AI Research

Overview

This research examines how machine learning models can be applied to onchain analysis, with a focus on distinguishing meaningful behavioral signal from noise across highly irregular wallet and contract activity. The objective is to evaluate where model based classification adds reliable value beyond fixed heuristics, and where it does not.

Background

Onchain behavior does not follow the structured, consistent patterns typical of traditional financial datasets. Wallets behave differently depending on intent, tooling, and context, and a single fixed rule is rarely sufficient to classify behavior accurately across a large and varied population of wallets and contracts. This variability is the primary motivation for exploring model based approaches rather than static rule sets alone.

Static pattern matching can identify known cases but tends to generalize poorly to new or slightly modified behavior. Model based classification offers a way to generalize across variations in behavior, provided the underlying training data and evaluation methodology are sound.

Technical Concepts

The following concepts are relevant to this research.

Wallet clustering refers to the process of grouping wallets that are likely related based on shared funding sources, transaction patterns, or behavioral similarity, rather than explicit onchain links alone.

Deployer reputation scoring refers to evaluating a deployer wallet based on its historical launch activity, including prior token outcomes and deployment frequency, in order to produce a structured assessment rather than a binary label.

Behavioral risk classification refers to categorizing wallet or contract activity according to observed risk indicators, such as those identified in related research on rug pull patterns and market structure, using a consistent and repeatable classification process.

Methodology

Candidate models are evaluated against historical onchain data with known outcomes, allowing classification results to be compared against confirmed cases rather than assumed correct. This includes testing model performance on wallet clustering, deployer scoring, and risk classification tasks separately, since each task presents distinct data characteristics and failure modes.

A particular focus of this methodology is identifying where classification confidence is low. Rather than treating model output as uniformly reliable, this research separates high confidence classifications from cases where the underlying data is ambiguous or insufficient, since these cases require different handling in any eventual product implementation.

Observations

Model based approaches show measurable improvement over static heuristics in identifying wallet relationships that are not explicitly connected onchain, particularly in cases involving indirect funding paths or intermediary wallets. However, performance is not uniform across all classification tasks, and certain behavioral categories remain difficult to classify with high confidence using currently available data.

Limitations

Model based classification is dependent on the quality and completeness of historical training data. Behavior that differs significantly from previously observed patterns, including newly emerging tactics, may not be classified reliably until sufficient data exists to evaluate it. Classification output should be understood as a probabilistic assessment rather than a definitive determination.

This research does not evaluate model performance in a live, adversarial setting where wallet behavior may adapt specifically to avoid classification. Current findings are based on historical and retrospective data only.

Research Objective

The objective of this research is to determine which onchain classification tasks are well suited to model based approaches, and to establish a methodology for evaluating classification confidence, as foundational work for the Risk Classification Research and Wallet Intelligence modules planned for the OwlScope Intelligence Platform. These modules are not yet available and remain under active development.

Future Direction

Future work will extend this research to evaluate model performance under more adversarial conditions, and to refine classification confidence measures as additional historical onchain data becomes available.