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.

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks

On the performance of machine-learning-assisted Monte Carlo in sampling from simple statistical physics models

Train with Perturbation, Infer after Merging: A Two-Stage Framework for Continual Learning

SOSBENCH: Benchmarking Safety Alignment on Scientific Knowledge

PARTONOMY: Large Multimodal Models with Part-Level Visual Understanding

Transparency in Healthcare AI: Testing European Regulatory Provisions against Users' Transparency Needs

FoMoH: A clinically meaningful foundation model evaluation for structured electronic health records

Oversmoothing, Oversquashing, Heterophily, Long-Range, and more: Demystifying Common Beliefs in Graph Machine Learning

Learning to Rank Chain-of-Thought: An Energy-Based Approach with Outcome Supervision

Fixed Point Explainability

X-Sim: Cross-Embodiment Learning via Real-to-Sim-to-Real

JAEGER: Dual-Level Humanoid Whole-Body Controller

AgentVigil: Generic Black-Box Red-teaming for Indirect Prompt Injection against LLM Agents

AI Recommendations and Non-instrumental Image Concerns

Entropic Time Schedulers for Generative Diffusion Models

JEPA4Rec: Learning Effective Language Representations for Sequential Recommendation via Joint Embedding Predictive Architecture

Benchmarking Practices in LLM-driven Offensive Security: Testbeds, Metrics, and Experiment Design

Scholar Inbox: Personalized Paper Recommendations for Scientists

On Synthesizing Data for Context Attribution in Question Answering

Evaluating how LLM annotations represent diverse views on contentious topics

QualiSpeech: A Speech Quality Assessment Dataset with Natural Language Reasoning and Descriptions

ShED-HD: A Shannon Entropy Distribution Framework for Lightweight Hallucination Detection on Edge Devices

OS-Kairos: Adaptive Interaction for MLLM-Powered GUI Agents

MathFusion: Enhancing Mathematical Problem-solving of LLM through Instruction Fusion

Transformers without Normalization

Compute Optimal Scaling of Skills: Knowledge vs Reasoning

Identifying Trustworthiness Challenges in Deep Learning Models for Continental-Scale Water Quality Prediction

InfiniSST: Simultaneous Translation of Unbounded Speech with Large Language Model

From Euler to AI: Unifying Formulas for Mathematical Constants

C2-DPO: Constrained Controlled Direct Preference Optimization

Less is More: Improving LLM Alignment via Preference Data Selection

Quantifying Memorization and Parametric Response Rates in Retrieval-Augmented Vision-Language Models

ProMedTS: A Self-Supervised, Prompt-Guided Multimodal Approach for Integrating Medical Text and Time Series

Boosting Generalization in Diffusion-Based Neural Combinatorial Solver via Inference Time Adaptation

HARBOR: Exploring Persona Dynamics in Multi-Agent Competition

SelfCite: Self-Supervised Alignment for Context Attribution in Large Language Models

Truth Knows No Language: Evaluating Truthfulness Beyond English

PoGDiff: Product-of-Gaussians Diffusion Models for Imbalanced Text-to-Image Generation

Multi-Knowledge-oriented Nighttime Haze Imaging Enhancer for Vision-driven Intelligent Systems

Life-Code: Central Dogma Modeling with Multi-Omics Sequence Unification

Is attention all you need to solve the correlated electron problem?

Optimizing Temperature for Language Models with Multi-Sample Inference

Speak Easy: Eliciting Harmful Jailbreaks from LLMs with Simple Interactions

Adapt-Pruner: Adaptive Structural Pruning for Efficient Small Language Model Training

The Other Side of the Coin: Unveiling the Downsides of Model Aggregation in Federated Learning from a Layer-peeled Perspective

Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search

Activation-Informed Merging of Large Language Models

Layer by Layer: Uncovering Hidden Representations in Language Models

Search-Based Adversarial Estimates for Improving Sample Efficiency in Off-Policy Reinforcement Learning

Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss

ASAP: Learning Generalizable Online Bin Packing via Adaptive Selection After Proposal

Which Optimizer Works Best for Physics-Informed Neural Networks and Kolmogorov-Arnold Networks?

Rethinking Table Instruction Tuning

Concurrent Learning with Aggregated States via Randomized Least Squares Value Iteration

Video Depth Anything: Consistent Depth Estimation for Super-Long Videos

Foundations of Large Language Models

Benchmarking Rotary Position Embeddings for Automatic Speech Recognition

An Investigation into Value Misalignment in LLM-Generated Texts for Cultural Heritage

Retrieval-Augmented Dynamic Prompt Tuning for Incomplete Multimodal Learning

Towards End-to-End Neuromorphic Voxel-based 3D Object Reconstruction Without Physical Priors

AnalogXpert: Automating Analog Topology Synthesis by Incorporating Circuit Design Expertise into Large Language Models

Achieving Collective Welfare in Multi-Agent Reinforcement Learning via Suggestion Sharing

ClusterKV: Manipulating LLM KV Cache in Semantic Space for Recallable Compression

AccDiffusion v2: Towards More Accurate Higher-Resolution Diffusion Extrapolation

Immune: Improving Safety Against Jailbreaks in Multi-modal LLMs via Inference-Time Alignment

