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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。
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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。

Dense Video Understanding with Gated Residual Tokenization

Machines are more productive than humans until they aren't, and vice versa

BWCache: Accelerating Video Diffusion Transformers through Block-Wise Caching

Exploring Data and Parameter Efficient Strategies for Arabic Dialect Identifications

The threat of analytic flexibility in using large language models to simulate human data: A call to attention

Evaluating undergraduate mathematics examinations in the era of generative AI: a curriculum-level case study

A Graph-Based Approach to Alert Contextualisation in Security Operations Centres

FunAudio-ASR Technical Report

Omni-CLST: Error-aware Curriculum Learning with guided Selective chain-of-Thought for audio question answering

Do Code Semantics Help? A Comprehensive Study on Execution Trace-Based Information for Code Large Language Models

Pluralistic Alignment for Healthcare: A Role-Driven Framework

ALIGNS: Unlocking nomological networks in psychological measurement through a large language model

A Survey of Reinforcement Learning for Large Reasoning Models

Skeleton-based sign language recognition using a dual-stream spatio-temporal dynamic graph convolutional network

Reconstruction Alignment Improves Unified Multimodal Models

Moment- and Power-Spectrum-Based Gaussianity Regularization for Text-to-Image Models

FASL-Seg: Anatomy and Tool Segmentation of Surgical Scenes

Dual-Mode Deep Anomaly Detection for Medical Manufacturing: Structural Similarity and Feature Distance

Exploit Tool Invocation Prompt for Tool Behavior Hijacking in LLM-Based Agentic System

Measuring the Measures: Discriminative Capacity of Representational Similarity Metrics Across Model Families

AR-KAN: Autoregressive-Weight-Enhanced Kolmogorov-Arnold Network for Time Series Forecasting

Ensemble of Pathology Foundation Models for MIDOG 2025 Track 2: Atypical Mitosis Classification

Deep Learning-Driven Multimodal Detection and Movement Analysis of Objects in Culinary

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning

MovieCORE: COgnitive REasoning in Movies

ASE: A Repository-Level Benchmark for Evaluating Security in AI-Generated Code

Generalized invariants meet constitutive neural networks: A novel framework for hyperelastic materials

Neural Logic Networks for Interpretable Classification

Roll Your Eyes: Gaze Redirection via Explicit 3D Eyeball Rotation

Controllable Surface Diffusion Generative Model for Neurodevelopmental Trajectories

Deciding how to respond: A deliberative framework to guide policymaker responses to AI systems

SCORPION: Addressing Scanner-Induced Variability in Histopathology

ThinkAct: Vision-Language-Action Reasoning via Reinforced Visual Latent Planning

SPICE: An Automated SWE-Bench Labeling Pipeline for Issue Clarity, Test Coverage, and Effort Estimation

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

EnCoBo: Energy-Guided Concept Bottlenecks for Interpretable Generation

T-SYNTH: A Knowledge-Based Dataset of Synthetic Breast Images

MedVAL: Toward Expert-Level Medical Text Validation with Language Models

Survivability of Backdoor Attacks on Unconstrained Face Recognition Systems

"What's Up, Doc?": Analyzing How Users Seek Health Information in Large-Scale Conversational AI Datasets

Engineering RAG Systems for Real-World Applications: Design, Development, and Evaluation

An Explainable AI Framework for Dynamic Resource Management in Vehicular Network Slicing

DiCoRe: Enhancing Zero-shot Event Detection via Divergent-Convergent LLM Reasoning

Semantic Exploration and Dense Mapping of Complex Environments using Ground Robot with Panoramic LiDAR-Camera Fusion

Evaluating Supervised Learning Models for Fraud Detection: A Comparative Study of Classical and Deep Architectures on Imbalanced Transaction Data

Binarized Neural Networks Converge Toward Algorithmic Simplicity: Empirical Support for the Learning-as-Compression Hypothesis

PMPO: Probabilistic Metric Prompt Optimization for Small and Large Language Models

DisastIR: A Comprehensive Information Retrieval Benchmark for Disaster Management

Preference Isolation Forest for Structure-based Anomaly Detection

Trustless Autonomy: Understanding Motivations, Benefits, and Governance Dilemmas in Self-Sovereign Decentralized AI Agents

GRADA: Graph-based Reranking against Adversarial Documents Attack

Modular Machine Learning: An Indispensable Path towards New-Generation Large Language Models

Direct Video-Based Spatiotemporal Deep Learning for Cattle Lameness Detection

Read Before You Think: Mitigating LLM Comprehension Failures with Step-by-Step Reading

Zero-Shot LLMs in Human-in-the-Loop RL: Replacing Human Feedback for Reward Shaping

Predicting Multi-Agent Specialization via Task Parallelizability

Fine-tuning Vision Language Models with Graph-based Knowledge for Explainable Medical Image Analysis

VLM-E2E: Enhancing End-to-End Autonomous Driving with Multimodal Driver Attention Fusion

METAL: A Multi-Agent Framework for Chart Generation with Test-Time Scaling

SNaRe: Domain-aware Data Generation for Low-Resource Event Detection

Superpose Task-specific Features for Model Merging

Examining False Positives under Inference Scaling for Mathematical Reasoning

SWAT: Sliding Window Adversarial Training for Gradual Domain Adaptation

Advanced Physics-Informed Neural Network with Residuals for Solving Complex Integral Equations

