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Daily Arxiv
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BrowseConf: Confidence-Guided Test-Time Scaling for Web Agents
FRBNet: Revisiting Low-Light Vision through Frequency-Domain Radial Basis Network
Eigen-Value: Efficient Domain-Robust Data Valuation via Eigenvalue-Based Approach
Robust Uncertainty Quantification for Self-Evolving Large Language Models via Continual Domain Pretraining
HyPerNav: Hybrid Perception for Object-Oriented Navigation in Unknown Environment
TraceTrans: Translation and Spatial Tracing for Surgical Prediction
GRAID: Enhancing Spatial Reasoning of VLMs Through High-Fidelity Data Generation
CustomIR: Unsupervised Fine-Tuning of Dense Embeddings for Known Document Corpora
Your Dense Retriever is Secretly an Expeditious Reasoner
FieldGen: From Teleoperated Pre-Manipulation Trajectories to Field-Guided Data Generation
Context-level Language Modeling by Learning Predictive Context Embeddings
Detecting Latin in Historical Books with Large Language Models: A Multimodal Benchmark
MENTOR: A Reinforcement Learning Framework for Enabling Tool Use in Small Models via Teacher-Optimized Rewards
MemoryBench: A Benchmark for Memory and Continual Learning in LLM Systems
SimpleVSF: VLM-Scoring Fusion for Trajectory Prediction of End-to-End Autonomous Driving
The Formalism-Implementation Gap in Reinforcement Learning Research
OmniVinci: Enhancing Architecture and Data for Omni-Modal Understanding LLM
TokenTiming: A Dynamic Alignment Method for Universal Speculative Decoding Model Pairs
Cross-Scenario Unified Modeling of User Interests at Billion Scale
DPRF: A Generalizable Dynamic Persona Refinement Framework for Optimizing Behavior Alignment Between Personalized LLM Role-Playing Agents and Humans
Think Just Enough: Sequence-Level Entropy as a Confidence Signal for LLM Reasoning
SEER: The Span-based Emotion Evidence Retrieval Benchmark
Distilled Protein Backbone Generation
Untargeted Jailbreak Attack
AdaDetectGPT: Adaptive Detection of LLM-Generated Text with Statistical Guarantees
Prosperity before Collapse: How Far Can Off-Policy RL Reach with Stale Data on LLMs?
On Robustness of Vision-Language-Action Model against Multi-Modal Perturbations
PEARL: Peer-Enhanced Adaptive Radio via On-Device LLM
Seeing Symbols, Missing Cultures: Probing Vision-Language Models' Reasoning on Fire Imagery and Cultural Meaning
ImageNet-trained CNNs are not biased towards texture: Revisiting feature reliance through controlled suppression
PTQTP: Post-Training Quantization to Trit-Planes for Large Language Models
The human-machine paradox: how collaboration creates or destroys value, and why augmentation is key to resolving it
Reproducible workflow for online AI in digital health
Pre-trained knowledge elevates large language models beyond traditional chemical reaction optimizers
MolErr2Fix: Benchmarking LLM Trustworthiness in Chemistry via Modular Error Detection, Localization, Explanation, and Revision
Robustness is Important: Limitations of LLMs for Data Fitting
DP-LLM: Runtime Model Adaptation with Dynamic Layer-wise Precision Assignment
FoGE: Fock Space inspired encoding for graph prompting
PULSE: Practical Evaluation Scenarios for Large Multimodal Model Unlearning
GEMeX-RMCoT: An Enhanced Med-VQA Dataset for Region-Aware Multimodal Chain-of-Thought Reasoning
Thermometry of simulated Bose--Einstein condensates using machine learning
LittleBit: Ultra Low-Bit Quantization via Latent Factorization
BNMusic: Blending Environmental Noises into Personalized Music
Evaluating AI-Powered Learning Assistants in Engineering Higher Education: Student Engagement, Ethical Challenges, and Policy Implications
Mixture-of-Experts Meets In-Context Reinforcement Learning
Improving Data Efficiency for LLM Reinforcement Fine-tuning Through Difficulty-targeted Online Data Selection and Rollout Replay
NOBLE -- Neural Operator with Biologically-informed Latent Embeddings to Capture Experimental Variability in Biological Neuron Models
Data Leakage and Deceptive Performance: A Critical Examination of Credit Card Fraud Detection Methodologies
REASONING COMPILER: LLM-Guided Optimizations for Efficient Model Serving
PVP: An Image Dataset for Personalized Visual Persuasion with Persuasion Strategies, Viewer Characteristics, and Persuasiveness Ratings
FALCON: An ML Framework for Fully Automated Layout-Constrained Analog Circuit Design
OmniResponse: Online Multimodal Conversational Response Generation in Dyadic Interactions
GraSS: Scalable Data Attribution with Gradient Sparsification and Sparse Projection
MixAT: Combining Continuous and Discrete Adversarial Training for LLMs
STree: Speculative Tree Decoding for Hybrid State-Space Models
Do Language Models Use Their Depth Efficiently?
