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.

Maximizing Confidence Alone Improves Reasoning

Pre-training for Recommendation Unlearning

FastTD3: Simple, Fast, and Capable Reinforcement Learning for Humanoid Control

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

Agent-UniRAG: A Trainable Open-Source LLM Agent Framework for Unified Retrieval-Augmented Generation Systems

Topological Structure Learning Should Be A Research Priority for LLM-Based Multi-Agent Systems

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

SplitLoRA: Balancing Stability and Plasticity in Continual Learning Through Gradient Space Splitting

Skywork Open Reasoner 1 Technical Report

Speculative Decoding Meets Quantization: Compatibility Evaluation and Hierarchical Framework Design

DORAEMON: Decentralized Ontology-aware Reliable Agent with Enhanced Memory Oriented Navigation

Cross-modal RAG: Sub-dimensional Retrieval-Augmented Text-to-Image Generation

CAST: Contrastive Adaptation and Distillation for Semi-Supervised Instance Segmentation

Scaling Up Liquid-Resistance Liquid-Capacitance Networks for Efficient Sequence Modeling

Is Attention Required for Transformer Inference? Explore Function-preserving Attention Replacement

VietASR: Achieving Industry-level Vietnamese ASR with 50-hour labeled data and Large-Scale Speech Pretraining

Hume: Introducing System-2 Thinking in Visual-Language-Action Model

DeSocial: Blockchain-based Decentralized Social Networks

Subgroups Matter for Robust Bias Mitigation

Hybrid Disagreement-Diversity Active Learning for Bioacoustic Sound Event Detection

Automatic Transmission for LLM Tiers: Optimizing Cost and Accuracy in Large Language Models

The challenge of hidden gifts in multi-agent reinforcement learning

Retrieval Visual Contrastive Decoding to Mitigate Object Hallucinations in Large Vision-Language Models

Risk-aware Direct Preference Optimization under Nested Risk Measure

Surrogate-Assisted Evolutionary Reinforcement Learning Based on Autoencoder and Hyperbolic Neural Network

A Novel Zero-Trust Identity Framework for Agentic AI: Decentralized Authentication and Fine-Grained Access Control

BroadGen: A Framework for Generating Effective and Efficient Advertiser Broad Match Keyphrase Recommendations

Reality Check: A New Evaluation Ecosystem Is Necessary to Understand AI's Real World Effects

How We Won the ISLES'24 Challenge by Preprocessing

SP2RINT: Spatially-Decoupled Physics-Inspired Progressive Inverse Optimization for Scalable, PDE-Constrained Meta-Optical Neural Network Training

Seek-CAD: A Self-refined Generative Modeling for 3D Parametric CAD Using Local Inference via DeepSeek

Rethinking the Sampling Criteria in Reinforcement Learning for LLM Reasoning: A Competence-Difficulty Alignment Perspective

OrionBench: A Benchmark for Chart and Human-Recognizable Object Detection in Infographics

EarthSE: A Benchmark Evaluating Earth Scientific Exploration Capability for Large Language Models

CASS: Nvidia to AMD Transpilation with Data, Models, and Benchmark

Beyond Face Swapping: A Diffusion-Based Digital Human Benchmark for Multimodal Deepfake Detection

Edge-First Language Model Inference: Models, Metrics, and Tradeoffs

Smaller, Smarter, Closer: The Edge of Collaborative Generative AI

YESciEval: Robust LLM-as-a-Judge for Scientific Question Answering

DiagnosisArena: Benchmarking Diagnostic Reasoning for Large Language Models

Articulatory Feature Prediction from Surface EMG during Speech Production

Exploring Spatiotemporal Emotional Synchrony in Dyadic Interactions: The Role of Speech Conditions in Facial and Vocal Affective Alignment

LEXam: Benchmarking Legal Reasoning on 340 Law Exams

Neural Networks as Universal Finite-State Machines: A Constructive Deterministic Finite Automaton Theory

The Geometry of ReLU Networks through the ReLU Transition Graph

RepCali: High Efficient Fine-tuning Via Representation Calibration in Latent Space for Pre-trained Language Models

Fusing Bidirectional Chains of Thought and Reward Mechanisms A Method for Enhancing Question-Answering Capabilities of Large Language Models for Chinese Intangible Cultural Heritage

Multimodal Survival Modeling in the Age of Foundation Models

Burger: Robust Graph Denoising-augmentation Fusion and Multi-semantic Modeling in Social Recommendation

The Aloe Family Recipe for Open and Specialized Healthcare LLMs

To Judge or not to Judge: Using LLM Judgements for Advertiser Keyphrase Relevance at eBay

LENSLLM: Unveiling Fine-Tuning Dynamics for LLM Selection

CoordField: Coordination Field for Agentic UAV Task Allocation In Low-altitude Urban Scenarios

Learning to Reason under Off-Policy Guidance

A Combinatorial Theory of Dropout: Subnetworks, Graph Geometry, and Generalization

Error Broadcast and Decorrelation as a Potential Artificial and Natural Learning Mechanism

Carbon-Efficient 3D DNN Acceleration: Optimizing Performance and Sustainability

Agentic Knowledgeable Self-awareness

Are Domain Generalization Benchmarks with Accuracy on the Line Misspecified?

