Daily Arxiv

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On the optimization dynamics of RLVR: Gradient gap and step size thresholds

Synthetic Series-Symbol Data Generation for Time Series Foundation Models

Learning Neural Exposure Fields for View Synthesis

Fewer Weights, More Problems: A Practical Attack on LLM Pruning

Can Lessons From Human Teams Be Applied to Multi-Agent Systems? The Role of Structure, Diversity, and Interaction Dynamics

Haystack Engineering: Context Engineering for Heterogeneous and Agentic Long-Context Evaluation

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations

Online Rubrics Elicitation from Pairwise Comparisons

Scalable multilingual PII annotation for responsible AI in LLMs

Untangling Component Imbalance in Hybrid Linear Attention Conversion Methods

Reinforce-Ada: An Adaptive Sampling Framework for Reinforce-Style LLM Training

Revealing Interconnections between Diseases: from Statistical Methods to Large Language Models

P2P: A Poison-to-Poison Remedy for Reliable Backdoor Defense in LLMs

MLLMEraser: Achieving Test-Time Unlearning in Multimodal Large Language Models through Activation Steering

Why Low-Precision Transformer Training Fails: An Analysis on Flash Attention

Spatiotemporal Forecasting as Planning: A Model-Based Reinforcement Learning Approach with Generative World Models

6G-Enabled Digital Twin Framework for Real-Time Cyber-Physical Systems: An Experimental Validation with Industrial Bearing Fault Detection

InstructPLM-mu: 1-Hour Fine-Tuning of ESM2 Beats ESM3 in Protein Mutation Predictions

Prompt-Aware Scheduling for Low-Latency LLM Serving

Learning Robust Diffusion Models from Imprecise Supervision

CLARITY: Clinical Assistant for Routing, Inference, and Triage

Nav-EE: Navigation-Guided Early Exiting for Efficient Vision-Language Models in Autonomous Driving

Machine Learning for Detection and Analysis of Novel LLM Jailbreaks

Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity

TimeScope: Towards Task-Oriented Temporal Grounding In Long Videos

Editable Noise Map Inversion: Encoding Target-image into Noise For High-Fidelity Image Manipulation

ClustRecNet: A Novel End-to-End Deep Learning Framework for Clustering Algorithm Recommendation

Trust Region Reward Optimization and Proximal Inverse Reward Optimization Algorithm

InfiR2: A Comprehensive FP8 Training Recipe for Reasoning-Enhanced Language Models

Benchmarking and Mitigate Sycophancy in Medical Vision-Language Models

CFDLLMBench: A Benchmark Suite for Evaluating Large Language Models in Computational Fluid Dynamics

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

Robust LLM Training Infrastructure at ByteDance

RPG: A Repository Planning Graph for Unified and Scalable Codebase Generation

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment

Beyond I'm Sorry, I Can't: Dissecting Large Language Model Refusal

Individual utilities of life satisfaction reveal inequality aversion unrelated to political alignment

COMPACT: Common-token Optimized Model Pruning Across Channels and Tokens

From Federated Learning to X-Learning: Breaking the Barriers of Decentrality Through Random Walks

Latent Variable Modeling in Multi-Agent Reinforcement Learning via Expectation-Maximization for UAV-Based Wildlife Protection

Quantifying Label-Induced Bias in Large Language Model Self- and Cross-Evaluations

Federated Fine-Tuning of Sparsely-Activated Large Language Models on Resource-Constrained Devices

On Task Vectors and Gradients

On-Policy RL Meets Off-Policy Experts: Harmonizing Supervised Fine-Tuning and Reinforcement Learning via Dynamic Weighting

LATTE: Learning Aligned Transactions and Textual Embeddings for Bank Clients

ACD-CLIP: Decoupling Representation and Dynamic Fusion for Zero-Shot Anomaly Detection

Neural Beam Field for Spatial Beam RSRP Prediction

AMFT: Aligning LLM Reasoners by Meta-Learning the Optimal Imitation-Exploration Balance

Reasoning through Exploration: A Reinforcement Learning Framework for Robust Function Calling

VAGPO: Vision-augmented Asymmetric Group Preference Optimization for Graph Routing Problems

TriP-LLM: A Tri-Branch Patch-wise Large Language Model Framework for Time-Series Anomaly Detection

Enabling Self-Improving Agents to Learn at Test Time With Human-In-The-Loop Guidance

Preprint: Poster: Did I Just Browse A Website Written by LLMs?

Site-Level Fine-Tuning with Progressive Layer Freezing: Towards Robust Prediction of Bronchopulmonary Dysplasia from Day-1 Chest Radiographs in Extremely Preterm Infants

AirScape: An Aerial Generative World Model with Motion Controllability

Tuning without Peeking: Provable Privacy and Generalization Bounds for LLM Post-Training

EFRame: Deeper Reasoning via Exploration-Filter-Replay Reinforcement Learning Framework

Mem4Nav: Boosting Vision-and-Language Navigation in Urban Environments with a Hierarchical Spatial-Cognition Long-Short Memory System

Quantifying Fairness in LLMs Beyond Tokens: A Semantic and Statistical Perspective

Interpretable and Granular Video-Based Quantification of Motor Characteristics from the Finger Tapping Test in Parkinson Disease

Bures-Wasserstein Flow Matching for Graph Generation

Symmetry in Neural Network Parameter Spaces

CausalVLBench: Benchmarking Visual Causal Reasoning in Large Vision-Language Models

Flattery in Motion: Benchmarking and Analyzing Sycophancy in Video-LLMs

AD-EE: Early Exiting for Fast and Reliable Vision-Language Models in Autonomous Driving

Robustness in Both Domains: CLIP Needs a Robust Text Encoder

Elicit and Enhance: Advancing Multimodal Reasoning in Medical Scenarios

Any-to-Bokeh: Arbitrary-Subject Video Refocusing with Video Diffusion Model

An LLM-as-Judge Metric for Bridging the Gap with Human Evaluation in SE Tasks

FinTagging: Benchmarking LLMs for Extracting and Structuring Financial Information

Beyond Demonstrations: Dynamic Vector Construction from Latent Representations

DDO: Dual-Decision Optimization for LLM-Based Medical Consultation via Multi-Agent Collaboration

Sequential Monte Carlo for Policy Optimization in Continuous POMDPs

Game of Trust: How Trustworthy Does Your Blockchain Think You Are?

