AI Research Division

Project: AiNEE

Artificial Intelligence Neural Efficiency Engine. A research platform for building memory-efficient, locally trainable AI systems capable of long-context reasoning, agentic planning, and modular expertise.

The problem we are solving

Current frontier AI systems are powerful but expensive, opaque, and difficult to adapt safely for personal, local, or constrained environments. Access to capable AI requires continuous dependence on remote infrastructure, and the systems themselves are largely uninspectable. AiNEE investigates an alternative path: whether structured runtime memory, asynchronous scheduling, compression-aware context handling, and expert specialization can improve efficiency and controllability without requiring ever-larger monolithic models.

Technical approach

Tile-native runtime architecture

Memory, context, computation, compression, and routing are treated as first-class research objects rather than incidental infrastructure. GPU-backed tile memory enables explicit state and context representation at runtime.

Teacher-to-student distillation

High-quality behavior is inherited from stronger external systems through curated distillation pipelines with enforced quality gates. The local student model trains on curated signals, not uncontrolled data accumulation.

Async scheduler and memory compression

A scheduler-driven system manages movement between active, cached, and compressed memory states, improving throughput under context load. Benchmarks demonstrate meaningful scheduling improvements over synchronous execution in controlled profiles.

Modular expert specialization

A shared behavioral foundation is trained first. Domain experts for coding, agentic planning, long-context reasoning, tool use, math, and review are then added as specialized modules, enabling targeted capability growth without full retraining.

Research aims

1. Validate tile-native memory as an efficient substrate for long-context AI. Measure latency, compression quality, recall behavior, and stability under increasing context pressure.
2. Develop a repeatable teacher-to-local-student distillation loop. Generate curated training signals, enforce quality gates, train locally, and evaluate behavioral deltas before accepting model updates.
3. Build a shared base model behavior before specialization. Establish a coherent local assistant foundation, then fork domain experts for targeted tasks.
4. Improve safety, reproducibility, and rollback for local AI training. Maintain explicit manifests, checkpoint provenance, evaluation reports, and recovery procedures so experiments can be audited and reversed.

Current progress

The runtime baseline has passed automated self-tests, manifest validation, benchmark checks, distillation quality gates, and native response evaluations. Early local student behavior is operational. Active work continues on agentic planning and long-context expert training. AiNEE is positioned as a platform for responsible AI experimentation — emphasizing quality gates, checkpoint rollback, and measurable before/after evaluation rather than uncontrolled capability growth.

Broader impact

If successful, AiNEE could lower the barrier to useful local AI assistants, reduce dependence on remote inference, and provide a more transparent framework for studying AI memory, autonomy, and specialization. Its emphasis on quality gates and rollback makes it suitable for responsible experimentation in personal, research, and small-lab settings.

Research inquiries and collaboration

We are selectively open to collaboration, evaluation partnerships, and grant-aligned research discussion.