Daily Arxiv

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A Systematic Review of Human-AI Co-Creativity

DFVEdit: Conditional Delta Flow Vector for Zero-shot Video Editing

Exploring the Capabilities of the Frontier Large Language Models for Nuclear Energy Research

MUPA: Towards Multi-Path Agentic Reasoning for Grounded Video Question Answering

Sparse-Reg: Improving Sample Complexity in Offline Reinforcement Learning using Sparsity

FEAST: A Flexible Mealtime-Assistance System Towards In-the-Wild Personalization

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?

Vision Transformers Don't Need Trained Registers

Eye of Judgement: Dissecting the Evaluation of Russian-speaking LLMs with POLLUX

Maximizing Confidence Alone Improves Reasoning

EasyDistill: A Comprehensive Toolkit for Effective Knowledge Distillation of Large Language Models

Improving LLM Outputs Against Jailbreak Attacks with Expert Model Integration

Personalized Robotic Object Rearrangement from Scene Context

Cannot See the Forest for the Trees: Invoking Heuristics and Biases to Elicit Irrational Choices of LLMs

OpenTCM: A GraphRAG-Empowered LLM-based System for Traditional Chinese Medicine Knowledge Retrieval and Diagnosis

Explicit neural network classifiers for non-separable data

USM-VC: Mitigating Timbre Leakage with Universal Semantic Mapping Residual Block for Voice Conversion

Towards Adaptive Memory-Based Optimization for Enhanced Retrieval-Augmented Generation

LoopGen: Training-Free Loopable Music Generation

Automated detection of atomicity violations in large-scale systems

Grammar and Gameplay-aligned RL for Game Description Generation with LLMs

Generative AI for Software Architecture. Applications, Challenges, and Future Directions

English K_Quantization of LLMs Does Not Disproportionately Diminish Multilingual Performance

Heuristics for AI-driven Graphical Asset Generation Tools in Game Design and Development Pipelines: A User-Centred Approach

Collective Reasoning Among LLMs: A Framework for Answer Validation Without Ground Truth

Multi-Turn Code Generation Through Single-Step Rewards

Round Attention: A Novel Round-Level Attention Mechanism to Accelerate LLM Inference

KITAB-Bench: A Comprehensive Multi-Domain Benchmark for Arabic OCR and Document Understanding

AB-UPT: Scaling Neural CFD Surrogates for High-Fidelity Automotive Aerodynamics Simulations via Anchored-Branched Universal Physics Transformers

MedRAG: Enhancing Retrieval-augmented Generation with Knowledge Graph-Elicited Reasoning for Healthcare Copilot

Generative Data Mining with Longtail-Guided Diffusion

Leveraging Online Olympiad-Level Math Problems for LLMs Training and Contamination-Resistant Evaluation

No More Sliding Window: Efficient 3D Medical Image Segmentation with Differentiable Top-k Patch Sampling

Jailbreaking Multimodal Large Language Models via Shuffle Inconsistency

End-to-End Long Document Summarization using Gradient Caching

Refining Salience-Aware Sparse Fine-Tuning Strategies for Language Models

KNN-MMD: Cross Domain Wireless Sensing via Local Distribution Alignment

EUR/USD Exchange Rate Forecasting incorporating Text Mining Based on Pre-trained Language Models and Deep Learning Methods

Large-Scale Multirobot Coverage Path Planning on Grids With Path Deconfliction

Dynamic Adaptive Rank Space Exploration for Efficient Sentiment Analysis with Large Language Models

Federated Data-Efficient Instruction Tuning for Large Language Models

QT-DoG: Quantization-aware Training for Domain Generalization

Testing Causal Models with Hidden Variables in Polynomial Delay via Conditional Independencies

Stability of Primal-Dual Gradient Flow Dynamics for Multi-Block Convex Optimization Problems

LRP4RAG: Detecting Hallucinations in Retrieval-Augmented Generation via Layer-wise Relevance Propagation

The Mamba in the Llama: Distilling and Accelerating Hybrid Models

EUR-USD Exchange Rate Forecasting Based on Information Fusion with Large Language Models and Deep Learning Methods

Dynamic Adaptive Optimization for Effective Sentiment Analysis Fine-Tuning on Large Language Models

