What We Are Exploring

DOMAIN 01

Novel Neural Architectures

Hybrid recurrent models (HELIX H-E-R blocks), selective state-space models, Hebbian plasticity with neuromodulation and sleep-style consolidation (SynapNet / HibbieFormer), and physics-inspired field layers (FieldTransformer).

“Do recurrent hybrids and biologically inspired plasticity offer practical advantages over standard transformers at scales one machine can train?”

DOMAIN 02

Local Intelligence

Foundation models trained end-to-end on hardware the lab owns — M3 Max unified memory, custom Metal kernels, memory-mapped corpora — plus edge TTS on Raspberry Pi.

“How much frontier-style capability can be built with zero rented compute?”

DOMAIN 03

Agentic Intelligence

Remy — the D-CSIL Lab Rat — a production terminal agent with a deterministic policy engine, reversible git-backed checkpoints, and durable memory. It replaced the earlier Hermes and OpenClaw agents.

“Can an autonomous engineering agent be safe, auditable, and reversible by construction rather than by model goodwill?”

DOMAIN 04

Machine Memory

Cortex: shared durable memory for every agent in the lab — facts, decisions, and context that survive across sessions, served over REST and MCP.

“What memory architecture lets many agents share one accumulating body of lab knowledge?”

DOMAIN 05

Autonomous Research Systems

The Autonomous R&D Lab: objective → plan → agents → experiments → evidence → critique → revision, with claim gates and immutable audit logs.

“Can a governed loop of specialist agents run real experiments and only publish claims that survive skeptical review?”

DOMAIN 06

Creative & Audio Intelligence

Conditional drum generation, 6-stem music source separation on MLX, XTTS voice cloning, and edge TTS — machine learning applied to sound.

“How far can locally trained models go in generating and deconstructing music and voice?”

DOMAIN 07

Physical AI & Embedded Systems

Connecting intelligence to the physical world: custom electronics, sensor integration, embedded computers, and edge devices that let AI observe and act on real environments.

“What does it take for AI to perceive, measure, and respond to the physical world — not just text?”