorization in LMs as a Multifaceted Phenomenon

Enhancing Differential Testing With LLMs For Testing Deep Learning Libraries

HORAE: A Domain-Agnostic Language for Automated Service Regulation

DEGAP: Dual Event-Guided Adaptive Prefixes for Templated-Based Event Argument Extraction with Slot Querying

Analyzing Consumer IoT Traffic from Security and Privacy Perspectives: a Comprehensive Survey

DyCE: Dynamically Configurable Exiting for Deep Learning Compression and Real-time Scaling

The Inadequacy of Similarity-based Privacy Metrics: Privacy Attacks against "Truly Anonymous" Synthetic Datasets

Connecting NTK and NNGP: A Unified Theoretical Framework for Wide Neural Network Learning Dynamics

An automated end-to-end deep learning-based framework for lung cancer diagnosis by detecting and classifying the lung nodules

Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions

Transformer-based assignment decision network for multiple object tracking

An alignment safety case sketch based on debate

The Power of Stories: Narrative Priming Shapes How LLM Agents Collaborate and Compete

A Survey of Slow Thinking-based Reasoning LLMs using Reinforced Learning and Inference-time Scaling Law

Agentic Neurodivergence as a Contingent Solution to the AI Alignment Problem

Theoretical Foundations for Semantic Cognition in Artificial Intelligence

Approximate Lifted Model Construction

MultiMind: Enhancing Werewolf Agents with Multimodal Reasoning and Theory of Mind

Advancing Embodied Agent Security: From Safety Benchmarks to Input Moderation

Do We Truly Need So Many Samples? Multi-LLM Repeated Sampling Efficiently Scales Test-Time Compute

Recursive Inference Scaling: A Winning Path to Scalable Inference in Language and Multimodal Systems

Generating Symbolic World Models via Test-time Scaling of Large Language Models

Imagining and building wise machines: The centrality of AI metacognition

Public Perceptions of Fairness Metrics Across Borders

Combating Confirmation Bias: A Unified Pseudo-Labeling Framework for Entity Alignment

Flow-GRPO: Training Flow Matching Models via Online RL

StreamBridge: Turning Your Offline Video Large Language Model into a Proactive Streaming Assistant

ComPO: Preference Alignment via Comparison Oracles

TransProQA: an LLM-based literary Translation evaluation metric with Professional Question Answering

TokLIP: Marry Visual Tokens to CLIP for Multimodal Comprehension and Generation

Reasoning Models Don't Always Say What They Think

Crosslingual Reasoning through Test-Time Scaling

CART-ELC: Oblique Decision Tree Induction via Exhaustive Search

Threshold Modulation for Online Test-Time Adaptation of Spiking Neural Networks

Time of the Flight of the Gaussians: Optimizing Depth Indirectly in Dynamic Radiance Fields

High-fidelity Grain Growth Modeling: Leveraging Deep Learning for Fast Computations

Feature-Augmented Deep Networks for Multiscale Building Segmentation in High-Resolution UAV and Satellite Imagery

Mapping User Trust in Vision Language Models: Research Landscape, Challenges, and Prospects

Scalable Chain of Thoughts via Elastic Reasoning

Benchmarking Ophthalmology Foundation Models for Clinically Significant Age Macular Degeneration Detection

PlaceIt3D: Language-Guided Object Placement in Real 3D Scenes

Software Development Life Cycle Perspective: A Survey of Benchmarks for CodeLLMs and Agents

T-T: Table Transformer for Tagging-based Aspect Sentiment Triplet Extraction

Enhancing Cooperative Multi-Agent Reinforcement Learning with State Modelling and Adversarial Exploration

FindAnything: Open-Vocabulary and Object-Centric Mapping for Robot Exploration in Any Environment

Created by

Haebom

저자

Sebastian Barbas Laina, Simon Boche, Sotiris Papatheodorou, Simon Schaefer, Jaehyung Jung, Stefan Leutenegger

개요

FindAnything는 시각-언어 정보를 밀집 체적 부분 지도에 통합하는 개방형 세계 매핑 및 탐색 프레임워크입니다. 기존의 순수 기하학적 정보와 개방형 어휘 의미 정보 간의 간극을 메워 높은 수준의 이해를 제공하며, 외부 기준 자세 정보 없이도 모든 환경을 탐색할 수 있도록 합니다. 환경을 일련의 체적 점유 부분 지도로 나타내어, 기반 SLAM 시스템이 드리프트를 수정할 때 자세 업데이트 시 변형되는 강력하고 정확한 지도 표현을 생성합니다. 효율적인 SAM(eSAM)에서 생성된 세그먼트에서 픽셀 단위 시각-언어 특징을 집계하여 개체 중심 체적 부분 지도에 통합함으로써, 개방형 어휘 쿼리에서 3D 기하학으로 매핑하고 메모리 사용 측면에서도 확장성을 제공합니다. Replica 데이터 세트에서 폐쇄형 평가에서 최첨단 의미 정확도를 달성하며, 자연어 쿼리를 통해 선택된 관심 객체 또는 영역을 기반으로 로봇이 환경을 탐색할 수 있도록 합니다. MAV와 같은 자원 제약이 있는 장치에 배포된 최초의 시스템으로, 실제 로봇 작업에 시각-언어 정보를 활용합니다.

시사점, 한계점

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시사점:

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기존의 기하학적 정보와 개방형 어휘 의미 정보를 결합하여 높은 수준의 환경 이해를 제공합니다.

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외부 기준 자세 정보 없이도 개방형 세계 환경 탐색이 가능합니다.

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자원 제약이 있는 장치(예: MAV)에서도 배포 가능합니다.

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Replica 데이터 세트에서 최첨단 의미 정확도를 달성했습니다.

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자연어 쿼리를 기반으로 한 객체 또는 영역 중심의 환경 탐색이 가능합니다.

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한계점:

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현재는 Replica 데이터셋을 중심으로 평가되었으므로, 다양한 실제 환경에서의 성능 검증이 추가적으로 필요합니다.

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시스템의 실시간 성능 및 확장성에 대한 더 자세한 분석이 필요합니다.

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eSAM에 대한 의존성이 존재하며, eSAM의 성능에 영향을 받을 수 있습니다.

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장기간의 로봇 작동에 대한 내구성 및 안정성에 대한 추가적인 연구가 필요합니다.

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