Daily Arxiv

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Knockout LLM Assessment: Using Large Language Models for Evaluations through Iterative Pairwise Comparisons

MambaNeXt-YOLO: A Hybrid State Space Model for Real-time Object Detection

Confidence-Guided Human-AI Collaboration: Reinforcement Learning with Distributional Proxy Value Propagation for Autonomous Driving

FOLIAGE: Towards Physical Intelligence World Models Via Unbounded Surface Evolution

UniWorld-V1: High-Resolution Semantic Encoders for Unified Visual Understanding and Generation

Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds

DGMO: Training-Free Audio Source Separation through Diffusion-Guided Mask Optimization

EvaLearn: Quantifying the Learning Capability and Efficiency of LLMs via Sequential Problem Solving

Flexiffusion: Training-Free Segment-Wise Neural Architecture Search for Efficient Diffusion Models

Psi-Sampler: Initial Particle Sampling for SMC-Based Inference-Time Reward Alignment in Score Models

NTPP: Generative Speech Language Modeling for Dual-Channel Spoken Dialogue via Next-Token-Pair Prediction

Linear Representation Transferability Hypothesis: Leveraging Small Models to Steer Large Models

Aligned but Blind: Alignment Increases Implicit Bias by Reducing Awareness of Race

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

Train One Sparse Autoencoder Across Multiple Sparsity Budgets to Preserve Interpretability and Accuracy

ReCalKV: Low-Rank KV Cache Compression via Head Reordering and Offline Calibration

ADG: Ambient Diffusion-Guided Dataset Recovery for Corruption-Robust Offline Reinforcement Learning

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

Breaking the Cloak! Unveiling Chinese Cloaked Toxicity with Homophone Graph and Toxic Lexicon

Inclusive, Differentially Private Federated Learning for Clinical Data

Position is Power: System Prompts as a Mechanism of Bias in Large Language Models (LLMs)

Electrolyzers-HSI: Close-Range Multi-Scene Hyperspectral Imaging Benchmark Dataset

In-context Language Learning for Endangered Languages in Speech Recognition

Rethinking Text-based Protein Understanding: Retrieval or LLM?

DreamPRM: Domain-Reweighted Process Reward Model for Multimodal Reasoning

Deconstructing Obfuscation: A four-dimensional framework for evaluating Large Language Models assembly code deobfuscation capabilities

STOPA: A Database of Systematic VariaTion Of DeePfake Audio for Open-Set Source Tracing and Attribution

FlowCut: Rethinking Redundancy via Information Flow for Efficient Vision-Language Models

Deriving Strategic Market Insights with Large Language Models: A Benchmark for Forward Counterfactual Generation

The Role of Diversity in In-Context Learning for Large Language Models

MetaGen Blended RAG: Unlocking Zero-Shot Precision for Specialized Domain Question-Answering

Bottlenecked Transformers: Periodic KV Cache Abstraction for Generalised Reasoning

AceReason-Nemotron: Advancing Math and Code Reasoning through Reinforcement Learning

SUS backprop: linear backpropagation algorithm for long inputs in transformers

Optimizing Anytime Reasoning via Budget Relative Policy Optimization

Information Science Principles of Machine Learning: A Causal Chain Meta-Framework Based on Formalized Information Mapping

MiMo: Unlocking the Reasoning Potential of Language Model -- From Pretraining to Posttraining

Piloting Structure-Based Drug Design via Modality-Specific Optimal Schedule

Full-Parameter Continual Pretraining of Gemma2: Insights into Fluency and Domain Knowledge

AGENTFUZZER: Generic Black-Box Fuzzing for Indirect Prompt Injection against LLM Agents

Rethinking LLM Advancement: Compute-Dependent and Independent Paths to Progress

Addressing Concept Mislabeling in Concept Bottleneck Models Through Preference Optimization

Goal-Oriented Time-Series Forecasting: Foundation Framework Design

Biased by Design: Leveraging Inherent AI Biases to Enhance Critical Thinking of News Readers

Hearing Anywhere in Any Environment

NorEval: A Norwegian Language Understanding and Generation Evaluation Benchmark

LLM Social Simulations Are a Promising Research Method

Firm or Fickle? Evaluating Large Language Models Consistency in Sequential Interactions

COMI-LINGUA: Expert Annotated Large-Scale Dataset for Multitask NLP in Hindi-English Code-Mixing

On The Sample Complexity Bounds In Bilevel Reinforcement Learning

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

ChatWise: A Strategy-Guided Chatbot for Enhancing Cognitive Support in Older Adults

Implicit Cross-Lingual Rewarding for Efficient Multilingual Preference Alignment

Knowledge Retention for Continual Model-Based Reinforcement Learning

When Claims Evolve: Evaluating and Enhancing the Robustness of Embedding Models Against Misinformation Edits

ATLaS: Agent Tuning via Learning Critical Steps

Safety Tax: Safety Alignment Makes Your Large Reasoning Models Less Reasonable

From Benign import Toxic: Jailbreaking the Language Model via Adversarial Metaphors

TurboFuzzLLM: Turbocharging Mutation-based Fuzzing for Effectively Jailbreaking Large Language Models in Practice

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

Contrastive Visual Data Augmentation

SNaRe: Domain-aware Data Generation for Low-Resource Event Detection

AnyTop: Character Animation Diffusion with Any Topology

Towards Robust ESG Analysis Against Greenwashing Risks: Aspect-Action Analysis with Cross-Category Generalization

GoRA: Gradient-driven Adaptive Low Rank Adaptation

Mind the Confidence Gap: Overconfidence, Calibration, and Distractor Effects in Large Language Models

Bandit Multiclass List Classification

CurvGAD: Leveraging Curvature for Enhanced Graph Anomaly Detection

Can Large Language Models Understand Intermediate Representations in Compilers?

