AI Research

Project: AiNEE

Artificial Intelligence Neural Efficiency Engine. St1tch Street's research into helping AI systems retain and correctly use information over time, within practical memory and computing budgets.

Why context matters

Useful AI needs continuity: the ability to recover an earlier fact, distinguish it from a similar statement, and recognize when a later correction changes what should be used. AiNEE investigates how to preserve that continuity after information has left a model's active context.

Our goal is to make longer-running AI workflows more dependable without relying on ever-larger active context windows or unconstrained resource use. This supports St1tch Street's broader work on efficient local AI and systems that remain inspectable and under their owner's control.

Current research direction

Persistent context and selective recall

We are developing an experimental context-management layer around existing models. It explores how information can be stored outside the active model window and made available again when needed, while keeping the working context bounded.

Correctness across time and changes

The research examines more than whether stored information can be retrieved. It asks whether the system selects the right source, respects its scope, applies corrections, and preserves uncertainty when the available evidence does not support a definite answer.

Controlled delivery and resource use

Prototype work includes context storage and delivery, revision checks, resource controls, and reproducible evaluation workflows. Experimental selection mechanisms are evaluated separately from the deterministic controls that govern access and delivery.

What we are testing

• Delayed recall: recover and correctly apply information after substantial intervening material has displaced it from the active context.
• Corrections and ambiguity: distinguish similar facts, honor later revisions, and avoid unsupported conclusions.
• Fair comparisons: compare context-management approaches against recent-history and persistent-storage baselines under matched model and resource conditions.
• Practical costs: measure memory use, latency, storage, and recovery behavior alongside answer quality.

Current progress

Research status reviewed October 26, 2026

The project has implemented research components and recorded checks for context preparation, transport, and delivery controls. Those checks establish specific component behavior; the current approach's combined evaluation of useful delayed recall and resource costs remains incomplete.

Experimental execution was paused on October 25 to free computing resources, after preparation for the latest evaluation was completed. Full retention-accuracy and efficiency measurements for the current design remain pending.

Broader research

AiNEE's wider research includes efficient runtimes, compression, teacher-to-student learning, and modular expertise. The present focus is validating the context pipeline around existing models, with component checks and full-workflow evaluations tracked separately.

Our Systems Research program explores the operating environments and isolation boundaries needed to support increasingly capable AI workloads.

Research inquiries and collaboration

We welcome inquiries about evaluation, research collaboration, and support for this work.