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AI Research / Information Space Dynamics

Measuring the geometry of thought in language models

Independent research into empirical observability of large language model behavior — trajectory mechanics, attractor dynamics, and the information geometry of conversation.

Papers & Publications

Framework

Information Space Dynamics

The theoretical foundation — velocity, curvature, torsional resistance, and attractor-approach diagnostics for LLM trajectory analysis.

DOI: 10.5281/zenodo.20690590 ↗

Instrument

Djehuti Cyberscope AI+

Protocol specification for the empirical measurement workbench — pure-observability rules, ingestion model, and measurement primitives.

DOI: 10.5281/zenodo.20739448 ↗

Coming soon

Articles & Notes

Shorter-form writing on measurement findings, anomalies, and theoretical extensions — forthcoming.

Projects

Active

Djehuti Cyberscope AI+

An empirical measurement workbench for studying LLM behavior in information space. F# backend, React dashboard, AI analyst grounded in ISD theory.

About

W. Westlake is an independent AI researcher focused on empirical observability of large language model behavior. The work centers on building rigorous measurement instruments that respect the boundary between what can be directly observed and what must remain a calibrated estimate.

The ISD framework treats conversation as a trajectory through information space — studying velocity, curvature, torsional accumulation, and attractor-approach signatures without requiring access to model internals.

Current work: expanding Djehuti's measurement capabilities, formalizing the zeta-4 observables, and developing multi-LLM interferometry protocols for comparative trajectory analysis.