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.

End-to-End Text-to-SQL with Dataset Selection: Leveraging LLMs for Adaptive Query Generation

Fourier-VLM: Compressing Vision Tokens in the Frequency Domain for Large Vision-Language Models

LAG: Logic-Augmented Generation from a Cartesian Perspective

Echo: Decoupling Inference and Training for Large-Scale RL Alignment on Heterogeneous Swarms

FDC-Net: Rethinking the association between EEG artifact removal and multi-dimensional affective computing

Fairness in Dysarthric Speech Synthesis: Understanding Intrinsic Bias in Dysarthric Speech Cloning using F5-TTS

RCR-Router: Efficient Role-Aware Context Routing for Multi-Agent LLM Systems with Structured Memory

DS$^2$Net: Detail-Semantic Deep Supervision Network for Medical Image Segmentation

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

When Cars Have Stereotypes: Auditing Demographic Bias in Objects from Text-to-Image Models

HiTeC: Hierarchical Contrastive Learning on Text-Attributed Hypergraph with Semantic-Aware Augmentation

SpectrumFM: Redefining Spectrum Cognition via Foundation Modeling

Dynamic Robot-Assisted Surgery with Hierarchical Class-Incremental Semantic Segmentation

A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations

A Bit of Freedom Goes a Long Way: Classical and Quantum Algorithms for Reinforcement Learning under a Generative Model

aLLoyM: A large language model for alloy phase diagram prediction

Learning Phonetic Context-Dependent Viseme for Enhancing Speech-Driven 3D Facial Animation

Are Vision Foundation Models Ready for Out-of-the-Box Medical Image Registration?

SystolicAttention: Fusing FlashAttention within a Single Systolic Array

RAPNet: A Receptive-Field Adaptive Convolutional Neural Network for Pansharpening

AMix-1: A Pathway to Test-Time Scalable Protein Foundation Model

Bridging the Last Mile of Prediction: Enhancing Time Series Forecasting with Conditional Guided Flow Matching

Speckle2Self: Self-Supervised Ultrasound Speckle Reduction Without Clean Data

LIRA: Inferring Segmentation in Large Multi-modal Models with Local Interleaved Region Assistance

Addressing The Devastating Effects Of Single-Task Data Poisoning In Exemplar-Free Continual Learning

Foundation versus Domain-specific Models: Performance Comparison, Fusion, and Explainability in Face Recognition

Probabilistic Optimality for Inference-time Scaling

ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation

Exploring Adapter Design Tradeoffs for Low Resource Music Generation

CycleDistill: Bootstrapping Machine Translation using LLMs with Cyclical Distillation

Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017

Robust Behavior Cloning Via Global Lipschitz Regularization

Granular-Ball-Induced Multiple Kernel K-Means

DRAMA-X: A Fine-grained Intent Prediction and Risk Reasoning Benchmark For Driving

MMET: A Multi-Input and Multi-Scale Transformer for Efficient PDEs Solving

A Two-stage Optimization Method for Wide-range Single-electron Quantum Magnetic Sensing

Physics-Informed Teleconnection-Aware Transformer for Global Subseasonal-to-Seasonal Forecasting

AI-Generated Compromises for Coalition Formation

MLOps with Microservices: A Case Study on the Maritime Domain

Winner-takes-all for Multivariate Probabilistic Time Series Forecasting

Leaps Beyond the Seen: Reinforced Reasoning Augmented Generation for Clinical Notes

Learning to Diagnose Privately: DP-Powered LLMs for Radiology Report Classification

HERGC: Heterogeneous Experts Representation and Generative Completion for Multimodal Knowledge Graphs

Verbal Werewolf: Engage Users with Verbalized Agentic Werewolf Game Framework

MaCP: Minimal yet Mighty Adaptation via Hierarchical Cosine Projection

CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning

FP4 All the Way: Fully Quantized Training of LLMs

Improving LLM Outputs Against Jailbreak Attacks with Expert Model Integration

Extracting Probabilistic Knowledge from Large Language Models for Bayesian Network Parameterization

RIDGECUT: Learning Graph Partitioning with Rings and Wedges

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

Can LLM-based Financial Investing Strategies Outperform the Market in Long Run?

A Multimodal Deep Learning Approach for White Matter Shape Prediction in Diffusion MRI Tractography

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation

Bidirectional Hierarchical Protein Multi-Modal Representation Learning

How Post-Training Reshapes LLMs: A Mechanistic View on Knowledge, Truthfulness, Refusal, and Confidence

$\mu$KE: Matryoshka Unstructured Knowledge Editing of Large Language Models

Learning 3D-Gaussian Simulators from RGB Videos

Learning Adaptive Dexterous Grasping from Single Demonstrations

A Theory of Learning with Autoregressive Chain of Thought

FunGraph: Functionality Aware 3D Scene Graphs for Language-Prompted Scene Interaction

From Reusing to Forecasting: Accelerating Diffusion Models with TaylorSeers

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

Collective Reasoning Among LLMs: A Framework for Answer Validation Without Ground Truth

