Daily Arxiv

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CoRT: Code-integrated Reasoning within Thinking

TransXSSM: A Hybrid Transformer State Space Model with Unified Rotary Position Embedding

Policy-Based Trajectory Clustering in Offline Reinforcement Learning

Understanding Human-AI Trust in Education

ConfPO: Exploiting Policy Model Confidence for Critical Token Selection in Preference Optimization

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning

CheMatAgent: Enhancing LLMs for Chemistry and Materials Science through Tree-Search Based Tool Learning

DeepVideo-R1: Video Reinforcement Fine-Tuning via Difficulty-aware Regressive GRPO

Robotic Policy Learning via Human-assisted Action Preference Optimization

LLM-D12: A Dual-Dimensional Scale of Instrumental and Relational Dependencies on Large Language Models

QuantMCP: Grounding Large Language Models in Verifiable Financial Reality

Future of Work with AI Agents: Auditing Automation and Augmentation Potential across the U.S. Workforce

Multi-Modal Multi-Task Federated Foundation Models for Next-Generation Extended Reality Systems: Towards Privacy-Preserving Distributed Intelligence in AR/VR/MR

Peer-Ranked Precision: Creating a Foundational Dataset for Fine-Tuning Vision Models from DataSeeds' Annotated Imagery

Q-Ponder: A Unified Training Pipeline for Reasoning-based Visual Quality Assessment

Sample Complexity and Representation Ability of Test-time Scaling Paradigms

Context Is Not Comprehension

High Performance Space Debris Tracking in Complex Skylight Backgrounds with a Large-Scale Dataset

SALAD: Systematic Assessment of Machine Unlearing on LLM-Aided Hardware Design

iQUEST: An Iterative Question-Guided Framework for Knowledge Base Question Answering

Incentivizing Reasoning for Advanced Instruction-Following of Large Language Models

SealQA: Raising the Bar for Reasoning in Search-Augmented Language Models

Subgraph Gaussian Embedding Contrast for Self-Supervised Graph Representation Learning

Quantum AIXI: Universal Intelligence via Quantum Information

VTool-R1: VLMs Learn to Think with Images via Reinforcement Learning on Multimodal Tool Use

Qronos: Correcting the Past by Shaping the Future... in Post-Training Quantization

QuXAI: Explainers for Hybrid Quantum Machine Learning Models

Convert Language Model into a Value-based Strategic Planner

PhysNav-DG: A Novel Adaptive Framework for Robust VLM-Sensor Fusion in Navigation Applications

Token-Efficient RL for LLM Reasoning

MAYA: Addressing Inconsistencies in Generative Password Guessing through a Unified Benchmark

Elucidating the Design Space of Multimodal Protein Language Models

A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce

On the Geometry of Receiver Operating Characteristic and Precision-Recall Curves

Expert Race: A Flexible Routing Strategy for Scaling Diffusion Transformer with Mixture of Experts

Content ARCs: Decentralized Content Rights in the Age of Generative AI

PLAY2PROMPT: Zero-shot Tool Instruction Optimization for LLM Agents via Tool Play

Computation Mechanism Behind LLM Position Generalization

CompMarkGS: Robust Watermarking for Compressed 3D Gaussian Splatting

Large Language Models for Outpatient Referral: Problem Definition, Benchmarking and Challenges

Social Bias Benchmark for Generation: A Comparison of Generation and QA-Based Evaluations

An energy-efficient learning solution for the Agile Earth Observation Satellite Scheduling Problem

Generative Uncertainty in Diffusion Models

EgoNormia: Benchmarking Physical Social Norm Understanding

Obliviate: Efficient Unmemorization for Protecting Intellectual Property in Large Language Models

From Features to Graphs: Exploring Graph Structures and Pairwise Interactions via GNNs

Object-Centric Latent Action Learning

Quality over Quantity: Boosting Data Efficiency Through Ensembled Multimodal Data Curation

TransMLA: Multi-Head Latent Attention Is All You Need

Implicit Language Models are RNNs: Balancing Parallelization and Expressivity

Lightweight Dataset Pruning without Full Training via Example Difficulty and Prediction Uncertainty

Prompt-based Depth Pruning of Large Language Models

Great Models Think Alike and this Undermines AI Oversight

Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting

Latent Action Learning Requires Supervision in the Presence of Distractors

SR-Reward: Taking The Path More Traveled

Heterogeneous Multi-Agent Reinforcement Learning for Distributed Channel Access in WLANs

SoK: Watermarking for AI-Generated Content

Engagement-Driven Content Generation with Large Language Models

PyGen: A Collaborative Human-AI Approach to Python Package Creation

DAWN: Designing Distributed Agents in a Worldwide Network

Efficient Length-Generalizable Attention via Causal Retrieval for Long-Context Language Modeling

Center-fixing of tropical cyclones using uncertainty-aware deep learning applied to high-temporal-resolution geostationary satellite imagery

LLM-Cure: LLM-based Competitor User Review Analysis for Feature Enhancement

Deploying Open-Source Large Language Models: A performance Analysis

Neural Networks Generalize on Low Complexity Data

M3-JEPA: Multimodal Alignment via Multi-gate MoE based on the Joint-Predictive Embedding Architecture

