Daily Arxiv

전 세계에서 발간되는 인공지능 관련 논문을 정리하는 페이지 입니다.
본 페이지는 Google Gemini를 활용해 요약 정리하며, 비영리로 운영 됩니다.
논문에 대한 저작권은 저자 및 해당 기관에 있으며, 공유 시 출처만 명기하면 됩니다.

Probing Experts' Perspectives on AI-Assisted Public Speaking Training

Learning Pole Structures of Hadronic States using Predictive Uncertainty Estimation

Neural Concept Verifier: Scaling Prover-Verifier Games via Concept Encodings

Hallucination Stations: On Some Basic Limitations of Transformer-Based Language Models

Objectomaly: Objectness-Aware Refinement for OoD Segmentation with Structural Consistency and Boundary Precision

KeyRe-ID: Keypoint-Guided Person Re-Identification using Part-Aware Representation in Videos

Squeeze the Soaked Sponge: Efficient Off-policy Reinforcement Finetuning for Large Language Model

The Dark Side of LLMs Agent-based Attacks for Complete Computer Takeover

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Hyperspectral Anomaly Detection Methods: A Survey and Comparative Study

EmissionNet: Air Quality Pollution Forecasting for Agriculture

The role of gain neuromodulation in layer-5 pyramidal neurons

USAD: End-to-End Human Activity Recognition via Diffusion Model with Spatiotemporal Attention

Hita: Holistic Tokenizer for Autoregressive Image Generation

Distributional Soft Actor-Critic with Diffusion Policy

Lighting the Night with Generative Artificial Intelligence

On the Necessity of Output Distribution Reweighting for Effective Class Unlearning

Upgrade or Switch: Do We Need a Next-Gen Trusted Architecture for the Internet of AI Agents?

Language-Grounded Hierarchical Planning and Execution with Multi-Robot 3D Scene Graphs

Grokking Beyond the Euclidean Norm of Model Parameters

An Outlook on the Opportunities and Challenges of Multi-Agent AI Systems

Deep Learning-Based Forecasting of Boarding Patient Counts to Address ED Overcrowding

An Exploration of Default Images in Text-to-Image Generation

Automatic Curriculum Learning for Driving Scenarios: Towards Robust and Efficient Reinforcement Learning

TPK: Trustworthy Trajectory Prediction Integrating Prior Knowledge For Interpretability and Kinematic Feasibility

Boundary-Guided Trajectory Prediction for Road Aware and Physically Feasible Autonomous Driving

Balancing Progress and Safety: A Novel Risk-Aware Objective for RL in Autonomous Driving

TS-SNN: Temporal Shift Module for Spiking Neural Networks

Red Teaming Large Language Models for Healthcare

One-Pass to Reason: Token Duplication and Block-Sparse Mask for Efficient Fine-Tuning on Multi-Turn Reasoning

AI Safety Should Prioritize the Future of Work

MGT: Extending Virtual Try-Off to Multi-Garment Scenarios

MedSegFactory: Text-Guided Generation of Medical Image-Mask Pairs

Generative Retrieval and Alignment Model: A New Paradigm for E-commerce Retrieval

Using AI to Summarize US Presidential Campaign TV Advertisement Videos, 1952-2012

REGEN: A Dataset and Benchmarks with Natural Language Critiques and Narratives

GTR: Guided Thought Reinforcement Prevents Thought Collapse in RL-based VLM Agent Training

Field Matching: an Electrostatic Paradigm to Generate and Transfer Data

SP$^2$T: Sparse Proxy Attention for Dual-stream Point Transformer

PIAD-SRNN: Physics-Informed Adaptive Decomposition in State-Space RNN

FonTS: Text Rendering with Typography and Style Controls

On the Principles of ReLU Networks with One Hidden Layer

Compositional Risk Minimization

End-to-end multi-channel speaker extraction and binaural speech synthesis

Post-hoc Study of Climate Microtargeting on Social Media Ads with LLMs: Thematic Insights and Fairness Evaluation

Downscaling Extreme Precipitation with Wasserstein Regularized Diffusion

Quantifying Context Bias in Domain Adaptation for Object Detection

An Empirical Study of Validating Synthetic Data for Formula Generation

Evaluating Implicit Bias in Large Language Models by Attacking From a Psychometric Perspective

HeSum: a Novel Dataset for Abstractive Text Summarization in Hebrew

SpecDec++: Boosting Speculative Decoding via Adaptive Candidate Lengths

GoalNet: Goal Areas Oriented Pedestrian Trajectory Prediction

Large Language Models in Mental Health Care: a Scoping Review

SDR-GAIN: A High Real-Time Occluded Pedestrian Pose Completion Method for Autonomous Driving

Temporal Motifs for Financial Networks: A Study on Mercari, JPMC, and Venmo Platforms

Measuring AI Alignment with Human Flourishing

StarDojo: Benchmarking Open-Ended Behaviors of Agentic Multimodal LLMs in Production-Living Simulations with Stardew Valley

Open Source Planning & Control System with Language Agents for Autonomous Scientific Discovery

Discovering Algorithms with Computational Language Processing

A Hybrid SMT-NRA Solver: Integrating 2D Cell-Jump-Based Local Search, MCSAT and OpenCAD

A taxonomy of epistemic injustice in the context of AI and the case for generative hermeneutical erasure

Bandit-Based Prompt Design Strategy Selection Improves Prompt Optimizers

Dualformer: Controllable Fast and Slow Thinking by Learning with Randomized Reasoning Traces

AI Delegates with a Dual Focus: Ensuring Privacy and Strategic Self-Disclosure

Text2BIM: Generating Building Models Using a Large Language Model-based Multi-Agent Framework

