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.

Batch Acquisition Function Evaluations and Decouple Optimizer Updates for Faster Bayesian Optimization

Physics-Informed Neural Networks for Nonlinear Output Regulation

Unlocking the Forgery Detection Potential of Vanilla MLLMs: A Novel Training-Free Pipeline

Generalized Denoising Diffusion Codebook Models (gDDCM): Tokenizing images using a pre-trained diffusion model

Rethinking Saliency Maps: A Cognitive Human Aligned Taxonomy and Evaluation Framework for Explanations

Dimension vs. Precision: A Comparative Analysis of Autoencoders and Quantization for Efficient Vector Retrieval on BEIR SciFact

UNSEEN: Enhancing Dataset Pruning from a Generalization Perspective

PFAvatar: Pose-Fusion 3D Personalized Avatar Reconstruction from Real-World Outfit-of-the-Day Photos

Optimal Look-back Horizon for Time Series Forecasting in Federated Learning

Fine-Grained Representation for Lane Topology Reasoning

VULPO: Context-Aware Vulnerability Detection via On-Policy LLM Optimization

A Meta-Heuristic Load Balancer for Cloud Computing Systems

Embedding Explainable AI in NHS Clinical Safety: The Explainability-Enabled Clinical Safety Framework (ECSF)

MedBuild AI: An Agent-Based Hybrid Intelligence Framework for Reshaping Agency in Healthcare Infrastructure Planning through Generative Design for Medical Architecture

MOON Embedding: Multimodal Representation Learning for E-commerce Search Advertising

Efficient Reinforcement Learning for Zero-Shot Coordination in Evolving Games

Who Gets the Reward, Who Gets the Blame? Evaluation-Aligned Training Signals for Multi-LLM Agents

A Style is Worth One Code: Unlocking Code-to-Style Image Generation with Discrete Style Space

LocalBench: Benchmarking LLMs on County-Level Local Knowledge and Reasoning

LoopTool: Closing the Data-Training Loop for Robust LLM Tool Calls

Tele-LLM-Hub: Building Context-Aware Multi-Agent LLM Systems for Telecom Networks

FGM-HD: Boosting Generation Diversity of Fractal Generative Models through Hausdorff Dimension Induction

Energy Consumption of Dataframe Libraries for End-to-End Deep Learning Pipelines:A Comparative Analysis

Resilient by Design -- Active Inference for Distributed Continuum Intelligence

Resource Efficient Sleep Staging via Multi-Level Masking and Prompt Learning

Physics-Informed Neural Networks for Real-Time Gas Crossover Prediction in PEM Electrolyzers: First Application with Multi-Membrane Validation

MusRec: Zero-Shot Text-to-Music Editing via Rectified Flow and Diffusion Transformers

Leveraging LLM-based agents for social science research: insights from citation network simulations

Deploying Rapid Damage Assessments from sUAS Imagery for Disaster Response

DRIP: Defending Prompt Injection via Token-wise Representation Editing and Residual Instruction Fusion

Clone Deterministic 3D Worlds

Posterior Sampling by Combining Diffusion Models with Annealed Langevin Dynamics

MMEdge: Accelerating On-device Multimodal Inference via Pipelined Sensing and Encoding

FinVet: A Collaborative Framework of RAG and External Fact-Checking Agents for Financial Misinformation Detection

From Reasoning LLMs to BERT: A Two-Stage Distillation Framework for Search Relevance

PromptGuard at BLP-2025 Task 1: A Few-Shot Classification Framework Using Majority Voting and Keyword Similarity for Bengali Hate Speech Detection

A More Realistic Evaluation of Cross-Frequency Transfer Learning and Foundation Forecasting Models

Towards Sharper Object Boundaries in Self-Supervised Depth Estimation

Patent Language Model Pretraining with ModernBERT

Retrosynthesis Planning via Worst-path Policy Optimisation in Tree-structured MDPs

Efficient Decoding Methods for Language Models on Encrypted Data

Spectral Masking and Interpolation Attack (SMIA): A Black-box Adversarial Attack against Voice Authentication and Anti-Spoofing Systems

DepthVision: Enabling Robust Vision-Language Models with GAN-Based LiDAR-to-RGB Synthesis for Autonomous Driving

Automatic Differentiation of Agent-Based Models

Do Retrieval Augmented Language Models Know When They Don't Know?