Collaborative Unlabeled Data Optimization

Reasoning Large Language Model Errors Arise from Hallucinating Critical Problem Features

System Prompt Optimization with Meta-Learning

Cognitio Emergens: Agency, Dimensions, and Dynamics in Human-AI Knowledge Co-Creation

Multimodal Language Models See Better When They Look Shallower

Exploring human-SAV interaction using LLMs: The impact of psychological factors on user experience

On Developers' Self-Declaration of AI-Generated Code: An Analysis of Practices

Diffusion Generative Recommendation with Continuous Tokens

TARO: Timestep-Adaptive Representation Alignment with Onset-Aware Conditioning for Synchronized Video-to-Audio Synthesis

DeepOHeat-v1: Efficient Operator Learning for Fast and Trustworthy Thermal Simulation and Optimization in 3D-IC Design

Brain2Text Decoding Model Reveals the Neural Mechanisms of Visual Semantic Processing

Issue Localization via LLM-Driven Iterative Code Graph Searching

CCDP: Composition of Conditional Diffusion Policies with Guided Sampling

Measuring directional bias amplification in image captions using predictability

Contrastive Learning Augmented Social Recommendations

WyckoffDiff -- A Generative Diffusion Model for Crystal Symmetry

Solving Linear-Gaussian Bayesian Inverse Problems with Decoupled Diffusion Sequential Monte Carlo

RadVLM: A Multitask Conversational Vision-Language Model for Radiology

IG-MCTS: Human-in-the-Loop Cooperative Navigation under Incomplete Information

OrcaLoca: An LLM Agent Framework for Software Issue Localization

Enabling Population-Level Parallelism in Tree-Based Genetic Programming for GPU Acceleration

AD-LLM: Benchmarking Large Language Models for Anomaly Detection

Preference Discerning with LLM-Enhanced Generative Retrieval

SwarmGPT: Combining Large Language Models with Safe Motion Planning for Drone Swarm Choreography

Generate Any Scene: Scene Graph Driven Data Synthesis for Visual Generation Training

Medchain: Bridging the Gap Between LLM Agents and Clinical Practice with Interactive Sequence

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

Created by

Haebom

저자

Tiany Peng, George Gui, Daniel J. Merlau, Grace Jiarui Fan, Malek Ben Sliman, Melanie Brucks, Eric J. Johnson, Vicki Morwitz, Abdullah Althenayyan, Silvia Bellezza, Dante Donati, Hortense Fong, Elizabeth Friedman, Ariana Guevara, Mohamed Hussein, Kinshuk Jerath, Bruce Kogut, Akshit Kumar, Kristen Lane, Hannah Li, Patryk Perkowski, Oded Netzer, Olivier Toubia

개요

디지털 트윈이 사회 과학 및 의사 결정에 혁신을 가져올 수 있다는 기대와 달리, 실제 사람을 얼마나 잘 모방하는지에 대한 의문이 존재한다. 대표적인 미국 패널과 이들의 디지털 트윈을 대상으로 19개의 사전 등록 연구를 수행하여, 다양한 도메인과 자극에 대한 인간과 디지털 트윈의 행동을 직접 비교했다. 그 결과, 디지털 트윈은 개인의 응답을 75% 정확도로 재현했지만, 인간 응답과의 상관관계는 낮았다 (약 0.2). 이 정확도는 인구 통계학적 정보만 기반으로 한 일반적인 페르소나와 유사했다. 그러나, 상세한 개인 정보를 포함한 디지털 트윈은 상관관계를 개선했으며, 추가 데이터가 필요한 기존의 머신러닝 벤치마크보다 성능이 우수했다. 디지털 트윈은 사회성 및 성격 도메인에서는 강점을 보였지만, 정치적 도메인에서는 약점을 보였다. 또한, 교육 수준, 소득 수준이 높고, 중도적인 정치적 성향 및 종교 활동을 하는 참가자에게 더 정확했다. 본 연구는 디지털 트윈의 가능성과 현재 한계를 보여주며, 개인의 상대적인 차이는 포착하지만, 특정 개인의 고유한 판단은 아직 재현하지 못함을 시사한다.

시사점, 한계점

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시사점:

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디지털 트윈은 개인 간의 상대적인 차이를 파악하는 데 유용할 수 있다.

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상세한 개인 정보를 활용하면 디지털 트윈의 예측 정확도를 높일 수 있다.

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디지털 트윈은 특정 도메인 (사회성, 성격)에서 더 나은 성능을 보일 수 있다.

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디지털 트윈의 예측 정확도는 개인의 특성 (교육 수준, 소득, 정치적 성향)에 따라 달라질 수 있다.

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데이터와 코드는 공개되어 디지털 트윈 개발을 지원한다.

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한계점:

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디지털 트윈은 특정 개인의 고유한 판단을 완전히 재현하지 못한다.

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디지털 트윈의 정확도는 여전히 제한적이며, 일반적인 페르소나와 유사한 수준일 수 있다.

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정치적 도메인에서는 디지털 트윈의 예측 성능이 떨어진다.

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디지털 트윈의 성능은 개인의 특성에 따라 편향될 수 있다.

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