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.

On the Brittleness of CLIP Text Encoders

NVIDIA Nemotron Nano V2 VL

Improving Diagnostic Performance on Small and Imbalanced Datasets Using Class-Based Input Image Composition

Retrofitters, pragmatists and activists: Public interest litigation for accountable automated decision-making

Text to Robotic Assembly of Multi Component Objects using 3D Generative AI and Vision Language Models

The Future of Generative AI in Software Engineering: A Vision from Industry and Academia in the European GENIUS Project

AURA: A Reinforcement Learning Framework for AI-Driven Adaptive Conversational Surveys

Grounded in Reality: Learning and Deploying Proactive LLM from Offline Logs

USF-MAE: Ultrasound Self-Supervised Foundation Model with Masked Autoencoding

CompressionAttack: Exploiting Prompt Compression as a New Attack Surface in LLM-Powered Agents

Every Activation Boosted: Scaling General Reasoner to 1 Trillion Open Language Foundation

Enterprise Deep Research: Steerable Multi-Agent Deep Research for Enterprise Analytics

What Can String Probability Tell Us About Grammaticality?

Fine-Tuning MedGemma for Clinical Captioning to Enhance Multimodal RAG over Malaysia CPGs

Learning to Navigate Socially Through Proactive Risk Perception

Policy-as-Prompt: Turning AI Governance Rules into Guardrails for AI Agents

A Mega-Study of Digital Twins Reveals Strengths, Weaknesses and Opportunities for Further Improvement

Amortized Latent Steering: Low-Cost Alternative to Test-Time Optimization

Amulet: a Python Library for Assessing Interactions Among ML Defenses and Risks

LimiX: Unleashing Structured-Data Modeling Capability for Generalist Intelligence

AI Agentic Vulnerability Injection And Transformation with Optimized Reasoning

What Matters in Data for DPO?

Chain-of-Query: Unleashing the Power of LLMs in SQL-Aided Table Understanding via Multi-Agent Collaboration

Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving

Advanced Hybrid Transformer LSTM Technique with Attention and TS Mixer for Drilling Rate of Penetration Prediction

Learning Dynamics of Meta-Learning in Small Model Pretraining

GAITEX: Human motion dataset of impaired gait and rehabilitation exercises using inertial and optical sensors

To Trust or Not to Trust: On Calibration in ML-based Resource Allocation for Wireless Networks

NMIXX: Domain-Adapted Neural Embeddings for Cross-Lingual eXploration of Finance

ZERO: Industry-ready Vision Foundation Model with Multi-modal Prompts

Conformal Information Pursuit for Interactively Guiding Large Language Models

Less Greedy Equivalence Search

Progressive Inference-Time Annealing of Diffusion Models for Sampling from Boltzmann Densities

Know What You Don't Know: Uncertainty Calibration of Process Reward Models

Conformal Prediction Adaptive to Unknown Subpopulation Shifts

ProRefine: Inference-Time Prompt Refinement with Textual Feedback

Learning of Population Dynamics: Inverse Optimization Meets JKO Scheme

RCCDA: Adaptive Model Updates in the Presence of Concept Drift under a Constrained Resource Budget

Optimizing Anytime Reasoning via Budget Relative Policy Optimization

Taskmaster Deconstructed: A Quantitative Look at Tension, Volatility, and Viewer Ratings

XBreaking: Understanding how LLMs security alignment can be broken

Pogobot: an Open-Source, Low-Cost Robot for Swarm Robotics and Programmable Active Matter

Large language models as uncertainty-calibrated optimizers for experimental discovery

AIRepr: An Analyst-Inspector Framework for Evaluating Reproducibility of LLMs in Data Science

InterFeedback: Unveiling Interactive Intelligence of Large Multimodal Models via Human Feedback

Analyzing limits for in-context learning

Activation-Informed Merging of Large Language Models

iTool: Reinforced Fine-Tuning with Dynamic Deficiency Calibration for Advanced Tool Use

MMDocIR: Benchmarking Multimodal Retrieval for Long Documents

Cognitive Edge Computing: A Comprehensive Survey on Optimizing Large Models and AI Agents for Pervasive Deployment

TOBUGraph: Knowledge Graph-Based Retrieval for Enhanced LLM Performance Beyond RAG

CTPD: Cross-Modal Temporal Pattern Discovery for Enhanced Multimodal Electronic Health Records Analysis

Affordance-based Robot Manipulation with Flow Matching

FunOTTA: On-the-Fly Adaptation on Cross-Domain Fundus Image via Stable Test-time Training

Tactical Decision Making for Autonomous Trucks by Deep Reinforcement Learning with Total Cost of Operation Based Reward

Characterizing the Training Dynamics of Private Fine-tuning with Langevin diffusion

Learning for Interval Prediction of Electricity Demand: A Cluster-based Bootstrapping Approach

The Less Intelligent the Elements, the More Intelligent the Whole. Or, Possibly Not?