Skill-Aligned Fairness in Multi-Agent Learning for Collaboration in Healthcare

Spatial Policy: Guiding Visuomotor Robotic Manipulation with Spatial-Aware Modeling and Reasoning

RynnEC: Bringing MLLMs into Embodied World

LENS: Learning to Segment Anything with Unified Reinforced Reasoning

MOON: Generative MLLM-based Multimodal Representation Learning for E-commerce Product Understanding

DIVER: A Multi-Stage Approach for Reasoning-intensive Information Retrieval

Explaining Similarity in Vision-Language Encoders with Weighted Banzhaf Interactions

Dynamic User-controllable Privacy-preserving Few-shot Sensing Framework

LLMDistill4Ads: Using Cross-Encoders to Distill from LLM Signals for Advertiser Keyphrase Recommendations

SlotMatch: Distilling Object-Centric Representations for Unsupervised Video Segmentation

GAIS: Frame-Level Gated Audio-Visual Integration with Semantic Variance-Scaled Perturbation for Text-Video Retrieval

GMAT: Grounded Multi-Agent Clinical Description Generation for Text Encoder in Vision-Language MIL for Whole Slide Image Classification

AgentArmor: Enforcing Program Analysis on Agent Runtime Trace to Defend Against Prompt Injection

Cross-Domain Few-Shot Learning with Coalescent Projections and Latent Space Reservation

FastDINOv2: Frequency Based Curriculum Learning Improves Robustness and Training Speed

OptScale: Probabilistic Optimality for Inference-time Scaling

EvoLM: In Search of Lost Language Model Training Dynamics

Synthetic Geology: Structural Geology Meets Deep Learning

Foundation Models in Medical Imaging: A Review and Outlook

FairDICE: Fairness-Driven Offline Multi-Objective Reinforcement Learning

OG-VLA: Orthographic Image Generation for 3D-Aware Vision-Language Action Model

Scaling Textual Gradients via Sampling-Based Momentum

MCTSr-Zero: Self-Reflective Psychological Counseling Dialogues Generation via Principles and Adaptive Exploration

In-context Language Learning for Endangered Languages in Speech Recognition

SpecEdge: Scalable Edge-Assisted Serving Framework for Interactive LLMs

Preference Learning with Lie Detectors can Induce Honesty or Evasion

Anti-adversarial Learning: Desensitizing Prompts for Large Language Models

AdCare-VLM: Towards a Unified and Pre-aligned Latent Representation for Healthcare Video Understanding

Non-Uniform Class-Wise Coreset Selection for Vision Model Fine-tuning

ACoRN: Noise-Robust Abstractive Compression in Retrieval-Augmented Language Models

A Survey of Cross-domain Graph Learning: Progress and Future Directions

Availability-aware Sensor Fusion via Unified Canonical Space

ElementaryNet: A Non-Strategic Neural Network for Predicting Human Behavior in Normal-Form Games

1-Lipschitz Network Initialization for Certifiably Robust Classification Applications: A Decay Problem

Personalized Image Generation for Recommendations Beyond Catalogs

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

MoM: Linear Sequence Modeling with Mixture-of-Memories

Rethinking Token-wise Feature Caching: Accelerating Diffusion Transformers with Dual Feature Caching

Automated Materials Discovery Platform Realized: Scanning Probe Microscopy of Combinatorial Libraries