Retrieval-Retro: Retrieval-based Inorganic Retrosynthesis with Expert Knowledge

Unlocking Legal Knowledge: A Multilingual Dataset for Judicial Summarization in Switzerland

Reconstruction of Differentially Private Text Sanitization via Large Language Models

3DS: Medical Domain Adaptation of LLMs via Decomposed Difficulty-based Data Selection

The Role of Graph Topology in the Performance of Biomedical Knowledge Graph Completion Models

Top K Enhanced Reinforcement Learning Attacks on Heterogeneous Graph Node Classification

Learn while Unlearn: An Iterative Unlearning Framework for Generative Language Models

EXPLOR: Extrapolatory Pseudo-Label Matching for Out-of-distribution Uncertainty Based Rejection

Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter

Rule-Based Error Detection and Correction to Operationalize Movement Trajectory Classification

Heterogeneous Directed Hypergraph Neural Network over Abtract syntax tree (AST) for Code Classification

The Art of Saying "Maybe": A Conformal Lens for Uncertainty Benchmarking in VLMs

Human + AI for Accelerating Ad Localization Evaluation

Statistical Methods in Generative AI

InMind: Evaluating LLMs in Capturing and Applying Individual Human Reasoning Styles

DSperse: A Framework for Targeted Verification in Zero-Knowledge Machine Learning

DualSG: A Dual-Stream Explicit Semantic-Guided Multivariate Time Series Forecasting Framework

Judging with Many Minds: Do More Perspectives Mean Less Prejudice? On Bias Amplifications and Resistance in Multi-Agent Based LLM-as-Judge

Mastering Multi-Drone Volleyball through Hierarchical Co-Self-Play Reinforcement Learning

Automatic Mapping of AutomationML Files to Ontologies for Graph Queries and Validation

Explicit Context-Driven Neural Acoustic Modeling for High-Fidelity RIR Generation

FlowRL: Matching Reward Distributions for LLM Reasoning

Orion: Fuzzing Workflow Automation

TITAN: A Trajectory-Informed Technique for Adaptive Parameter Freezing in Large-Scale VQE

Fast and Fluent Diffusion Language Models via Convolutional Decoding and Rejective Fine-tuning

SMARTER: A Data-efficient Framework to Improve Toxicity Detection with Explanation via Self-augmenting Large Language Models

Watermarking and Anomaly Detection in Machine Learning Models for LORA RF Fingerprinting

Semi-Supervised 3D Medical Segmentation from 2D Natural Images Pretrained Model

Leveraging Geometric Visual Illusions as Perceptual Inductive Biases for Vision Models

Exploring How Audio Effects Alter Emotion with Foundation Models

WorldForge: Unlocking Emergent 3D/4D Generation in Video Diffusion Model via Training-Free Guidance

The mechanization of science illustrated by the Lean formalization of the multi-graded Proj construction

Vulnerable Agent Identification in Large-Scale Multi-Agent Reinforcement Learning

TextMine: LLM-Powered Knowledge Extraction for Humanitarian Mine Action

Listening, Imagining \& Refining: A Heuristic Optimized ASR Correction Framework with LLMs

Communication Efficient Split Learning of ViTs with Attention-based Double Compression

ALIGNS: Unlocking nomological networks in psychological measurement through a large language model

Created by

Haebom

作者

Kai R. Larsen, Sen Yan, Roland M. Mueller, Lan Sang, Mikko R onkk o, Ravi Starzl, Donald Edmondson

概要

本論文は、クロンバッハとミルルが提案した概念と測定の関係を示す理論的指導である名目的ネットワーク構築の難しさを解決するために、大規模言語モデルベースのシステムであるALIGNS（Analysis of Latent Indicators to Generate Nomological Structures）を提示します。 ALIGNSは、検証されたアンケート測定値で訓練されたシステムで、心理学、医学、社会政策など、さまざまな分野にわたって55万以上の指標を含む3つの包括的な名目ネットワークを提供します。本研究は、大規模言語モデルを測定検証の基本的な問題を解決するために初めて適用した事例であり、モデル開発に使用された分類精度テストと3つの評価適合性評価）結果を提示します。 ALIGNSはnomologicalnetwork.orgで無料で入手でき、既存の検証方法を大規模な名目分析で補完します。

Takeaways、Limitations

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Takeaways：

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大規模な言語モデルを活用して、既存の困難を克服し、名目上のネットワーク構築を自動化することで、測定検証プロセスを効率化できます。

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さまざまな分野の広範な指標を含む包括的な名目ネットワークを提供し、研究や政策決定に役立つ情報を提供します。

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既存の測定ツールの限界を克服し、新しい次元を発見するのに貢献することができます。

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既存の検証方法を補完する新しいアプローチの提示

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Limitations：

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ALIGNSのパフォーマンスはトレーニングデータの品質に依存し、偏向データは結果の信頼性を損なう可能性があります。

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大規模言語モデルのブラックボックスの特性により、モデルの意思決定プロセスに対する透明性が不足する可能性があります。

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専門家のレビューと検証が必要であり、すべての結果がすぐに適用できるわけではありません。

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アルゴリズムの一般化の可能性に関するさらなる研究が必要である。

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