Mitigating Metropolitan Carbon Emissions with Dynamic Eco-driving at Scale

CAPM: Fast and Robust Verification on Maxpool-based CNN via Dual Network

MimicMotion: High-Quality Human Motion Video Generation with Confidence-aware Pose Guidance

From Data Quality for AI to AI for Data Quality: A Systematic Review of Tools for AI-Augmented Data Quality Management in Data Warehouses

FuzzAug: Data Augmentation by Coverage-guided Fuzzing for Neural Test Generation

RLSF: Fine-tuning LLMs via Symbolic Feedback

A Survey on Patent Analysis: From NLP to Multimodal AI

Enhancing Object Detection Robustness: Detecting and Restoring Confidence in the Presence of Adversarial Patch Attacks

Bias-Augmented Consistency Training Reduces Biased Reasoning in Chain-of-Thought

Programming Distributed Collective Processes in the eXchange Calculus

Communication-Efficient Heterogeneous Federated Learning with Generalized Heavy-Ball Momentum

Fairness and Bias in Algorithmic Hiring: a Multidisciplinary Survey

On CNF formulas irredundant with respect to unit clause propagation

SONG: Self-Organizing Neural Graphs

Mobile-R1: Towards Interactive Reinforcement Learning for VLM-Based Mobile Agent via Task-Level Rewards

KunLunBaizeRAG: Reinforcement Learning Driven Inference Performance Leap for Large Language Models

FEAT: A Preference Feedback Dataset through a Cost-Effective Auto-Generation and Labeling Framework for English AI Tutoring

Dynamic Knowledge Exchange and Dual-diversity Review: Concisely Unleashing the Potential of a Multi-Agent Research Team

PhysUniBench: An Undergraduate-Level Physics Reasoning Benchmark for Multimodal Models

From Human to Machine Psychology: A Conceptual Framework for Understanding Well-Being in Large Language Models

The SWE-Bench Illusion: When State-of-the-Art LLMs Remember Instead of Reason

VLM@school -- Evaluation of AI image understanding on German middle school knowledge

The AI Imperative: Scaling High-Quality Peer Review in Machine Learning

Toward Data Systems That Are Business Semantic Centric and AI Agents Assisted

ScienceBoard: Evaluating Multimodal Autonomous Agents in Realistic Scientific Workflows

$C^3$-Bench: The Things Real Disturbing LLM based Agent in Multi-Tasking

StarFT: Robust Fine-tuning of Zero-shot Models via Spuriosity Alignment

REMOR: Automated Peer Review Generation with LLM Reasoning and Multi-Objective Reinforcement Learning

Epistemic Artificial Intelligence is Essential for Machine Learning Models to Truly 'Know When They Do Not Know'

Local Markov Equivalence and Local Causal Discovery for Identifying Controlled Direct Effects

Adapting Probabilistic Risk Assessment for AI

From Superficial to Deep: Integrating External Knowledge for Follow-up Question Generation Using Knowledge Graph and LLM

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

SENSEI: Semantic Exploration Guided by Foundation Models to Learn Versatile World Models

OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis

Problem Solving Through Human-AI Preference-Based Cooperation

CRAB: Cross-environment Agent Benchmark for Multimodal Language Model Agents

CLoVE: Personalized Federated Learning through Clustering of Loss Vector Embeddings

HyperCLOVA X THINK Technical Report

Dehazing Light Microscopy Images with Guided Conditional Flow Matching: finding a sweet spot between fidelity and realism

QuickSilver -- Speeding up LLM Inference through Dynamic Token Halting, KV Skipping, Contextual Token Fusion, and Adaptive Matryoshka Quantization

Multi-View Contrastive Learning for Robust Domain Adaptation in Medical Time Series Analysis

Towards Distributed Neural Architectures

Can Video Large Multimodal Models Think Like Doubters-or Double-Down: A Study on Defeasible Video Entailment

Probabilistic Optimality for Inference-time Scaling

Sheaf-Based Decentralized Multimodal Learning for Next-Generation Wireless Communication Systems

From Ground to Air: Noise Robustness in Vision Transformers and CNNs for Event-Based Vehicle Classification with Potential UAV Applications

Concept-Level AI for Telecom: Moving Beyond Large Language Models

A Framework for Multi-source Privacy Preserving Epidemic Analysis

A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake

Less Greedy Equivalence Search

A Practical Approach to Power Saving in Hearables Using Sub-Nyquist Sampling with Bandwidth Extension

