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.

Ring-lite: Scalable Reasoning via C3PO-Stabilized Reinforcement Learning for LLMs

Accurate and scalable exchange-correlation with deep learning

AIn't Nothing But a Survey? Using Large Language Models for Coding German Open-Ended Survey Responses on Survey Motivation

Probabilistic Aggregation and Targeted Embedding Optimization for Collective Moral Reasoning in Large Language Models

Aligning Evaluation with Clinical Priorities: Calibration, Label Shift, and Error Costs

GRAM: A Generative Foundation Reward Model for Reward Generalization

VideoMAR: Autoregressive Video Generatio with Continuous Tokens

FrontendBench: A Benchmark for Evaluating LLMs on Front-End Development via Automatic Evaluation

Seewo's Submission to MLC-SLM: Lessons learned from Speech Reasoning Language Models

No-Regret Learning Under Adversarial Resource Constraints: A Spending Plan Is All You Need!

Serving Large Language Models on Huawei CloudMatrix384

PLD: A Choice-Theoretic List-Wise Knowledge Distillation

TARDIS STRIDE: A Spatio-Temporal Road Image Dataset and World Model for Autonomy

Refactoring Codebases through Library Design

TransXSSM: A Hybrid Transformer State Space Model with Unified Rotary Position Embedding

Multi-Agent Language Models: Advancing Cooperation, Coordination, and Adaptation

Multi-Task Reward Learning from Human Ratings

Too Big to Think: Capacity, Memorization, and Generalization in Pre-Trained Transformers

Router-R1: Teaching LLMs Multi-Round Routing and Aggregation via Reinforcement Learning

Vision Transformers Don't Need Trained Registers

BIS Reasoning 1.0: The First Large-Scale Japanese Benchmark for Belief-Inconsistent Syllogistic Reasoning

LaMP-Cap: Personalized Figure Caption Generation With Multimodal Figure Profiles

CORA: Coalitional Rational Advantage Decomposition for Multi-Agent Policy Gradients

Supervised Quantum Machine Learning: A Future Outlook from Qubits to Enterprise Applications

ChemHAS: Hierarchical Agent Stacking for Enhancing Chemistry Tools

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models

Think Twice before Adaptation: Improving Adaptability of DeepFake Detection via Online Test-Time Adaptation

Efficient Long CoT Reasoning in Small Language Models

Imagine Beyond! Distributionally Robust Auto-Encoding for State Space Coverage in Online Reinforcement Learning

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion

J4R: Learning to Judge with Equivalent Initial State Group Relative Policy Optimization

Fractured Chain-of-Thought Reasoning

DreamGen: Unlocking Generalization in Robot Learning through Video World Models

UD-English-CHILDES: A Collected Resource of Gold and Silver Universal Dependencies Trees for Child Language Interactions

Position Paper: Rethinking Privacy in RL for Sequential Decision-making in the Age of LLMs

Influential Bandits: Pulling an Arm May Change the Environment

SCAM: A Real-World Typographic Robustness Evaluation for Multimodal Foundation Models

Trust Region Preference Approximation: A simple and stable reinforcement learning algorithm for LLM reasoning

Exploring Personalized Federated Learning Architectures for Violence Detection in Surveillance Videos

A Bird Song Detector for improving bird identification through Deep Learning: a case study from Do\~nana

KANITE: Kolmogorov-Arnold Networks for ITE estimation

Beyond Propagation of Chaos: A Stochastic Algorithm for Mean Field Optimization

Resolving UnderEdit & OverEdit with Iterative & Neighbor-Assisted Model Editing

Adding Chocolate to Mint: Mitigating Metric Interference in Machine Translation

EgoBlind: Towards Egocentric Visual Assistance for the Blind

PsychBench: A comprehensive and professional benchmark for evaluating the performance of LLM-assisted psychiatric clinical practice

Machine Learners Should Acknowledge the Legal Implications of Large Language Models as Personal Data

Supporting the development of Machine Learning for fundamental science in a federated Cloud with the AI_INFN platform

CODESYNC: Synchronizing Large Language Models with Dynamic Code Evolution at Scale

Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning

Task-Aware Virtual Training: Enhancing Generalization in Meta-Reinforcement Learning for Out-of-Distribution Tasks

Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions

Perspective Transition of Large Language Models for Solving Subjective Tasks

Can LLMs Ask Good Questions?

Aligning AI Research with the Needs of Clinical Coding Workflows: Eight Recommendations Based on US Data Analysis and Critical Review

SurgSora: Object-Aware Diffusion Model for Controllable Surgical Video Generation

Large Language Models for Automated Literature Review: An Evaluation of Reference Generation, Abstract Writing, and Review Composition

Multiclass Post-Earthquake Building Assessment Integrating High-Resolution Optical and SAR Satellite Imagery, Ground Motion, and Soil Data with Transformers

REVOLVE: Optimizing AI Systems by Tracking Response Evolution in Textual Optimization

FLARE: Towards Universal Dataset Purification against Backdoor Attacks

Heterogeneous Relationships of Subjects and Shapelets for Semi-supervised Multivariate Series Classification

Contrast Similarity-Aware Dual-Pathway Mamba for Multivariate Time Series Node Classification

Semantic-Geometric-Physical-Driven Robot Manipulation Skill Transfer via Skill Library and Tactile Representation