MiZero: The Shadowy Defender Against Text Style Infringements

Firm or Fickle? Evaluating Large Language Models Consistency in Sequential Interactions

LoTUS: Large-Scale Machine Unlearning with a Taste of Uncertainty

Temporal Relation Extraction in Clinical Texts: A Span-based Graph Transformer Approach

LEAVS: An LLM-based Labeler for Abdominal CT Supervision

From Head to Tail: Towards Balanced Representation in Large Vision-Language Models through Adaptive Data Calibration

Enhancing Retrieval for ESGLLM via ESG-CID -- A Disclosure Content Index Finetuning Dataset for Mapping GRI and ESRS

DynaCode: A Dynamic Complexity-Aware Code Benchmark for Evaluating Large Language Models in Code Generation

Aligning Text to Image in Diffusion Models is Easier Than You Think

Can LLMs Reason About Program Semantics? A Comprehensive Evaluation of LLMs on Formal Specification Inference

BatteryLife: A Comprehensive Dataset and Benchmark for Battery Life Prediction

Bridging Critical Gaps in Convergent Learning: How Representational Alignment Evolves Across Layers, Training, and Distribution Shifts

ExpandR: Teaching Dense Retrievers Beyond Queries with LLM Guidance

Audio Visual Segmentation Through Text Embeddings

Privacy-Aware Joint DNN Model Deployment and Partitioning Optimization for Collaborative Edge Inference Services

Learning to Reason from Feedback at Test-Time

ParamMute: Suppressing Knowledge-Critical FFNs for Faithful Retrieval-Augmented Generation

STeCa: Step-level Trajectory Calibration for LLM Agent Learning

GSQ-Tuning: Group-Shared Exponents Integer in Fully Quantized Training for LLMs On-Device Fine-tuning

Enhancing Semi-supervised Learning with Zero-shot Pseudolabels

DELMAN: Dynamic Defense Against Large Language Model Jailbreaking with Model Editing

Multilingual Encoder Knows more than You Realize: Shared Weights Pretraining for Extremely Low-Resource Languages

Jailbreaking to Jailbreak

Behavior-Regularized Diffusion Policy Optimization for Offline Reinforcement Learning

Position: Scaling LLM Agents Requires Asymptotic Analysis with LLM Primitives

CodeSteer: Symbolic-Augmented Language Models via Code/Text Guidance

Toward universal steering and monitoring of AI models

SPRI: Aligning Large Language Models with Context-Situated Principles

MedRAX: Medical Reasoning Agent for Chest X-ray

Adaptive Exploration for Multi-Reward Multi-Policy Evaluation

Wake-Informed 3D Path Planning for Autonomous Underwater Vehicles Using A* and Neural Network Approximations

Fast Large Language Model Collaborative Decoding via Speculation

A Statistical Learning Perspective on Semi-dual Adversarial Neural Optimal Transport Solvers

Joint Localization and Activation Editing for Low-Resource Fine-Tuning

KBQA-o1: Agentic Knowledge Base Question Answering with Monte Carlo Tree Search

Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability

Chain of Grounded Objectives: Bridging Process and Goal-oriented Prompting for Code Generation

Re-ranking Using Large Language Models for Mitigating Exposure to Harmful Content on Social Media Platforms

Beyond Reward Hacking: Causal Rewards for Large Language Model Alignment

Tensor Product Attention Is All You Need

VideoRAG: Retrieval-Augmented Generation over Video Corpus

Neural Networks as Universal Finite-State Machines: A Constructive Deterministic Finite Automaton Theory

Created by

Haebom

저자

Sahil Rajesh Dhayalkar

개요

본 논문은 피드포워드 신경망이 유니버설 유한 상태 기계(N-FSM)임을 확립하는 완전한 이론적 및 경험적 프레임워크를 제시합니다. 유한 깊이의 ReLU 및 임계값 네트워크가 결정적 유한 오토마타(DFA)의 상태 전이를 깊이 방향 신경망 계층으로 펼침으로써 정확하게 시뮬레이션할 수 있음을 증명하고, 필요한 깊이, 너비 및 상태 압축에 대한 공식적인 특성을 제시합니다. DFA 전이는 선형적으로 분리 가능하며, 이진 임계값 활성화는 지수적 압축을 허용하고, Myhill-Nerode 동치 클래스는 분리 가능성을 유지하면서 연속적인 잠재 공간에 포함될 수 있음을 보여줍니다. 또한 고정된 깊이의 피드포워드 네트워크는 무한한 메모리를 필요로 하는 비정규 언어를 인식할 수 없다는 표현력의 경계를 공식화합니다. 이전의 휴리스틱 또는 프로빙 기반 연구와 달리, 본 논문은 구성적인 증명을 제공하고 모든 주장을 경험적으로 검증하는 명시적인 DFA-펼침 신경망 아키텍처를 설계합니다. 본 연구 결과는 심층 학습, 오토마타 이론 및 신경 기호 계산을 연결하여 이산 기호 프로세스가 연속적인 신경 시스템에서 어떻게 구현될 수 있는지에 대한 엄격한 청사진을 제공합니다.

시사점, 한계점

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시사점:

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피드포워드 신경망이 DFA를 정확하게 시뮬레이션할 수 있음을 수학적으로 증명하고, 그 아키텍처를 제시함으로써 심층 학습과 오토마타 이론 간의 엄밀한 연결을 확립했습니다.

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DFA 전이의 선형 분리 가능성과 이진 임계값 활성화를 통한 지수적 상태 압축 가능성을 밝혔습니다.

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신경 기호 계산에 대한 새로운 이론적 토대를 제공합니다.

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심층 신경망의 표현력 한계를 명확히 정의했습니다.

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한계점:

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본 연구는 DFA에 국한되어 있으며, 더 복잡한 오토마타(예: 비결정적 유한 오토마타, 푸시다운 오토마타)에 대한 일반화는 추가 연구가 필요합니다.

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고정된 깊이의 네트워크는 비정규 언어를 인식할 수 없다는 한계가 존재합니다. 실제 응용에서는 깊이 제한이 중요한 제약이 될 수 있습니다.

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본 연구에서 제시된 아키텍처의 효율성 및 확장성에 대한 추가적인 분석이 필요합니다.

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