Exploring the Capabilities of the Frontier Large Language Models for Nuclear Energy Research

Created by

Haebom

저자

Ahmed Almeldein, Mohammed Alnaggar, Rick Archibald, Tom Beck, Arpan Biswas, Rike Bostelmann, Wes Brewer, Chris Bryan, Christopher Calle, Cihangir Celik, Rajni Chahal, Jong Youl Choi, Arindam Chowdhury, Mark Cianciosa, Franklin Curtis, Gregory Davidson, Sebastian De Pascuale, Lisa Fassino, Ana Gainaru, Yashika Ghai, Luke Gibson, Qian Gong, Christopher Greulich, Scott Greenwood, Cory Hauck, Ehab Hassan, Rinkle Juneja, Soyoung Kang, Scott Klasky, Atul Kumar, Vineet Kumar, Paul Laiu, Calvin Lear, Yan-Ru Lin, Jono McConnell, Furkan Oz, Rishi Pillai, Anant Raj, Pradeep Ramuhalli, Marie Romedenne, Samantha Sabatino, Jose Salcedo-Perez, Nathan D. See, Arpan Sircar, Punam Thankur, Tim Younkin, Xiao-Ying Yu, Prashant Jain, Tom Evans, Prasanna Balaprakash

개요

Oak Ridge National Laboratory에서 개최된 AI for Nuclear Energy 워크샵에서는 대규모 언어 모델(LLM)이 핵융합 및 핵분열 연구를 가속화할 가능성을 평가했습니다. 14개의 학제 간 팀이 하루 동안 ChatGPT, Gemini, Claude 등의 AI 모델을 사용하여 다양한 핵 과학적 과제를 탐구했습니다. 응용 분야는 핵융합로 제어를 위한 기초 모델 개발부터 몬테카를로 시뮬레이션 자동화, 재료 열화 예측, 고급 원자로를 위한 실험 프로그램 설계에 이르기까지 다양했습니다. 팀들은 프롬프트 엔지니어링, 심층 연구 기능, 반복적 개선을 결합한 구조화된 워크플로를 사용하여 가설, 프로토타입 코드 및 연구 전략을 생성했습니다. 주요 결과는 LLM이 초기 단계 탐색, 문헌 종합 및 워크플로 설계에 탁월하며, 연구 격차를 성공적으로 식별하고 타당한 실험 프레임워크를 생성함을 보여줍니다. 그러나 새로운 재료 설계, 모델링 및 시뮬레이션을 위한 고급 코드 생성 및 전문가 검증이 필요한 도메인별 세부 정보와 같은 상당한 한계가 나타났습니다. 성공적인 결과는 전문가 주도의 프롬프트 엔지니어링과 AI를 물리 기반 방법의 대체물이 아닌 보완 도구로 취급한 결과입니다. 이 워크샵은 신속한 반복 및 학제 간 종합을 통해 AI가 핵 에너지 연구를 가속화할 가능성을 확인했지만, 큐레이션된 핵 특정 데이터 세트, 워크플로 자동화 및 특수 모델 개발의 필요성을 강조했습니다. 이러한 결과는 AI 도구를 핵 과학 워크플로에 통합하기 위한 로드맵을 제공하여 더 안전하고 효율적인 핵 에너지 시스템의 개발 주기를 단축하면서 엄격한 과학적 표준을 유지할 수 있습니다.

시사점, 한계점

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시사점:

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LLM은 핵 에너지 연구의 초기 단계 탐색, 문헌 조사 및 워크플로 설계에 효과적이다.

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AI를 활용하여 연구 격차를 파악하고 실험 프레임워크를 생성할 수 있다.

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AI는 핵 에너지 연구의 개발 주기를 단축하고 효율성을 높일 수 있는 잠재력을 가지고 있다.

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전문가 주도의 프롬프트 엔지니어링과 AI의 적절한 활용이 중요하다.

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한계점:

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새로운 재료 설계 및 고급 코드 생성에는 어려움이 있다.

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도메인 특이적 세부 정보는 전문가 검증이 필요하다.

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핵 특정 데이터 세트, 워크플로 자동화 및 특수 모델 개발이 필요하다.

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