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.

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

Sleepless Nights, Sugary Days: Creating Synthetic Users with Health Conditions for Realistic Coaching Agent Interactions

Created by

Haebom

저자

Taedong Yun, Eric Yang, Mustafa Safdari, Jong Ha Lee, Vaishnavi Vinod Kumar, S. Sara Mahdavi, Jonathan Amar, Derek Peyton, Reut Aharony, Andreas Michaelides, Logan Schneider, Isaac Galatzer-Levy, Yugang Jia, John Canny, Arthur Gretton, Maja Mataric

개요

본 논문은 건강 및 라이프스타일 코칭과 같이 긍정적인 행동 변화를 장려하도록 설계된 대화형 에이전트를 평가하기 위한 합성 사용자 생성을 위한 엔드투엔드 프레임워크를 제시합니다. 이 연구에서는 수면 및 당뇨병 관리에 중점을 두고, 현실적인 상호 작용을 보장하기 위해 건강 및 라이프스타일 조건에 기반한 합성 사용자를 생성합니다. 합성 사용자는 두 단계로 생성됩니다. 첫째, 기본 인구 통계 및 행동 특성 외에도 실제 건강 및 라이프스타일 요소를 기반으로 구조화된 데이터를 생성합니다. 둘째, 구조화된 데이터를 조건으로 합성 사용자의 전체 프로필을 개발합니다. 합성 사용자와 코칭 에이전트 간의 상호 작용은 Concordia와 같은 생성 에이전트 기반 모델을 사용하거나 언어 모델을 프롬프트하여 직접 시뮬레이션합니다. 수면 및 당뇨병 코칭을 위한 두 개의 독립적으로 개발된 에이전트를 사례 연구로 사용하여, 코칭 에이전트의 합성 사용자의 필요와 과제에 대한 이해를 분석함으로써 이 프레임워크의 유효성을 입증합니다. 마지막으로, 인간 전문가에 의한 사용자-코치 상호 작용에 대한 여러 차례의 맹검 평가를 통해, 건강 및 행동 특성이 있는 합성 사용자가 해당 특성을 가진 실제 사용자를 더 정확하게 묘사한다는 것을 보여줍니다. 제안된 프레임워크는 광범위하고 현실적이며 기반이 있는 시뮬레이션된 상호 작용을 통해 대화형 에이전트의 효율적인 개발을 위한 기반을 마련합니다.

시사점, 한계점

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시사점:

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건강 및 라이프스타일 코칭 에이전트의 효과적인 평가를 위한 새로운 프레임워크 제시

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실제 사용자와 유사한 합성 사용자 생성을 통한 현실적인 시뮬레이션 환경 구축

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에이전트 개발 과정의 효율성 증대 및 개발 비용 절감

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맹검 평가를 통한 합성 사용자의 유효성 검증

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한계점:

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현재 수면 및 당뇨병 관리에 국한된 사례 연구로, 다른 영역으로의 일반화 가능성 검토 필요

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사용된 생성 모델(Concordia 또는 언어 모델)의 한계가 합성 사용자의 현실성에 영향을 미칠 수 있음

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인간 전문가의 평가에 대한 주관성이 결과에 영향을 줄 수 있음

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합성 사용자 생성에 사용된 데이터의 편향이 결과에 영향을 미칠 수 있음

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