Monitor-Generate-Verify (MGV): Formalising Metacognitive Theory for Language Model Reasoning

Outbidding and Outbluffing Elite Humans: Mastering Liar's Poker via Self-Play and Reinforcement Learning

A Proprietary Model-Based Safety Response Framework for AI Agents

Orion-MSP: Multi-Scale Sparse Attention for Tabular In-Context Learning

Inverse Knowledge Search over Verifiable Reasoning: Synthesizing a Scientific Encyclopedia from a Long Chains-of-Thought Knowledge Base

From Observability Data to Diagnosis: An Evolving Multi-agent System for Incident Management in Cloud Systems

How Do AI Agents Do Human Work? Comparing AI and Human Workflows Across Diverse Occupations

String Seed of Thought: Prompting LLMs for Distribution-Faithful and Diverse Generation

HugAgent: Benchmarking LLMs for Simulation of Individualized Human Reasoning

Internal World Models as Imagination Networks in Cognitive Agents

Open Agent Specification (Agent Spec): A Unified Representation for AI Agents

Bilinear relational structure fixes reversal curse and enables consistent model editing

Introducing LongCat-Flash-Thinking: A Technical Report

AI Through the Human Lens: Investigating Cognitive Theories in Machine Psychology

Wider or Deeper? Scaling LLM Inference-Time Compute with Adaptive Branching Tree Search

Retrieval Augmented Diffusion Model for Structure-informed Antibody Design and Optimization

Joint Verification and Refinement of Language Models for Safety-Constrained Planning

TimeSearch-R: Adaptive Temporal Search for Long-Form Video Understanding via Self-Verification Reinforcement Learning

DGTN: Graph-Enhanced Transformer with Diffusive Attention Gating Mechanism for Enzyme DDG Prediction

On Flow Matching KL Divergence

AI Literacy Assessment Revisited: A Task-Oriented Approach Aligned with Real-world Occupations

SWE-Compass: Towards Unified Evaluation of Agentic Coding Abilities for Large Language Models

Self-adaptive weighting and sampling for physics-informed neural networks

APP: Accelerated Path Patching with Task-Specific Pruning

"I Like That You Have to Poke Around": Instructors on How Experiential Approaches to AI Literacy Spark Inquiry and Critical Thinking

ProDER: A Continual Learning Approach for Fault Prediction in Evolving Smart Grids

Multi-modal Loop Closure Detection with Foundation Models in Severely Unstructured Environments

Robust Neural Audio Fingerprinting using Music Foundation Models

Sample Complexity of Distributionally Robust Off-Dynamics Reinforcement Learning with Online Interaction

AI Assisted AR Assembly: Object Recognition and Computer Vision for Augmented Reality Assisted Assembly

TeaRAG: A Token-Efficient Agentic Retrieval-Augmented Generation Framework

AI Literacy for Community Colleges: Instructors' Perspectives on Scenario-Based and Interactive Approaches to Teaching AI

A multimodal multiplex of the mental lexicon for multilingual individuals

Perceptually Aligning Representations of Music via Noise-Augmented Autoencoders

What Are the Facts? Automated Extraction of Court-Established Facts from Criminal-Court Opinions

Rethinking Metrics and Diffusion Architecture for 3D Point Cloud Generation

LiveStar: Live Streaming Assistant for Real-World Online Video Understanding

DeepEyesV2: Toward Agentic Multimodal Model

TAMAS: Benchmarking Adversarial Risks in Multi-Agent LLM Systems

Integrating Score-Based Diffusion Models with Machine Learning-Enhanced Localization for Advanced Data Assimilation in Geological Carbon Storage

Accelerating Task Generalisation with Multi-Level Skill Hierarchies

Created by

Haebom

저자

Thomas P Cannon, Ozgur Simsek

개요

본 논문은 강화학습 에이전트의 새로운 과제에 대한 효과적인 일반화를 달성하는 다층적 계층적 강화학습 방법인 Fracture Cluster Options (FraCOs)를 제시합니다. FraCOs는 에이전트 행동의 패턴을 식별하고 해당 패턴의 예상되는 미래 유용성을 기반으로 옵션을 형성하여 새로운 과제에 대한 빠른 적응을 가능하게 합니다. 표 형식 환경에서 FraCOs는 효과적인 전이 학습을 보여주며 계층적 깊이가 증가함에 따라 성능이 향상됩니다. 여러 복잡한 절차적으로 생성된 환경에서 최첨단 심층 강화학습 알고리즘과 FraCOs를 비교 평가한 결과, FraCOs는 경쟁 알고리즘보다 분포 내 및 분포 외 성능이 더 높음을 보여줍니다.

시사점, 한계점

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시사점:

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다층적 계층적 강화학습을 통해 새로운 과제에 대한 강화학습 에이전트의 일반화 성능을 향상시킬 수 있음을 보여줍니다.

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FraCOs는 에이전트 행동 패턴의 미래 유용성을 기반으로 옵션을 형성하여 효과적인 전이 학습을 가능하게 합니다.

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복잡한 절차적으로 생성된 환경에서 최첨단 알고리즘보다 우수한 성능을 달성합니다.

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한계점:

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현재는 표 형식 환경과 절차적으로 생성된 환경에서의 성능 평가에 국한되어 있습니다. 실제 세계의 복잡한 환경으로의 확장성에 대한 추가 연구가 필요합니다.

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계층적 깊이가 증가함에 따라 계산 비용이 증가할 수 있습니다. 효율적인 계산 방법에 대한 추가 연구가 필요합니다.

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FraCOs의 일반화 성능이 모든 유형의 과제에 대해 우수한 것은 아닐 수 있습니다. 다양한 유형의 과제에 대한 추가적인 실험이 필요합니다.

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