Higher-Order Transformers With Kronecker-Structured Attention

Optimizing Federated Learning by Entropy-Based Client Selection

VisAidMath: Benchmarking Visual-Aided Mathematical Reasoning

Can Machines Think Like Humans? A Behavioral Evaluation of LLM Agents in Dictator Games

Generating Streamlining Constraints with Large Language Models

The Energy Cost of Artificial Intelligence Lifecycle in Communication Networks

KWT-Tiny: RISC-V Accelerated, Embedded Keyword Spotting Transformer

Benchmark on Drug Target Interaction Modeling from a Drug Structure Perspective

Virtual Human Generative Model: Masked Modeling Approach for Learning Human Characteristics

NAIST Academic Travelogue Dataset

SemCo: Toward Semantic Coherent Visual Relationship Forecasting

Multi-Agent Deep Research: Training Multi-Agent Systems with M-GRPO

Requirements for Aligned, Dynamic Resolution of Conflicts in Operational Constraints

CTRL-ALT-DECEIT: Sabotage Evaluations for Automated AI R&D

VSPO: Validating Semantic Pitfalls in Ontology via LLM-Based CQ Generation

Are We Asking the Right Questions? On Ambiguity in Natural Language Queries for Tabular Data Analysis

Pairwise Matching of Intermediate Representations for Fine-grained Explainability

Created by

Haebom

저자

Lauren Shrack, Timm Haucke, Antoine Salaun, Arjun Subramonian, Sara Beery

개요

본 논문은 PAIR-X라는 새로운 설명 가능성 기법을 제안합니다. PAIR-X는 중간 모델 활성화와 역전파 관련 점수를 활용하여 미세한 차이를 가진 이미지들에 대해 정밀하고 국지적인 쌍별 시각적 설명을 생성합니다. 동물 및 건물 재식별(re-ID)을 주요 사례 연구로 사용하여 35개의 공개 재식별 데이터셋에서 다양한 설명 가능성 기준과 비교하여 질적으로 향상된 결과를 보여줍니다. 동물 재식별 전문가 인터뷰에서 PAIR-X가 기존 기준보다 개선되었다는 의견이 일치하였으며, 시각화가 작업에 직접 적용될 수 있다고 제안했습니다. 또한 새로운 정량적 평가 지표를 제안하고, 쌍에 대한 모델 유사성 점수가 동일하더라도 PAIR-X 시각화가 정확한 이미지 매칭에 대해 더욱 그럴듯하게 나타나는 것을 보여줍니다. 해석성을 향상시킴으로써 PAIR-X는 사람들이 정확한 매칭과 잘못된 매칭을 더 잘 구별할 수 있도록 합니다. 코드는 https://github.com/pairx-explains/pairx 에서 이용 가능합니다.

GitHub - pairx-explains/pairx: Demo code for Pairwise mAtching of Intermediate Representations for eXplainability (PAIR-X).

Demo code for Pairwise mAtching of Intermediate Representations for eXplainability (PAIR-X). - pairx-explains/pairx

시사점, 한계점

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시사점:

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미세 차이를 가진 이미지에 대한 정밀하고 국지적인 쌍별 시각적 설명을 제공하는 새로운 설명 가능성 기법 PAIR-X 제안

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다양한 설명 가능성 기준에 비해 질적으로 향상된 결과를 35개의 공개 재식별 데이터셋에서 검증

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동물 재식별 전문가 인터뷰를 통해 실제 적용 가능성 확인

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새로운 정량적 평가 지표 제안 및 정확한 매칭에 대한 시각화의 신뢰성 증명

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모델 해석성 향상을 통한 정확한 매칭과 잘못된 매칭의 구별 용이성 증가

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한계점:

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본 논문에서는 명시적으로 한계점을 언급하지 않음. 추가적인 데이터셋이나 다른 유형의 분류 문제에 대한 일반화 가능성에 대한 추가 연구가 필요할 수 있음. 정량적 평가 지표의 범용성에 대한 추가 검증이 필요할 수 있음.

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