A Generalized Label Shift Perspective for Cross-Domain Gaze Estimation
The Logical Expressiveness of Temporal GNNs via Two-Dimensional Product Logics
Group-in-Group Policy Optimization for LLM Agent Training
BRIDGE: Benchmarking Large Language Models for Understanding Real-world Clinical Practice Text
Offline Learning and Forgetting for Reasoning with Large Language Models
Multimodal 3D Genome Pre-training
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model
Mirror Descent and Novel Exponentiated Gradient Algorithms Using Trace-Form Entropies and Deformed Logarithms
Benchmarking AI Models in Software Engineering: A Review, Search Tool, and Unified Approach for Elevating Benchmark Quality
Generalized Exponentiated Gradient Algorithms Using the Euler Two-Parameter Logarithm
FragFM: Hierarchical Framework for Efficient Molecule Generation via Fragment-Level Discrete Flow Matching
ADMN: A Layer-Wise Adaptive Multimodal Network for Dynamic Input Noise and Compute Resources
A High-Dimensional Statistical Method for Optimizing Transfer Quantities in Multi-Source Transfer Learning
$\beta$-DQN: Improving Deep Q-Learning By Evolving the Behavior
Provable Scaling Laws for the Test-Time Compute of Large Language Models
Learned, Lagged, LLM-splained: LLM Responses to End User Security Questions
One-Step is Enough: Sparse Autoencoders for Text-to-Image Diffusion Models
TrajAgent: An LLM-Agent Framework for Trajectory Modeling via Large-and-Small Model Collaboration
GRS: Generating Robotic Simulation Tasks from Real-World Images
Navigation with VLM framework: Towards Going to Any Language
Retrieval-Augmented Generation-based Relation Extraction
Diffusion Models Meet Contextual Bandits
Querying Inconsistent Prioritized Data with ORBITS: Algorithms, Implementation, and Experiments
Multi-Agent Evolve: LLM Self-Improve through Co-evolution
ReCode: Unify Plan and Action for Universal Granularity Control
Human-Like Goalkeeping in a Realistic Football Simulation: a Sample-Efficient Reinforcement Learning Approach
From Prompt Optimization to Multi-Dimensional Credibility Evaluation: Enhancing Trustworthiness of Chinese LLM-Generated Liver MRI Reports
Huxley-G\"odel Machine: Human-Level Coding Agent Development by an Approximation of the Optimal Self-Improving Machine
Understanding AI Trustworthiness: A Scoping Review of AIES & FAccT Articles
PanicToCalm: A Proactive Counseling Agent for Panic Attacks
A Comprehensive Survey on Reinforcement Learning-based Agentic Search: Foundations, Roles, Optimizations, Evaluations, and Applications
Co-TAP: Three-Layer Agent Interaction Protocol Technical Report
Evaluating the Use of Large Language Models as Synthetic Social Agents in Social Science Research
MathBode: Understanding LLM Reasoning with Dynamical Systems
Is It Certainly a Deepfake? Reliability Analysis in Detection & Generation Ecosystem
Accelerate Scaling of LLM Finetuning via Quantifying the Coverage and Depth of Instruction Set
Freeze and Conquer: Reusable Ansatz for Solving the Traveling Salesman Problem
A Neuroscience-Inspired Dual-Process Model of Compositional Generalization
Memory Mosaics at scale
The Confidence Paradox: Can LLM Know When It's Wrong
VIRAL: Vision-grounded Integration for Reward design And Learning
Partner Modelling Emerges in Recurrent Agents (But Only When It Matters)
Multimodal Dreaming: A Global Workspace Approach to World Model-Based Reinforcement Learning
TableTime: Reformulating Time Series Classification as Training-Free Table Understanding with Large Language Models
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FoGE: Fock Space inspired encoding for graph prompting
Created by
Haebom
저자
Sotirios Panagiotis Chytas, Rudrasis Chakraborty, Vikas Singh
개요
본 논문은 최신 대규모 언어 모델(LLM)이 그래프와 같은 구조적 데이터에 대한 질문을 이해하고 답변할 수 있음을 보여준다. 이를 위해 수학 물리학에서 차용한 Fock 공간 표현을 기반으로 하는 파라미터가 없는 그래프 인코더를 사용하여 사전 훈련된 LLM에 공급할 수 있는 풍부한 정보를 담은 그래프 인코딩을 생성한다. 이 방식은 다양한 그래프 구조에 효과적이며, 기존 솔루션을 단순화하고 일반화하는 데 기여한다.
시사점, 한계점
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시사점:
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파라미터가 없는 그래프 인코더를 활용하여 LLM의 그래프 이해 능력을 향상시킴.
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다양한 그래프 구조 (단순 그래프, 단백질, 하이퍼그래프 등)에 효과적으로 적용 가능.
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기존 솔루션보다 단순하고 일반화된 모델 구축 가능.
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한계점:
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구체적인 한계점은 논문에 명시되지 않음.
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