Contrastive Representation Distillation via Multi-Scale Feature Decoupling

Blackout DIFUSCO

Multiple Invertible and Partial-Equivariant Function for Latent Vector Transformation to Enhance Disentanglement in VAEs

Analytical Lyapunov Function Discovery: An RL-based Generative Approach

Explainability in Practice: A Survey of Explainable NLP Across Various Domains

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

Spectro-Riemannian Graph Neural Networks

The Lessons of Developing Process Reward Models in Mathematical Reasoning

Efficiently Serving Large Multimodal Models Using EPD Disaggregation

Hybrid deep convolution model for lung cancer detection with transfer learning

Uncovering Memorization Effect in the Presence of Spurious Correlations

OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Visual Text Localization and Reasoning

An analytic theory of creativity in convolutional diffusion models

HashEvict: A Pre-Attention KV Cache Eviction Strategy using Locality-Sensitive Hashing

From Intention To Implementation: Automating Biomedical Research via LLMs

Proactive Model Adaptation Against Concept Drift for Online Time Series Forecasting

MuLan: Adapting Multilingual Diffusion Models for Hundreds of Languages with Negligible Cost

Functional relevance based on the continuous Shapley value

Empower Structure-Based Molecule Optimization with Gradient Guided Bayesian Flow Networks

What Happened in LLMs Layers when Trained for Fast vs. Slow Thinking: A Gradient Perspective

Focus On This, Not That! Steering LLMs with Adaptive Feature Specification

A Large Recurrent Action Model: xLSTM enables Fast Inference for Robotics Tasks

David and Goliath: Small One-step Model Beats Large Diffusion with Score Post-training

Context is Key: A Benchmark for Forecasting with Essential Textual Information

Diff-Instruct++: Training One-step Text-to-image Generator Model to Align with Human Preferences

Beyond Position: the emergence of wavelet-like properties in Transformers

The Disparate Benefits of Deep Ensembles

Not All Options Are Created Equal: Textual Option Weighting for Token-Efficient LLM-Based Knowledge Tracing

Evaluating Morphological Compositional Generalization in Large Language Models

Stein Variational Evolution Strategies

Biased AI can Influence Political Decision-Making

Random-key genetic algorithms: Principles and applications

Created by

Haebom

저자

Mariana A. Londe, Luciana S. Pessoa, Carlos E. Andrade, Jose F. Gon\c{c}alves, Mauricio G. C. Resende

개요

무작위 키 유전 알고리즘(Random-Key Genetic Algorithm, RKGA)은 이산 및 전역 최적화를 위한 진화형 메타휴리스틱 기법이다. 각 해는 [0, 1) 구간에서 무작위로 생성된 실수인 N개의 무작위 키 벡터로 인코딩된다. 디코더는 각 무작위 키 벡터를 최적화 문제의 해로 매핑하고 비용을 계산한다. 이 접근 방식의 장점은 해결 중인 문제와 상관없이 모든 유전 연산자와 변환을 단위 초입방체 내에서 유지할 수 있다는 것이다. 이는 핵심 프레임워크의 생산성과 유지 관리를 향상시킨다. 알고리즘은 P개의 무작위 키 벡터 집단으로 시작한다. 각 반복에서 벡터는 두 집합, 즉 더 작은 고가치 엘리트 해 집합과 나머지 비엘리트 해 집합으로 분할된다. 모든 엘리트 요소는 변경 없이 다음 집단에 복사된다. 소수의 무작위 키 벡터(돌연변이체)가 다음 반복의 집단에 추가된다. 다음 반복의 집단의 나머지 요소는 Spears와 DeJong (1991)의 매개변수화된 균일 교차를 사용하여 솔루션 쌍을 결합하여 생성된다. 본 장에서는 무작위 키 유전 알고리즘을 검토하고 편향된 무작위 키 유전 알고리즘이라는 효과적인 변형을 설명한다.

시사점, 한계점

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시사점: 단위 초입방체 내에서 모든 연산을 수행하여 문제 해결 과정의 생산성과 유지보수를 향상시킬 수 있다. 편향된 무작위 키 유전 알고리즘을 통해 알고리즘의 효율성을 높일 수 있는 가능성을 제시한다.

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한계점: 알고리즘의 성능은 매개변수 (예: 엘리트 집합의 크기, 돌연변이 비율, 교차 방식)에 민감하며, 최적의 매개변수 설정은 문제에 따라 달라질 수 있다. 디코더의 설계가 최적화 문제 해결에 중요한 영향을 미치며, 효율적인 디코더 설계가 필요하다. 특정 문제에 대한 적용성 및 일반화 가능성에 대한 추가적인 연구가 필요하다.

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