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.

Aggregating Concepts of Accuracy and Fairness in Prediction Algorithms

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning

Graph neural networks and MSO

Generative Pre-trained Autoregressive Diffusion Transformer

SAS-Bench: A Fine-Grained Benchmark for Evaluating Short Answer Scoring with Large Language Models

Towards user-centered interactive medical image segmentation in VR with an assistive AI agent

Towards Scalable IoT Deployment for Visual Anomaly Detection via Efficient Compression

X-Sim: Cross-Embodiment Learning via Real-to-Sim-to-Real

UniVLA: Learning to Act Anywhere with Task-centric Latent Actions

Assessing Tenstorrent's RISC-V MatMul Acceleration Capabilities

Examining the Source of Defects from a Mechanical Perspective for 3D Anomaly Detection

Understanding In-context Learning of Addition via Activation Subspaces

Risk-sensitive Reinforcement Learning Based on Convex Scoring Functions

Intelligently Augmented Contrastive Tensor Factorization: Empowering Multi-dimensional Time Series Classification in Low-Data Environments

Modeling Human Behavior in a Strategic Network Game with Complex Group Dynamics

GPU Performance Portability needs Autotuning

RM-R1: Reward Modeling as Reasoning

Fast and Robust: Task Sampling with Posterior and Diversity Synergies for Adaptive Decision-Makers in Randomized Environments

Data-Driven Calibration of Prediction Sets in Large Vision-Language Models Based on Inductive Conformal Prediction

WildFireCan-MMD: A Multimodal Dataset for Classification of User-Generated Content During Wildfires in Canada

CLASH: Evaluating Language Models on Judging High-Stakes Dilemmas from Multiple Perspectives

Optimizing Power Grid Topologies with Reinforcement Learning: A Survey of Methods and Challenges

Flexible Graph Similarity Computation With A Proactive Optimization Strategy

CryoSAMU: Enhancing 3D Cryo-EM Density Maps of Protein Structures at Intermediate Resolution with Structure-Aware Multimodal U-Nets

PromptMobile: Efficient Promptus for Low Bandwidth Mobile Video Streaming

Implicit Bias-Like Patterns in Reasoning Models

EMMOE: A Comprehensive Benchmark for Embodied Mobile Manipulation in Open Environments

Temporal Triplane Transformers as Occupancy World Models

The Lazy Student's Dream: ChatGPT Passing an Engineering Course on Its Own

Atlas: A Framework for ML Lifecycle Provenance & Transparency

Centrally Coordinated Multi-Agent Reinforcement Learning for Power Grid Topology Control

Illegal Waste Detection in Remote Sensing Images: A Case Study

ARR: Question Answering with Large Language Models via Analyzing, Retrieving, and Reasoning

ImprovNet -- Generating Controllable Musical Improvisations with Iterative Corruption Refinement

Lightspeed Geometric Dataset Distance via Sliced Optimal Transport

Benchmarking Generative AI for Scoring Medical Student Interviews in Objective Structured Clinical Examinations (OSCEs)

Representation Convergence: Mutual Distillation is Secretly a Form of Regularization

Goal-Conditioned Supervised Learning for Multi-Objective Recommendation

Not All Adapters Matter: Selective Adapter Freezing for Memory-Efficient Fine-Tuning of Language Models

Simple and Provable Scaling Laws for the Test-Time Compute of Large Language Models

KBAlign: Efficient Self Adaptation on Specific Knowledge Bases

Natural Language Reinforcement Learning

Translating Electrocardiograms to Cardiac Magnetic Resonance Imaging Useful for Cardiac Assessment and Disease Screening: A Multi-Center Study AI for ECG to CMR Translation Study

TSINR: Capturing Temporal Continuity via Implicit Neural Representations for Time Series Anomaly Detection

Towards Graph Foundation Models: Training on Knowledge Graphs Enables Transferability to General Graphs

Temporal-Difference Variational Continual Learning

FAMMA: A Benchmark for Financial Domain Multilingual Multimodal Question Answering

Time Awareness in Large Language Models: Benchmarking Fact Recall Across Time

Self-supervised Learning for Acoustic Few-Shot Classification

Double Successive Over-Relaxation Q-Learning with an Extension to Deep Reinforcement Learning

SAKR: Enhancing Retrieval-Augmented Generation via Streaming Algorithm and K-Means Clustering

Compensate Quantization Errors+: Quantized Models Are Inquisitive Learners

Beyond Next Token Prediction: Patch-Level Training for Large Language Models

PersLLM: A Personified Training Approach for Large Language Models

PyramidKV: Dynamic KV Cache Compression based on Pyramidal Information Funneling

Demystifying AI Platform Design for Distributed Inference of Next-Generation LLM models

RoBERTa-BiLSTM: A Context-Aware Hybrid Model for Sentiment Analysis

Tokenization Matters! Degrading Large Language Models through Challenging Their Tokenization

Aligning Transformers with Continuous Feedback via Energy Rank Alignment

Large Language Models for Cyber Security: A Systematic Literature Review

Construction and Application of Materials Knowledge Graph in Multidisciplinary Materials Science via Large Language Model

CreativeSynth: Cross-Art-Attention for Artistic Image Synthesis with Multimodal Diffusion