Advancing AI-Powered Medical Image Synthesis: Insights from MedVQA-GI Challenge Using CLIP, Fine-Tuned Stable Diffusion, and Dream-Booth + LoRA

Predicting Depression in Screening Interviews from Interactive Multi-Theme Collaboration

Schema-Guided Scene-Graph Reasoning based on Multi-Agent Large Language Model System

MQuant: Unleashing the Inference Potential of Multimodal Large Language Models via Full Static Quantization

Mitigating Traffic Oscillations in Mixed Traffic Flow with Scalable Deep Koopman Predictive Control

Improving Your Model Ranking on Chatbot Arena by Vote Rigging

FIT-Print: Towards False-claim-resistant Model Ownership Verification via Targeted Fingerprint

Softplus Attention with Re-weighting Boosts Length Extrapolation in Large Language Models

Ehrenfeucht-Haussler Rank and Chain of Thought

WebWalker: Benchmarking LLMs in Web Traversal

Generative AI for Cel-Animation: A Survey

Toward Intelligent and Secure Cloud: Large Language Model Empowered Proactive Defense

MomentMix Augmentation with Length-Aware DETR for Temporally Robust Moment Retrieval

POEX: Towards Policy Executable Jailbreak Attacks Against the LLM-based Robots

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens

LoRA.rar: Learning to Merge LoRAs via Hypernetworks for Subject-Style Conditioned Image Generation

Understanding and Mitigating Memorization in Generative Models via Sharpness of Probability Landscapes

Steering AI-Driven Personalization of Scientific Text for General Audiences

Zero-Shot Voice Conversion via Content-Aware Timbre Ensemble and Conditional Flow Matching

EfficientEQA: An Efficient Approach to Open-Vocabulary Embodied Question Answering

UoMo: A Universal Model of Mobile Traffic Forecasting for Wireless Network Optimization

MaCP: Minimal yet Mighty Adaptation via Hierarchical Cosine Projection

Exploring Spatial Representation to Enhance LLM Reasoning in Aerial Vision-Language Navigation

A Closer Look at Machine Unlearning for Large Language Models

In-Situ Fine-Tuning of Wildlife Models in IoT-Enabled Camera Traps for Efficient Adaptation

EEG-Language Pretraining for Highly Label-Efficient Clinical Phenotyping

A Practice of Post-Training on Llama-3 70B with Optimal Selection of Additional Language Mixture Ratio

Reward-Directed Score-Based Diffusion Models via q-Learning

Chain of Thought Still Thinks Fast: APriCoT Helps with Thinking Slow

A Survey on Model MoErging: Recycling and Routing Among Specialized Experts for Collaborative Learning

AI-AI Bias: large language models favor communications generated by large language models

LVBench: An Extreme Long Video Understanding Benchmark

From Spikes to Heavy Tails: Unveiling the Spectral Evolution of Neural Networks

Fractured Glass, Failing Cameras: Simulating Physics-Based Adversarial Samples for Autonomous Driving Systems

Runtime Monitoring and Enforcement of Conditional Fairness in Generative AIs

On the Sample Efficiency of Abstractions and Potential-Based Reward Shaping in Reinforcement Learning

GenoMAS: A Multi-Agent Framework for Scientific Discovery via Code-Driven Gene Expression Analysis

Created by

Haebom

저자

Haoyang Liu, Yijiang Li, Haohan Wang

개요

GenoMAS는 대규모 반정형 전사체 데이터 분석의 어려움을 해결하기 위해, LLM 기반의 6개 전문 에이전트 팀을 활용하는 새로운 접근법을 제시하는 논문입니다. 각 에이전트는 타입화된 메시지 전달 프로토콜을 통해 상호 작용하며, 가이드형 계획 프레임워크를 통해 작업 단위를 생성하고 상황에 맞게 계획을 조정합니다. GenoTEX 벤치마크에서 기존 최고 성능 대비 데이터 전처리(Composite Similarity Correlation 89.13%, 10.61% 향상) 및 유전자 식별(F1 score 60.48%, 16.85% 향상) 성능 향상을 보였으며, 생물학적으로 타당한 유전자-표현형 연관성을 제시합니다.

시사점, 한계점

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시사점:

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LLM 기반의 분산 에이전트 시스템을 활용하여 복잡한 생물정보학 문제를 효과적으로 해결할 수 있음을 보여줌.

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기존 자동화 방식의 한계를 극복하고, 유연성과 정확성을 동시에 확보 가능.

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GenoTEX 벤치마크에서 우수한 성능을 통해 GenoMAS의 효율성과 정확성 검증.

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생물학적으로 타당한 결과 도출 및 문헌 검증을 통한 신뢰성 확보.

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한계점:

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GenoMAS의 성능 평가가 특정 벤치마크(GenoTEX)에 국한됨. 다양한 데이터셋 및 벤치마크에 대한 추가적인 평가 필요.

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LLM 기반 시스템의 해석력 및 신뢰성에 대한 추가적인 연구 필요.

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실제 생물학적 문제에 대한 적용 및 검증이 추가적으로 필요.

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LLM 에이전트 간의 상호 작용 및 의사결정 과정에 대한 투명성 확보 필요.

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