LL\"aMmlein: Transparent, Compact and Competitive German-Only Language Models from Scratch

Math Neurosurgery: Isolating Language Models' Math Reasoning Abilities Using Only Forward Passes

The Epochal Sawtooth Phenomenon: Unveiling Training Loss Oscillations in Adam and Other Optimizers

Pap2Pat: Benchmarking Outline-Guided Long-Text Patent Generation with Patent-Paper Pairs

Deep Graph Anomaly Detection: A Survey and New Perspectives

A Novel Perturb-ability Score to Mitigate Evasion Adversarial Attacks on Flow-Based ML-NIDS

Style-Preserving Lip Sync via Audio-Aware Style Reference

Advancing oncology with federated learning: transcending boundaries in breast, lung, and prostate cancer. A systematic review

Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Survey

Informed Correctors for Discrete Diffusion Models

RadioRAG: Online Retrieval-augmented Generation for Radiology Question Answering

A Systematic Survey of Natural Language Processing for the Greek Language

Predicting the Understandability of Computational Notebooks through Code Metrics Analysis

An Effective Incorporating Heterogeneous Knowledge Curriculum Learning for Sequence Labeling

HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction

The NordDRG AI Benchmark for Large Language Models

From Data-Driven to Purpose-Driven Artificial Intelligence: Systems Thinking for Data-Analytic Automation of Patient Care

Breaking Bad Molecules: Are MLLMs Ready for Structure-Level Molecular Detoxification?

Entropy-based Exploration Conduction for Multi-step Reasoning

Solving Satisfiability Modulo Counting Exactly with Probabilistic Circuits

Synthesizing Composite Hierarchical Structure from Symbolic Music Corpora

Learning Strategic Language Agents in the Werewolf Game with Iterative Latent Space Policy Optimization

Optimal Transport for Probabilistic Circuits

OM4OV: Leveraging Ontology Matching for Ontology Versioning

Behaviour Planning: A Toolkit for Diverse Planning

Spatial Context-based Self-Supervised Learning for Handwritten Text Recognition

"Generate" the Future of Work through AI: Empirical Evidence from Online Labor Markets

Dense SAE Latents Are Features, Not Bugs

Sekai: A Video Dataset towards World Exploration

Leaky Thoughts: Large Reasoning Models Are Not Private Thinkers

AutoRule: Reasoning Chain-of-thought Extracted Rule-based Rewards Improve Preference Learning

Demystifying the Visual Quality Paradox in Multimodal Large Language Models

Revisiting Compositional Generalization Capability of Large Language Models Considering Instruction Following Ability

Federated Learning for MRI-based BrainAGE: a multicenter study on post-stroke functional outcome prediction

GFLC: Graph-based Fairness-aware Label Correction for Fair Classification

The Compositional Architecture of Regret in Large Language Models

LoX: Low-Rank Extrapolation Robustifies LLM Safety Against Fine-tuning

A Mathematical Framework for AI-Human Integration in Work

Created by

Haebom

저자

L. Elisa Celis, Lingxiao Huang, Nisheeth K. Vishnoi

개요

본 논문은 생성형 AI(GenAI) 도구의 등장이 다양한 직업 환경에서 인간 노동자를 보완하거나 대체하는 역할에 대한 논쟁을 불러일으킨 점을 배경으로, 직업, 노동자, 노동자-직업 적합성을 모델링하는 수학적 프레임워크를 제시한다. 인간과 GenAI의 상호 보완적인 강점을 반영하기 위해 기술을 의사결정 수준과 행동 수준의 하위 기술로 새롭게 분해한다. 하위 기술 능력의 변화가 직무 성공에 미치는 영향을 분석하고, 성공 확률의 급격한 변화 조건을 파악한다. 또한 상호 보완적인 하위 기술을 가진 노동자를 결합하는 것이 단일 노동자에 의존하는 것보다 훨씬 더 나은 성과를 낼 수 있는 충분 조건을 확립한다. 이는 GenAI 지원이 저숙련 노동자에게 더 큰 이익을 가져다주는 생산성 압축과 같은 현상을 설명한다. O*NET 및 Big-Bench Lite의 데이터를 사용하여 프레임워크의 실용성을 보여주고, 하위 기술 분할 방법을 통해 실제 데이터와 모델을 정렬한다. 연구 결과는 GenAI가 인간의 기술을 대체하기보다는 언제 어떻게 보완하는지를 강조한다.

시사점, 한계점

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시사점:

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GenAI가 인간의 기술을 보완하는 방식에 대한 수학적 프레임워크를 제공한다.

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의사결정 수준과 행동 수준의 하위 기술 분해를 통해 인간과 GenAI의 상호작용을 분석한다.

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GenAI 지원이 저숙련 노동자에게 더 큰 생산성 향상을 가져오는 현상(생산성 압축)을 설명한다.

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실제 데이터(O*NET, Big-Bench Lite)를 활용하여 모델의 실용성을 검증한다.

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한계점:

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제시된 수학적 프레임워크의 일반화 가능성에 대한 추가 연구가 필요하다.

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하위 기술 분할 방법의 객관성 및 신뢰성에 대한 검토가 필요하다.

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실제 직업 환경의 복잡성을 완전히 반영하지 못할 수 있다.

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GenAI의 발전 속도를 고려할 때, 모델의 장기적인 유효성에 대한 검토가 필요하다.

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