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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.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout

Energy Efficiency in AI for 5G and Beyond: A DeepRx Case Study

A PBN-RL-XAI Framework for Discovering a "Hit-and-Run" Therapeutic Strategy in Melanoma

(Almost) Free Modality Stitching of Foundation Models

Prompt4Trust: A Reinforcement Learning Prompt Augmentation Framework for Clinically-Aligned Confidence Calibration in Multimodal Large Language Models

SEALGuard: Safeguarding the Multilingual Conversations in Southeast Asian Languages for LLM Software Systems

Dually Hierarchical Drift Adaptation for Online Configuration Performance Learning

Tree-Structured Parzen Estimator Can Solve Black-Box Combinatorial Optimization More Efficiently

EXPO: Stable Reinforcement Learning with Expressive Policies

Reinforcement Learning with Action Chunking

On the Effect of Instruction Tuning Loss on Generalization

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

Text to model via SysML: Automated generation of dynamical system computational models from unstructured natural language text via enhanced System Modeling Language diagrams

Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why

DRAGON: Dynamic RAG Benchmark On News

Solar Flare Prediction Using Long Short-term Memory (LSTM) and Decomposition-LSTM with Sliding Window Pattern Recognition

Conversation Forests: The Key to Fine Tuning Large Language Models for Multi-Turn Medical Conversations is Branching

RAG-R1 : Incentivize the Search and Reasoning Capabilities of LLMs through Multi-query Parallelism

Following the Clues: Experiments on Person Re-ID using Cross-Modal Intelligence

Stylometry recognizes human and LLM-generated texts in short samples

QLPro: Automated Code Vulnerability Discovery via LLM and Static Code Analysis Integration

Evaluating Multimodal Large Language Models on Educational Textbook Question Answering

FeDa4Fair: Client-Level Federated Datasets for Fairness Evaluation

Alleviating User-Sensitive bias with Fair Generative Sequential Recommendation Model

MATE: LLM-Powered Multi-Agent Translation Environment for Accessibility Applications

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE

ImpliRet: Benchmarking the Implicit Fact Retrieval Challenge

The Price of Freedom: Exploring Expressivity and Runtime Tradeoffs in Equivariant Tensor Products

The Limits of Tractable Marginalization

A quantum semantic framework for natural language processing

ProtocolLLM: RTL Benchmark for SystemVerilog Generation of Communication Protocols

Deepfake Technology Unveiled: The Commoditization of AI and Its Impact on Digital Trust

Training Dynamics Underlying Language Model Scaling Laws: Loss Deceleration and Zero-Sum Learning

Critique-GRPO: Advancing LLM Reasoning with Natural Language and Numerical Feedback

Matrix Is All You Need

Temporal Chunking Enhances Recognition of Implicit Sequential Patterns

Seven Security Challenges That Must be Solved in Cross-domain Multi-agent LLM Systems

PAN-Crafter: Learning Modality-Consistent Alignment for PAN-Sharpening

FlowAlign: Trajectory-Regularized, Inversion-Free Flow-based Image Editing

Traveling Across Languages: Benchmarking Cross-Lingual Consistency in Multimodal LLMs

FalseReject: A Resource for Improving Contextual Safety and Mitigating Over-Refusals in LLMs via Structured Reasoning

Multimodal Sentiment Analysis on CMU-MOSEI Dataset using Transformer-based Models

Nexus-Gen: Unified Image Understanding, Generation, and Editing via Prefilled Autoregression in Shared Embedding Space

Leveraging Large Language Models for Multi-Class and Multi-Label Detection of Drug Use and Overdose Symptoms on Social Media

Rethinking the Foundations for Continual Reinforcement Learning

Compositional Flows for 3D Molecule and Synthesis Pathway Co-design

Rethinking RoPE: A Mathematical Blueprint for N-dimensional Positional Embedding

Speculative Automated Refactoring of Imperative Deep Learning Programs to Graph Execution

Test-time Adaptation for Foundation Medical Segmentation Model without Parametric Updates

Style over Substance: Distilled Language Models Reason Via Stylistic Replication

AnnoPage Dataset: Dataset of Non-Textual Elements in Documents with Fine-Grained Categorization

Multi-View Node Pruning for Accurate Graph Representation

Hi Robot: Open-Ended Instruction Following with Hierarchical Vision-Language-Action Models

Voting or Consensus? Decision-Making in Multi-Agent Debate

Assistance or Disruption? Exploring and Evaluating the Design and Trade-offs of Proactive AI Programming Support

A Generative Approach to LLM Harmfulness Detection with Special Red Flag Tokens

Score-of-Mixture Training: Training One-Step Generative Models Made Simple via Score Estimation of Mixture Distributions

Model Tampering Attacks Enable More Rigorous Evaluations of LLM Capabilities

Synthetic Datasets for Machine Learning on Spatio-Temporal Graphs using PDEs

Comply: Learning Sentences with Complex Weights inspired by Fruit Fly Olfaction

Inverse Reinforcement Learning with Switching Rewards and History Dependency for Characterizing Animal Behaviors

Few-Shot Radar Signal Recognition through Self-Supervised Learning and Radio Frequency Domain Adaptation

Transfer Learning Analysis of Variational Quantum Circuits

Plancraft: an evaluation dataset for planning with LLM agents

Fully Data-driven but Interpretable Human Behavioural Modelling with Differentiable Discrete Choice Model

A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation

Is Training Data Quality or Quantity More Impactful to Small Language Model Performance?

