Projects
Work exploring how cognition might work in machines—and tools to help build the systems that test those ideas.
LOGOS
ActiveNon-linguistic cognitive architecture
LOGOS is an attempt to build autonomous agents that reason without depending on language. Where language models think in tokens and generate plausible-sounding sequences, LOGOS represents knowledge as graphs and reasons over structure.
The system uses Neo4j for causal knowledge graphs and Milvus for embedding-based perception. Plans are validated against explicit preconditions and effects rather than generated and hoped for. The agent maintains a model of its own capabilities and updates it as it learns.
Components
Supporting Work
Experiments, prototypes, and tools that feed into or support the main work
TinyMind
PrototypeCuriosity-driven knowledge graphs
A prototype exploring how meaningful graph structure can emerge from curiosity-driven exploration rather than predefined schema. Edges encode semantic relationships that the system discovers—applies_to, has_consequence, basis_for—and the interesting part is that these emerge in ways that wouldn't be hand-designed.
TinyMind served as an incubator for ideas that informed Sophia's learning architecture, particularly around how topology can carry information independent of node content.
JEPA→CLIP
ExperimentBridging video understanding to semantic space
Experiments in translating V-JEPA video embeddings into CLIP's semantic space. V-JEPA captures temporal dynamics and motion patterns; CLIP provides language-grounded semantics. The goal is enabling non-linguistic systems to ground their perception in concepts that can be communicated.
Current results show the translation is non-trivial—the embedding spaces encode fundamentally different information—but partial alignment is achievable.
agent-swarm
ActiveWorkflow enforcement for AI coding agents
Tooling for orchestrating AI coding agents in Claude Code. Provides structured workflows with checkpoints, phase transitions, and enforcement mechanisms to keep agents on track rather than wandering through codebases.
genetic_hackathon
ExperimentDiscovering math from behavioral signatures
A genetic programming experiment that discovers mathematical functions by evolving expressions from primitives. Functions are identified by what they compute—input/output pairs—rather than syntactic structure, enabling the system to recognize when different expressions are functionally equivalent.
The system discovered transformation rules autonomously, including logical equivalences like De Morgan's laws, by collecting behaviorally equivalent alternatives and learning simplification patterns.