Articles, analyses, and explorations at the frontier of cognitive AI systems, infrastructure design, and machine intelligence architecture.
Exploration of perception-planning-action-reflection loops as a foundation for robust autonomous AI systems. How borrowing from cognitive science yields more resilient architectures than pure instruction-following.
How embedding-based task classification can intelligently route requests to the cheapest capable model, with fallback chains and real-time health monitoring across nine model backends.
Deep dive into the three-tier memory architecture powering NEXUS: how events become facts become skills through nightly consolidation pipelines, analogous to biological memory formation.
Why filtering isn't enough for production AI systems. A framework for action-level risk scoring, reversibility analysis, and human escalation triggers that scales with agent capability.
Investigating hybrid approaches that combine dense vector retrieval with structured knowledge graph traversal — trading some recall speed for dramatically improved factual precision.
Combining classical Hierarchical Task Network planning with LLM-guided method selection — getting the best of symbolic planning's guarantees and neural flexibility.