Daily Arxiv

전 세계에서 발간되는 인공지능 관련 논문을 정리하는 페이지 입니다.
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Exploring Diffusion Transformer Designs via Grafting

Teaming in the AI Era: AI-Augmented Frameworks for Forming, Simulating, and Optimizing Human Teams

ECoRAG: Evidentiality-guided Compression for Long Context RAG

Dissecting Bias in LLMs: A Mechanistic Interpretability Perspective

Does It Make Sense to Speak of Introspection in Large Language Models?

Sparse Autoencoders, Again?

Feature-Based Lie Group Transformer for Real-World Applications

TracLLM: A Generic Framework for Attributing Long Context LLMs

SemiOccam: A Robust Semi-Supervised Image Recognition Network Using Sparse Labels

Labelling Data with Unknown References

Tug-of-war between idiom's figurative and literal meanings in LLMs

State-Covering Trajectory Stitching for Diffusion Planners

Deep Learning Weather Models for Subregional Ocean Forecasting: A Case Study on the Canary Current Upwelling System

DORAEMON: Decentralized Ontology-aware Reliable Agent with Enhanced Memory Oriented Navigation

Subspecialty-Specific Foundation Model for Intelligent Gastrointestinal Pathology

RepoMaster: Autonomous Exploration and Understanding of GitHub Repositories for Complex Task Solving

An Uncertainty-Aware ED-LSTM for Probabilistic Suffix Prediction

SageAttention2++: A More Efficient Implementation of SageAttention2

Autocomp: LLM-Driven Code Optimization for Tensor Accelerators

IDA-Bench: Evaluating LLMs on Interactive Guided Data Analysis

Decoupling Representation and Learning in Genetic Programming: the LaSER Approach

Common Data Format (CDF): A Standardized Format for Match-Data in Football (Soccer)

Web Intellectual Property at Risk: Preventing Unauthorized Real-Time Retrieval by Large Language Models

How can Diffusion Models Evolve into Continual Generators?

Open Your Eyes: Vision Enhances Message Passing Neural Networks in Link Prediction

m-KAILIN: Knowledge-Driven Agentic Scientific Corpus Distillation Framework for Biomedical Large Language Models Training

FinSage: A Multi-aspect RAG System for Financial Filings Question Answering

Not All Rollouts are Useful: Down-Sampling Rollouts in LLM Reinforcement Learning

LauraTSE: Target Speaker Extraction using Auto-Regressive Decoder-Only Language Models

Reasoning Towards Fairness: Mitigating Bias in Language Models through Reasoning-Guided Fine-Tuning

Sparse Autoencoders Learn Monosemantic Features in Vision-Language Models

Multivariate Temporal Regression at Scale: A Three-Pillar Framework Combining ML, XAI, and NLP

GENIUS: A Generative Framework for Universal Multimodal Search

TinySQL: A Progressive Text-to-SQL Dataset for Mechanistic Interpretability Research

ARMOR: Empowering Multimodal Understanding Model with Interleaved Multimodal Generation Capability

A Survey on Sparse Autoencoders: Interpreting the Internal Mechanisms of Large Language Models

Knowledge Retention for Continual Model-Based Reinforcement Learning

Adversarial Tokenization

UDora: A Unified Red Teaming Framework against LLM Agents by Dynamically Hijacking Their Own Reasoning

SAGE: A Framework of Precise Retrieval for RAG

Graph Attention Networks Unleashed: A Fast and Explainable Vulnerability Assessment Framework for Microgrids

SafeAuto: Knowledge-Enhanced Safe Autonomous Driving with Multimodal Foundation Models

Emergent Symbolic Mechanisms Support Abstract Reasoning in Large Language Models

Improving Customer Service with Automatic Topic Detection in User Emails

DualSpec: Text-to-spatial-audio Generation via Dual-Spectrogram Guided Diffusion Model

Jacobian Sparse Autoencoders: Sparsify Computations, Not Just Activations

SpargeAttention: Accurate and Training-free Sparse Attention Accelerating Any Model Inference

MimeQA: Towards Socially-Intelligent Nonverbal Foundation Models

A Comprehensive Survey on Concept Erasure in Text-to-Image Diffusion Models

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws

Maximum Entropy Reinforcement Learning with Diffusion Policy

TituLLMs: A Family of Bangla LLMs with Comprehensive Benchmarking

Relational Conformal Prediction for Correlated Time Series

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models

UniDB: A Unified Diffusion Bridge Framework via Stochastic Optimal Control

The Complexity of Learning Sparse Superposed Features with Feedback

Peri-LN: Revisiting Normalization Layer in the Transformer Architecture

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods

An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus

ProofAug: Efficient Neural Theorem Proving via Fine-grained Proof Structure Analysis

FDLLM: A Dedicated Detector for Black-Box LLMs Fingerprinting

The Bakers and Millers Game with Restricted Locations

Diving into Self-Evolving Training for Multimodal Reasoning

Reasoning Through Execution: Unifying Process and Outcome Rewards for Code Generation

A Riemannian Optimization Perspective of the Gauss-Newton Method for Feedforward Neural Networks