LLM A*: Human in the Loop Large Language Models Enabled A* Search for Robotics

Neurodivergent Influenceability as a Contingent Solution to the AI Alignment Problem

AssertionForge: Enhancing Formal Verification Assertion Generation with Structured Representation of Specifications and RTL

Demonstrating specification gaming in reasoning models

SensorChat: Answering Qualitative and Quantitative Questions during Long-Term Multimodal Sensor Interactions

MapExplorer: New Content Generation from Low-Dimensional Visualizations

Addressing and Visualizing Misalignments in Human Task-Solving Trajectories

AriGraph: Learning Knowledge Graph World Models with Episodic Memory for LLM Agents

Learning Progress Driven Multi-Agent Curriculum

Neural Thermodynamic Laws for Large Language Model Training

MathCoder-VL: Bridging Vision and Code for Enhanced Multimodal Mathematical Reasoning

Does Feasibility Matter? Understanding the Impact of Feasibility on Synthetic Training Data

Real-Time Out-of-Distribution Failure Prevention via Multi-Modal Reasoning

LibIQ: Toward Real-Time Spectrum Classification in O-RAN dApps

Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation

Multi-Token Prediction Needs Registers

PnPXAI: A Universal XAI Framework Providing Automatic Explanations Across Diverse Modalities and Models

UniEval: Unified Holistic Evaluation for Unified Multimodal Understanding and Generation

Fine-tuning Diffusion Policies with Backpropagation Through Diffusion Timesteps

Superposition Yields Robust Neural Scaling

SEAL: Searching Expandable Architectures for Incremental Learning

Vision language models have difficulty recognizing virtual objects

Are Large Language Models Robust in Understanding Code Against Semantics-Preserving Mutations?

IN-RIL: Interleaved Reinforcement and Imitation Learning for Policy Fine-Tuning

PIF: Anomaly detection via preference embedding

Learned Lightweight Smartphone ISP with Unpaired Data

Visual Fidelity Index for Generative Semantic Communications with Critical Information Embedding

Rethinking Repetition Problems of LLMs in Code Generation

Inconsistency Handling in DatalogMTL

Uncovering Magnetic Phases with Synthetic Data and Physics-Informed Training

Schreier-Coset Graph Propagation

Multi-Agent Path Finding For Large Agents Is Intractable

Are Sparse Autoencoders Useful for Java Function Bug Detection?

ILIF: Temporal Inhibitory Leaky Integrate-and-Fire Neuron for Overactivation in Spiking Neural Networks

FactsR: A Safer Method for Producing High Quality Healthcare Documentation

SpikeVideoFormer: An Efficient Spike-Driven Video Transformer with Hamming Attention and $\mathcal{O}(T)$ Complexity

Uniform Loss vs. Specialized Optimization: A Comparative Analysis in Multi-Task Learning

Emergence of Structure in Ensembles of Random Neural Networks

"There Is No Such Thing as a Dumb Question," But There Are Good Ones

Created by

Haebom

저자

Minjung Shin, Donghyun Kim, Jeh-Kwang Ryu

개요

본 논문은 인간과 인공지능 모두에게 점점 중요해지고 있는 질문의 질에 대한 종합적인 평가 연구가 부족하다는 점을 지적하며, 질문의 질을 정의하고 체계적인 평가 프레임워크를 제시한다. 적절성(맥락에서의 사회언어적 능력)과 효과성(목표 달성에서의 전략적 능력)이라는 두 가지 주요 평가 차원을 제안하고, 이를 기반으로 루브릭 기반 채점 시스템을 개발하였다. 동적인 맥락 변수를 통합하여 반자동 적응 기준을 통해 구조와 유연성을 확보한 평가 프레임워크는 CAUS 및 SQUARE 데이터셋을 사용하여 검증되었으며, 다양한 맥락에 적응하면서 잘 구성된 질문과 문제가 있는 질문 모두를 평가할 수 있음을 보여준다. 본 연구는 질문 행위를 질문의 본질적인 특성에 기반한 구조적 분석 방법과 통합하는 데 중요한 발걸음을 내딛는다.

시사점, 한계점

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시사점:

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질문의 질에 대한 체계적이고 포괄적인 평가 프레임워크를 제시하여 질문 행위의 분석 및 평가에 대한 새로운 접근 방식을 제공한다.

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적절성과 효과성이라는 두 가지 중요한 평가 차원을 제시함으로써 질문의 질을 다각적으로 평가할 수 있는 기반을 마련한다.

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동적인 맥락 변수를 고려한 반자동 적응 기준을 통해 다양한 상황에 적용 가능한 유연한 프레임워크를 구축한다.

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CAUS 및 SQUARE 데이터셋을 활용한 검증을 통해 프레임워크의 실효성을 입증한다.

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한계점:

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제시된 프레임워크의 객관성과 일반화 가능성에 대한 추가적인 연구가 필요하다.

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다양한 유형의 질문과 맥락에 대한 적용 가능성을 더욱 심도 있게 검증해야 한다.

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루브릭 기반 채점 시스템의 신뢰도와 타당도에 대한 추가적인 분석이 필요하다.

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다른 평가 프레임워크와의 비교 분석을 통해 본 프레임워크의 우수성을 더욱 명확히 밝힐 필요가 있다.

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