FastRAG: Retrieval Augmented Generation for Semi-structured Data

A dataset of questions on decision-theoretic reasoning in Newcomb-like problems

Knowledge-Augmented Multimodal Clinical Rationale Generation for Disease Diagnosis with Small Language Models

Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on Tasks where Thinking Makes Humans Worse

Building, Reusing, and Generalizing Abstract Representations from Concrete Sequences

Chemical Language Model Linker: blending text and molecules with modular adapters

Context-Augmented Code Generation Using Programming Knowledge Graphs

Convex Markov Games: A New Frontier for Multi-Agent Reinforcement Learning

FrameBridge: Improving Image-to-Video Generation with Bridge Models

Accurate and Regret-aware Numerical Problem Solver for Tabular Question Answering

MoH: Multi-Head Attention as Mixture-of-Head Attention

On Information-Theoretic Measures of Predictive Uncertainty

Compositional Shielding and Reinforcement Learning for Multi-Agent Systems

Advanced Gesture Recognition in Autism: Integrating YOLOv7, Video Augmentation and VideoMAE for Video Analysis

Upcycling Large Language Models into Mixture of Experts

Geometric Representation Condition Improves Equivariant Molecule Generation

How Much Can We Forget about Data Contamination?

On the Feasibility of Fully AI-automated Vishing Attacks

Improving Clinical Note Generation from Complex Doctor-Patient Conversation

Toward End-to-End Bearing Fault Diagnosis for Industrial Scenarios with Spiking Neural Networks

AgentCourt: Simulating Court with Adversarial Evolvable Lawyer Agents

A Survey of Text-to-SQL in the Era of LLMs: Where are we, and where are we going?

VideoQA in the Era of LLMs: An Empirical Study

TrialBench: Multi-Modal Artificial Intelligence-Ready Clinical Trial Datasets

The Remarkable Robustness of LLMs: Stages of Inference?

Navigating LLM Ethics: Advancements, Challenges, and Future Directions

OR-Bench: An Over-Refusal Benchmark for Large Language Models

DP-IQA: Utilizing Diffusion Prior for Blind Image Quality Assessment in the Wild

Does learning the right latent variables necessarily improve in-context learning?

Consistency of Neural Causal Partial Identification

Generalized Multi-Objective Reinforcement Learning with Envelope Updates in URLLC-enabled Vehicular Networks

Aptly: Making Mobile Apps from Natural Language

Personalized Wireless Federated Learning for Large Language Models

Unifying Lane-Level Traffic Prediction from a Graph Structural Perspective: Benchmark and Baseline

Counterfactual contrastive learning: robust representations via causal image synthesis

SANNet: A Semantic-Aware Agentic AI Networking Framework for Multi-Agent Cross-Layer Coordination

Created by

Haebom

저자

Yong Xiao, Haoran Zhou, Xubo Li, Yayu Gao, Guangming Shi, Ping Zhang

개요

본 논문은 에이전트 기반 AI 네트워킹(AgentNet)의 한계점을 극복하기 위한 새로운 아키텍처인 SANNet을 제안한다. AgentNet은 다수의 특화된 AI 에이전트의 협업을 통해 자율적인 의사결정, 동적인 환경 적응, 복잡한 목표 달성을 가능하게 하는 차세대 네트워킹 패러다임이다. 하지만 서로 다른 에이전트 간의 상충되는 목표가 발생할 수 있다는 문제점이 존재한다. SANNet은 사용자의 의미적 목표를 추론하고, 모바일 시스템의 다양한 계층에 연관된 에이전트에게 자동으로 작업을 할당하는 기능을 제공한다. 또한, 동적 가중치 기반의 충돌 해결 메커니즘을 도입하여 상충되는 목표를 가진 에이전트 간의 협업을 가능하게 한다. 본 논문에서는 SANNet의 충돌 해결 및 모델 일반화 성능에 대한 이론적 보장을 제시하고, Open RAN 및 5GS 코어 플랫폼 기반의 하드웨어 프로토타입을 개발하여 실험을 통해 성능 향상을 검증한다.

시사점, 한계점

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시사점:

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사용자의 의미적 목표를 추론하여 에이전트에게 작업을 자동으로 할당하는 새로운 아키텍처 SANNet 제안.

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동적 가중치 기반 충돌 해결 메커니즘을 통해 상충되는 에이전트 목표 문제 해결.

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이론적 성능 보장 및 실험적 결과를 통해 SANNet의 효과성 검증.

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Open RAN 및 5GS 코어 플랫폼 기반의 하드웨어 프로토타입 개발.

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AgentNet의 실용성 향상 및 완전 자율 네트워킹 시스템 구축을 위한 기반 마련.

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한계점:

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SANNet은 아직 초기 개발 단계이며, 더욱 광범위한 실험 및 검증이 필요하다.

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복잡한 네트워크 환경에서의 확장성 및 안정성에 대한 추가적인 연구가 필요하다.

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다양한 유형의 에이전트와 복잡한 목표에 대한 적용성 및 일반화 성능에 대한 추가적인 검증이 필요하다.

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에이전트 간의 의사소통 오버헤드 및 효율성에 대한 분석이 필요하다.

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