Paired Completion: Flexible Quantification of Issue-framing at Scale with LLMs

The Fellowship of the LLMs: Multi-Agent Workflows for Synthetic Preference Optimization Dataset Generation

TimeBridge: Better Diffusion Prior Design with Bridge Models for Time Series Generation

Multi-group Uncertainty Quantification for Long-form Text Generation

Hey, That's My Model! Introducing Chain & Hash, An LLM Fingerprinting Technique

Privacy-Aware Spectrum Pricing and Power Control Optimization for LEO Satellite Internet-of-Things

IndoToxic2024: A Demographically-Enriched Dataset of Hate Speech and Toxicity Types for Indonesian Language

Incentivizing Quality Text Generation via Statistical Contracts

Visually Descriptive Language Model for Vector Graphics Reasoning

Securing Large Language Models: Threats, Vulnerabilities and Responsible Practices

Mitigating Object Hallucination in Large Vision-Language Models via Image-Grounded Guidance

Near-Optimal Algorithms for Constrained k-Center Clustering with Instance-level Background Knowledge

IoTGeM: Generalizable Models for Behaviour-Based IoT Attack Detection

Improved Algorithm for Deep Active Learning under Imbalance via Optimal Separation

ConvD: Attention Enhanced Dynamic Convolutional Embeddings for Knowledge Graph Completion

Noise Balance and Stationary Distribution of Stochastic Gradient Descent

The Packing Chromatic Number of the Infinite Square Grid is 15

Reinforcing Multimodal Understanding and Generation with Dual Self-rewards

A Proposal to Extend the Common Model of Cognition with Metacognition

The Optimization Paradox in Clinical AI Multi-Agent Systems

CHANCERY: Evaluating Corporate Governance Reasoning Capabilities in Language Models

DeePoly: A High-Order Accuracy Scientific Machine Learning Framework for Function Approximation and Solving PDE

Beamforming and Resource Allocation for Delay Optimization in RIS-Assisted OFDM Systems

Evaluation of LLMs for mathematical problem solving

The Automated but Risky Game: Modeling Agent-to-Agent Negotiations and Transactions in Consumer Markets

A Heuristic Algorithm Based on Beam Search and Iterated Local Search for the Maritime Inventory Routing Problem

A Vision for Auto Research with LLM Agents

AssistanceZero: Scalably Solving Assistance Games

Don't Lag, RAG: Training-Free Adversarial Detection Using RAG

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

Training-Free Safe Denoisers for Safe Use of Diffusion Models

CollabLLM: From Passive Responders to Active Collaborators

Position: Theory of Mind Benchmarks are Broken for Large Language Models

EvoMesh: Adaptive Physical Simulation with Hierarchical Graph Evolutions

Created by

Haebom

저자

Huayu Deng, Xiangming Zhu, Yunbo Wang, Xiaokang Yang

개요

EvoMesh는 메시 기반 물리 시뮬레이션을 위한 새로운 그래프 신경망 프레임워크입니다. 기존 방법들이 수동으로 설계된 고정된 계층적 그래프 구조를 사용하는 것과 달리, EvoMesh는 물리적 입력에 의해 적응적으로 유도되는, 그래프 계층 구조와 물리적 동역학을 함께 학습하는 완전한 미분 가능 프레임워크입니다. 이를 위해 이방성 메시지 전달을 도입하여 각 계층 내 노드 간의 방향 특정 동적 특징 집계를 가능하게 하고, 물리적 맥락에 따라 다음 계층 수준의 노드 선택 확률을 동시에 학습합니다. 이러한 설계는 보다 유연한 메시지 지름길을 만들고 장거리 의존성을 포착하는 모델의 능력을 향상시킵니다. 다섯 개의 벤치마크 물리 시뮬레이션 데이터 세트에 대한 광범위한 실험은 EvoMesh가 최근 고정 계층 메시지 전달 네트워크보다 훨씬 우수한 성능을 보임을 보여줍니다. 코드는 https://github.com/hbell99/EvoMesh 에서 이용 가능합니다.

GitHub - hbell99/EvoMesh: About code release of "EvoMesh: Adaptive Physical Simulation with Hierarchical Graph Evolutions", ICML 2025

About code release of "EvoMesh: Adaptive Physical Simulation with Hierarchical Graph Evolutions", ICML 2025 - hbell99/EvoMesh

시사점, 한계점

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시사점:

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물리적 입력에 의해 적응적으로 유도되는 그래프 계층 구조 학습을 통해 복잡한 물리 시스템의 진화하는 역학에 적응할 수 있는 유연성을 제공합니다.

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이방성 메시지 전달을 통해 장거리 의존성을 효과적으로 포착하고 모델의 성능을 향상시킵니다.

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다양한 물리 시뮬레이션 데이터셋에서 기존 방법보다 우수한 성능을 보입니다.

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공개된 코드를 통해 재현성과 추가 연구를 용이하게 합니다.

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한계점:

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제안된 방법의 계산 비용 및 메모리 요구사항에 대한 분석이 부족합니다.

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다양한 유형의 물리 시스템에 대한 일반화 성능에 대한 추가적인 연구가 필요합니다.

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학습 과정의 안정성과 수렴 속도에 대한 자세한 분석이 필요합니다.

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