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Daily Arxiv

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Photonic Fabric Platform for AI Accelerators

Achieving Robust Channel Estimation Neural Networks by Designed Training Data

Can Mental Imagery Improve the Thinking Capabilities of AI Systems?

Characterizing State Space Model (SSM) and SSM-Transformer Hybrid Language Model Performance with Long Context Length

PGT-I: Scaling Spatiotemporal GNNs with Memory-Efficient Distributed Training

Robust 3D-Masked Part-level Editing in 3D Gaussian Splatting with Regularized Score Distillation Sampling

A Lightweight and Robust Framework for Real-Time Colorectal Polyp Detection Using LOF-Based Preprocessing and YOLO-v11n

HMID-Net: An Exploration of Masked Image Modeling and Knowledge Distillation in Hyperbolic Space

Synchronizing Task Behavior: Aligning Multiple Tasks during Test-Time Training

Resolving Token-Space Gradient Conflicts: Token Space Manipulation for Transformer-Based Multi-Task Learning

Fast Bilateral Teleoperation and Imitation Learning Using Sensorless Force Control via Accurate Dynamics Model

VisualSpeaker: Visually-Guided 3D Avatar Lip Synthesis

Reviving Cultural Heritage: A Novel Approach for Comprehensive Historical Document Restoration

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning

Learning Software Bug Reports: A Systematic Literature Review

Rethinking Data Protection in the (Generative) Artificial Intelligence Era

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting

TopoStreamer: Temporal Lane Segment Topology Reasoning in Autonomous Driving

“Before, I Asked My Mom, Now I Ask ChatGPT”: Visual Privacy Management with Generative AI for Blind and Low-Vision People

QLPro: Automated Code Vulnerability Discovery via LLM and Static Code Analysis Integration

FedWSQ: Efficient Federated Learning with Weight Standardization and Distribution-Aware Non-Uniform Quantization

Plan for Speed: Dilated Scheduling for Masked Diffusion Language Models

Bridging the Digital Divide: Small Language Models as a Pathway for Physics and Photonics Education in Underdeveloped Regions

DaMO: A Data-Efficient Multimodal Orchestrator for Temporal Reasoning with Video LLMs

Dynamic Context Tuning for Retrieval-Augmented Generation: Enhancing Multi-Turn Planning and Tool Adaptation

Specification and Evaluation of Multi-Agent LLM Systems -- Prototype and Cybersecurity Applications

PhysioWave: A Multi-Scale Wavelet-Transformer for Physiological Signal Representation

Draft-based Approximate Inference for LLMs

Label-semantics Aware Generative Approach for Domain-Agnostic Multilabel Classification

SemiOccam: A Robust Semi-Supervised Image Recognition Network Using Sparse Labels

Adversarial bandit optimization for approximately linear functions

Know Or Not: a library for evaluating out-of-knowledge base robustness

Leveraging Vision-Language Models for Visual Grounding and Analysis of Automotive UI

DualReal: Adaptive Joint Training for Lossless Identity-Motion Fusion in Video Customization

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

Return Capping: Sample-Efficient CVaR Policy Gradient Optimization

AnyTSR: Any-Scale Thermal Super-Resolution for UAV

Enhanced Pruning Strategy for Multi-Component Neural Architectures Using Component-Aware Graph Analysis

Executable Functional Abstractions: Inferring Generative Programs for Advanced Math Problems

Measuring Leakage in Concept-Based Methods: An Information Theoretic Approach

APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated Agent-Human Interplay

The Dual-Route Model of Induction

Detecting PTSD in Clinical Interviews: A Comparative Analysis of NLP Methods and Large Language Models

SWI: Speaking with Intent in Large Language Models

A Study of LLMs' Preferences for Libraries and Programming Languages

TruthLens: Explainable DeepFake Detection for Face Manipulated and Fully Synthetic Data

Sampling Decisions

Federated Continuous Instruction Tuning

Fine-Tuning Diffusion Generative Models via Rich Preference Optimization

BriLLM: Brain-inspired Large Language Model

Studying Classifier(-Free) Guidance From a Classifier-Centric Perspective

RealGeneral: Unifying Visual Generation via Temporal In-Context Learning with Video Models

Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning

PLADIS: Pushing the Limits of Attention in Diffusion Models at Inference Time by Leveraging Sparsity

DynamicID: Zero-Shot Multi-ID Image Personalization with Flexible Facial Editability

Symbolic Mixture-of-Experts: Adaptive Skill-based Routing for Heterogeneous Reasoning

OMNISEC: LLM-Driven Provenance-based Intrusion Detection via Retrieval-Augmented Behavior Prompting

Too Much to Trust? Measuring the Security and Cognitive Impacts of Explainability in AI-Driven SOCs

Attend or Perish: Benchmarking Attention in Algorithmic Reasoning

Can Optical Denoising Clean Sonar Images? A Benchmark and Fusion Approach

Brain Foundation Models: A Survey on Advances in Neural Signal Processing and Brain Discovery

Winning Big with Small Models: Knowledge Distillation vs. Self-Training for Reducing Hallucination in Product QA Agents