CoopetitiveV: Leveraging LLM-powered Coopetitive Multi-Agent Prompting for High-quality Verilog Generation

TraceVLA: Visual Trace Prompting Enhances Spatial-Temporal Awareness for Generalist Robotic Policies

The Synergy of LLMs & RL Unlocks Offline Learning of Generalizable Language-Conditioned Policies with Low-fidelity Data

Understanding Memorization in Generative Models via Sharpness in Probability Landscapes

A Cognac shot to forget bad memories: Corrective Unlearning in GNNs

Fundamental Limits of Prompt Tuning Transformers: Universality, Capacity and Efficiency

Who Can Withstand Chat-Audio Attacks? An Evaluation Benchmark for Large Audio-Language Models

CLIPErase: Efficient Unlearning of Visual-Textual Associations in CLIP

The Impact of Inference Acceleration on Bias of LLMs

pLDDT-Predictor: High-speed Protein Screening Using Transformer and ESM2

Simmering: Sufficient is better than optimal for training neural networks

PoisonBench: Assessing Large Language Model Vulnerability to Data Poisoning

Efficient Fine-Grained Guidance for Diffusion Model Based Symbolic Music Generation

AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML

VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters

Deconfounding Multi-Cause Latent Confounders: A Factor-Model Approach to Climate Model Bias Correction

DyGMamba: Efficiently Modeling Long-Term Temporal Dependency on Continuous-Time Dynamic Graphs with State Space Models

Proximal Policy Distillation

BoA: Attention-aware Post-training Quantization without Backpropagation

Certification for Differentially Private Prediction in Gradient-Based Training

Multi-Agent Collaboration via Cross-Team Orchestration

LlavaGuard: An Open VLM-based Framework for Safeguarding Vision Datasets and Models

Computational Limits of Low-Rank Adaptation (LoRA) Fine-Tuning for Transformer Models

Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network models

Mirage: A Multi-Level Superoptimizer for Tensor Programs

Multidimensional Adaptive Coefficient for Inference Trajectory Optimization in Flow and Diffusion

Longitudinal Targeted Minimum Loss-based Estimation with Temporal-Difference Heterogeneous Transformer

TMT: Tri-Modal Translation between Speech, Image, and Text by Processing Different Modalities as Different Languages

Structure Guided Large Language Model for SQL Generation

GraphGPT: Generative Pre-trained Graph Eulerian Transformer

Graph Deep Learning for Time Series Forecasting

Diffusion Policies for Out-of-Distribution Generalization in Offline Reinforcement Learning

Just Enough Thinking: Efficient Reasoning with Adaptive Length Penalties Reinforcement Learning

Rethinking Machine Unlearning in Image Generation Models

The Coming Crisis of Multi-Agent Misalignment: AI Alignment Must Be a Dynamic and Social Process

Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network models

Created by

Haebom

저자

Lun Ai, Stephen H. Muggleton, Shi-Shun Liang, Geoff S. Baldwin

개요

본 논문은 유전자 조작 세포의 행동 예측에 어려움을 겪는 유전체 규모 대사 네트워크 모델(GEM)의 한계를 극복하기 위해, 부울 행렬 논리 프로그래밍(BMLP) 기반의 새로운 시스템인 $BMLP_{active}$를 제시합니다. $BMLP_{active}$는 해석 가능한 논리 프로그램을 사용하여 모델 박테리아 유기체의 최첨단 GEM을 인코딩하고, 비용 효율적인 실험을 유도하여 유전자 상호작용을 학습합니다. 이는 제한된 훈련 예시만으로도 유전자 쌍 간의 상호작용을 성공적으로 학습하여 실험 설계 공간 증가 문제를 해결하며, 유용한 화합물을 생산하는 생물 시스템의 신뢰할 수 있는 엔지니어링을 위한 대사 모델의 빠른 최적화를 가능하게 합니다. 궁극적으로, 생물학적 발견을 위한 자율 주행 실험실 구축을 위한 현실적인 접근 방식을 제공합니다.

시사점, 한계점

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시사점:

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부울 행렬 논리 프로그래밍(BMLP)을 이용하여 GEM의 예측 정확도를 향상시키는 새로운 방법 제시.

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$BMLP_{active}$ 시스템을 통해 비용 효율적인 실험 설계 및 유전자 상호작용 학습 가능.

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제한된 데이터로도 효과적인 학습 가능성을 보여줌.

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생물학적 시스템 엔지니어링 및 유용한 화합물 생산 최적화에 기여.

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자율 주행 실험실 구축을 위한 현실적인 접근 방식 제시.

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한계점:

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$BMLP_{active}$의 성능이 다른 기계 학습 방법론과 비교 분석되지 않음.

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모델 박테리아 유기체에 대한 결과를 다른 유기체로 일반화할 수 있는지에 대한 검증 필요.

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실제 자율 주행 실험실 구축 및 운영에 대한 구체적인 계획이나 실험적 증거 부족.

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복잡한 생물학적 시스템의 모든 상호작용을 정확하게 모델링하는 데 대한 한계 존재 가능성.

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