Interpreting systems as solving POMDPs: a step towards a formal understanding of agency

Lumos-1: On Autoregressive Video Generation from a Unified Model Perspective

NeuralOS: Towards Simulating Operating Systems via Neural Generative Models

KV Cache Steering for Inducing Reasoning in Small Language Models

Optimistic Exploration for Risk-Averse Constrained Reinforcement Learning

On Barriers to Archival Audio Processing

A Hybrid Multi-Well Hopfield-CNN with Feature Extraction and K-Means for MNIST Classification

Compress Any Segment Anything Model (SAM)

Penalizing Infeasible Actions and Reward Scaling in Reinforcement Learning with Offline Data

Geo-ORBIT: A Federated Digital Twin Framework for Scene-Adaptive Lane Geometry Detection

Adaptive Nonlinear Vector Autoregression: Robust Forecasting for Noisy Chaotic Time Series

Catastrophic Forgetting Mitigation Through Plateau Phase Activity Profiling

Dually Hierarchical Drift Adaptation for Online Configuration Performance Learning

Monitoring Risks in Test-Time Adaptation

Multilingual Multimodal Software Developer for Code Generation

KG-Attention: Knowledge Graph-Guided Attention at Test-Time via Bidirectional Information Aggregation

ONION: A Multi-Layered Framework for Participatory ER Design

A Personalised Formal Verification Framework for Monitoring Activities of Daily Living of Older Adults Living Independently in Their Homes

MoSAiC: Multi-Modal Multi-Label Supervision-Aware Contrastive Learning for Remote Sensing

KELPS: A Framework for Verified Multi-Language Autoformalization via Semantic-Syntactic Alignment

Safe Deep Reinforcement Learning for Resource Allocation with Peak Age of Information Violation Guarantees

DatasetAgent: A Novel Multi-Agent System for Auto-Constructing Datasets from Real-World Images

Scaling Attention to Very Long Sequences in Linear Time with Wavelet-Enhanced Random Spectral Attention (WERSA)

Normalized vs Diplomatic Annotation: A Case Study of Automatic Information Extraction from Handwritten Uruguayan Birth Certificates

Adaptive Framework for Ambient Intelligence in Rehabilitation Assistance

A comprehensive study of LLM-based argument classification: from LLAMA through GPT-4o to Deepseek-R1

Towards Collaborative Fairness in Federated Learning Under Imbalanced Covariate Shift

Generating Proto-Personas through Prompt Engineering: A Case Study on Efficiency, Effectiveness and Empathy

To Trade or Not to Trade: An Agentic Approach to Estimating Market Risk Improves Trading Decisions

A Multi-Modal Fusion Framework for Brain Tumor Segmentation Based on 3D Spatial-Language-Vision Integration and Bidirectional Interactive Attention Mechanism

FreeAudio: Training-Free Timing Planning for Controllable Long-Form Text-to-Audio Generation

RadiomicsRetrieval: A Customizable Framework for Medical Image Retrieval Using Radiomics Features

White-Basilisk: A Hybrid Model for Code Vulnerability Detection

MIDI-VALLE: Improving Expressive Piano Performance Synthesis Through Neural Codec Language Modelling

PromotionGo at SemEval-2025 Task 11: A Feature-Centric Framework for Cross-Lingual Multi-Emotion Detection in Short Texts

Position: The Future of Bayesian Prediction Is Prior-Fitted

Created by

Haebom

저자

Samuel Muller, Arik Reuter, Noah Hollmann, David Rugamer, Frank Hutter

개요

무작위로 생성된 인공 데이터셋으로 신경망을 훈련하면 데이터셋 생성 분포에 의해 정의된 사전 확률을 포착하는 베이지안 모델이 생성됩니다. 사전-데이터 적합 네트워크(PFNs)는 이러한 통찰력을 활용하도록 설계된 방법론의 한 종류입니다. 사전 훈련을 위한 컴퓨팅 자원이 급증하는 시대에 많은 응용 분야에서 새로운 실제 데이터 생성은 거의 정체되어 있는 상황에서 PFNs는 광범위한 응용 분야에서 더 중요한 역할을 할 것으로 예상됩니다. PFNs는 사전 훈련 컴퓨팅을 데이터 부족 시나리오에 효율적으로 할당할 수 있게 합니다. 원래는 소규모 베이지안 모델링 작업에 적용되었지만, PFNs 분야는 더 복잡한 도메인과 더 큰 데이터셋을 처리하도록 크게 확장되었습니다. 이 논문에서는 PFNs 및 기타 감가상각 추론 접근 방식이 감가상각 학습을 활용하여 데이터 부족 문제를 해결하는 베이지안 추론의 미래를 나타낸다고 주장합니다. 따라서 이는 유익한 연구 분야라고 생각합니다. 이 논문에서는 PFNs의 잠재력과 현재 한계를 해결하기 위한 방향을 탐구합니다.

시사점, 한계점

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시사점:

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PFNs는 데이터 부족 문제를 해결하는 효율적인 방법을 제공합니다.

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사전 훈련된 컴퓨팅 자원을 데이터 부족 시나리오에 효과적으로 할당할 수 있습니다.

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베이지안 추론의 미래를 위한 유망한 연구 분야입니다.

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복잡한 도메인과 대규모 데이터셋에 적용 가능성이 높습니다.

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한계점:

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현재 한계점 및 이를 해결하기 위한 방향에 대한 자세한 논의가 부족합니다.

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PFNs의 성능을 제한하는 요소에 대한 구체적인 분석이 필요합니다.

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