Searching Latent Program Spaces

The Pragmatic Frames of Spurious Correlations in Machine Learning: Interpreting How and Why They Matter

ComFairGNN: Community Fair Graph Neural Network

DroidSpeak: KV Cache Sharing for Cross-LLM Communication and Multi-LLM Serving

Online Intrinsic Rewards for Decision Making Agents from Large Language Model Feedback

Large Language Models Engineer Too Many Simple Features For Tabular Data

Overcoming Slow Decision Frequencies in Continuous Control: Model-Based Sequence Reinforcement Learning for Model-Free Control

IdeaSynth: Iterative Research Idea Development Through Evolving and Composing Idea Facets with Literature-Grounded Feedback

SECURE: Semantics-aware Embodied Conversation under Unawareness for Lifelong Robot Learning

Advancing Depth Anything Model for Unsupervised Monocular Depth Estimation in Endoscopy

SA-GDA: Spectral Augmentation for Graph Domain Adaptation

The GPT Surprise: Offering Large Language Model Chat in a Massive Coding Class Reduced Engagement but Increased Adopters Exam Performances

State-Constrained Offline Reinforcement Learning

SimAD: A Simple Dissimilarity-based Approach for Time Series Anomaly Detection

Unified ODE Analysis of Smooth Q-Learning Algorithms

FairTargetSim: An Interactive Simulator for Understanding and Explaining the Fairness Effects of Target Variable Definition

Fine-grained Stateful Knowledge Exploration: Effective and Efficient Graph Retrieval with Large Language Models

Learning Safe Numeric Planning Action Models

Augmenting End-to-End Steering Angle Prediction with CAN Bus Data

EASTER: Embedding Aggregation-based Heterogeneous Models Training in Vertical Federated Learning

GRAPES: Learning to Sample Graphs for Scalable Graph Neural Networks

Acquiring and Adapting Priors for Novel Tasks via Neural Meta-Architectures

VerifyBench: A Systematic Benchmark for Evaluating Reasoning Verifiers Across Domains

Is Human-Written Data Enough? The Challenge of Teaching Reasoning to LLMs Without RL or Distillation

Working with AI: Measuring the Occupational Implications of Generative AI

Establishing Best Practices for Building Rigorous Agentic Benchmarks

An Agentic Framework for Autonomous Metamaterial Modeling and Inverse Design

Seeking to Collide: Online Safety-Critical Scenario Generation for Autonomous Driving with Retrieval Augmented Large Language Models

BOOST: Bootstrapping Strategy-Driven Reasoning Programs for Program-Guided Fact-Checking

The Odyssey of the Fittest: Can Agents Survive and Still Be Good?

Agentic Reasoning: A Streamlined Framework for Enhancing LLM Reasoning with Agentic Tools

ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning

Can AI Freelancers Compete? Benchmarking Earnings, Reliability, and Task Success at Scale

Created by

Haebom

저자

David Noever, Forrest McKee

개요

본 연구는 대규모 언어 모델(LLM)을 실제 세계 과제, 특히 프리랜서 소프트웨어 개발에 적용하는 것을 탐구합니다. Kaggle 프리랜서 데이터셋의 일자리 게시물을 기반으로 생성된 합성 과제를 사용하여 새로운 벤치마크를 제시합니다. 모든 과제는 USD로 표준화된 가격(중간 고정 프로젝트 가격 약 $250, 평균 $306)이 책정되어 있으며, 구조화된 입력-출력 테스트 케이스가 함께 제공되어 자동화된 정확성 검사 및 금전적 성과 평가가 가능합니다. Claude 3.5 Haiku, GPT-4o-mini, Qwen 2.5, Mistral 네 가지 최신 LLM을 평가하여 정확도(과제 성공률 및 테스트 케이스 통과율)와 총 "프리랜서 수입"(해결된 과제 가격의 합계)을 측정했습니다. Claude 3.5 Haiku가 약 152만 달러로 가장 높은 수입을 올렸고, GPT-4o-mini(149만 달러), Qwen 2.5(133만 달러), Mistral(70만 달러)이 뒤를 이었습니다. 과제별 오류 분포를 분석하여 강력한 모델일수록 더 많은 과제를 해결하고 프로젝트에서 완전히 실패하는 경우가 드물다는 것을 확인했습니다. AI의 프리랜서 개발자로서의 실현 가능성, 자동화된 벤치마크 접근 방식의 장단점, 그리고 구조화된 과제에서의 성능과 실제 세계 프리랜서 작업의 복잡성 간의 차이점에 대해 논의합니다.

시사점, 한계점

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시사점:

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LLM이 프리랜서 프로그래밍 및 데이터 분석 작업에서 상당한 성과를 달성할 수 있음을 보여줍니다.

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제시된 자동화된 벤치마크는 LLM의 실제 세계 적용 가능성을 평가하는 데 유용한 도구임을 시사합니다.

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LLM의 성능 차이를 정량적으로 비교 분석할 수 있는 기준을 제공합니다.

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특정 LLM의 강점과 약점을 파악하는 데 도움이 됩니다.

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한계점:

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벤치마크가 합성 과제를 기반으로 하므로 실제 세계 프리랜서 작업의 복잡성을 완전히 반영하지 못할 수 있습니다.

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프로그램 방식으로 테스트 가능한 과제에 초점을 맞추어 실제 프리랜서 작업의 다양성을 충분히 고려하지 못할 수 있습니다.

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예측 가격을 사용하여 과제의 가치를 평가하는 방식은 실제 시장 가격과 차이가 있을 수 있습니다.

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LLM의 창의성, 의사소통 능력 등 비 기술적 요소는 평가에 반영되지 않았습니다.

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