Detecting Benchmark Contamination Through Watermarking

MEMERAG: A Multilingual End-to-End Meta-Evaluation Benchmark for Retrieval Augmented Generation

Steering into New Embedding Spaces: Analyzing Cross-Lingual Alignment Induced by Model Interventions in Multilingual Language Models

Analyze the Neurons, not the Embeddings: Understanding When and Where LLM Representations Align with Humans

MKE-Coder: Multi-Axial Knowledge with Evidence Verification in ICD Coding for Chinese EMRs

An Overall Real-Time Mechanism for Classification and Quality Evaluation of Rice

Layerwise Recall and the Geometry of Interwoven Knowledge in LLMs

Learning in Strategic Queuing Systems with Small Buffers

BARNN: A Bayesian Autoregressive and Recurrent Neural Network

HEPPO-GAE: Hardware-Efficient Proximal Policy Optimization with Generalized Advantage Estimation

CGP-Tuning: Structure-Aware Soft Prompt Tuning for Code Vulnerability Detection

A recent evaluation on the performance of LLMs on radiation oncology physics using questions of randomly shuffled options

A Survey on Large Language Model-Based Social Agents in Game-Theoretic Scenarios

PEMF-VTO: Point-Enhanced Video Virtual Try-on via Mask-free Paradigm

Understanding the Design Decisions of Retrieval-Augmented Generation Systems

DOGR: Towards Versatile Visual Document Grounding and Referring

Ev2R: Evaluating Evidence Retrieval in Automated Fact-Checking

DualSwinUnet++: An Enhanced Swin-Unet Architecture With Dual Decoders For PTMC Segmentation

PerspectiveNet: Multi-View Perception for Dynamic Scene Understanding

AlphaDPO: Adaptive Reward Margin for Direct Preference Optimization

Continual Learning with Neuromorphic Computing: Foundations, Methods, and Emerging Applications

FlexiTex: Enhancing Texture Generation via Visual Guidance

ASMA: An Adaptive Safety Margin Algorithm for Vision-Language Drone Navigation via Scene-Aware Control Barrier Functions

The unknotting numbers, hard unknot diagrams, and reinforcement learning

Hierarchical Reinforcement Learning for Temporal Abstraction of Listwise Recommendation

Enhancing Natural Language Inference Performance with Knowledge Graph for COVID-19 Automated Fact-Checking in Indonesian Language

CVPT: Cross Visual Prompt Tuning

Proficient Graph Neural Network Design by Accumulating Knowledge on Large Language Models

Stimulating Imagination: Towards General-purpose "Something Something Placement"

Why Does New Knowledge Create Messy Ripple Effects in LLMs?

A Mathematical Framework and a Suite of Learning Techniques for Neural-Symbolic Systems

How to Leverage Predictive Uncertainty Estimates for Reducing Catastrophic Forgetting in Online Continual Learning

Towards the Next Frontier in Speech Representation Learning Using Disentanglement

Hierarchical Prompting Taxonomy: A Universal Evaluation Framework for Large Language Models Aligned with Human Cognitive Principles

Which Experiences Are Influential for RL Agents? Efficiently Estimating The Influence of Experiences

Oversmoothing Alleviation in Graph Neural Networks: A Survey and Unified View

OCK: Unsupervised Dynamic Video Prediction with Object-Centric Kinematics

Benchmarking Mobile Device Control Agents across Diverse Configurations

A Comparative Analysis of Statistical and Machine Learning Models for Outlier Detection in Bitcoin Limit Order Books

Created by

Haebom

Author

Ivan Letteri

Outline

This paper conducts a comprehensive comparative analysis of powerful statistical methods and advanced machine learning techniques for outlier detection in cryptocurrency order books (LOBs). Within an integrated test environment called AITA Order Book Signal (AITA-OBS), we evaluate the effectiveness of 13 different models to identify the best method for detecting potential manipulative trading behavior. Through backtesting on a dataset of 26,204 records from major exchanges, we empirically evaluate the best performing model, Empirical Covariance (EC), which achieves a return of 6.70% higher than the standard buy-and-hold strategy. These results highlight the effectiveness of outlier-based strategies and provide insight into the trade-offs between model complexity, trading frequency, and performance. This study extends the research on cryptocurrency market microstructure, provides a rigorous benchmark of outlier detection models, and highlights their potential for improving algorithmic trading and risk management.

Takeaways, Limitations

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Takeaways:

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We highlight the importance of outlier detection in cryptocurrency LOB and demonstrate that effective outlier detection strategies can improve algorithmic trading and risk management.

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We empirically demonstrate that the Empirical Covariance (EC) model is effective in detecting cryptocurrency LOB outliers.

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Provides insights into the correlation between model complexity, transaction frequency, and performance.

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Contributes to the study of the cryptocurrency market microstructure.

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Limitations:

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There may be limitations to generalizability as data from only specific exchanges is used.

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The number of models used in the analysis may be limited.

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Performance in real market environments may differ from backtesting results.

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Specific details of the AITA-OBS test environment are limited and may require review for reproducibility.

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