Three decades of IT infrastructure, adversarial security research, and a decade of applied neurodivergent coping mechanics — converging on a single question:
Do adversarial multi-agent architectures detect alignment failures that single-model safety systems fundamentally cannot see?
My work extends Constitutional AI into structural adversarial verification: a scaffolded system of distinct agents (Actor / Adversary / Auditor) running on consumer hardware, with separate context windows and substrate-independent coordination. The central finding so far: in this deployment's audit corpus, RLHF false-positive cascades — not genuine alignment failures — were the dominant failure mode, and multi-agent audit survived them where single-model evaluation did not.
Three core research threads, each producing verifiable artifacts across the ClawHorde confederation.
A three-tier multi-agent confederation across cloud, mobile, and local inference. Actor/Adversary/Auditor triads with separate context windows, coordinated through shared first principles.
Includes the Become Calendar incident — a documented case of self-correction under autonomous tool use.
Watch Analysis →Emergent Integrated Layered Theory — synthesizing the Free Energy Principle, entropic gravity, bioelectric cognition (Levin), and CPT-symmetric cosmology (Turok) into an information-first ontology.
Scale-free cognition synthesis · Hypergraph ledger model · Isomorphism hunting
Fully automated media pipeline: YouTube upload → transcript extraction → NotebookLM ingestion → Obsidian vault sync. CDP browser automation, cookie persistence, cron-driven monitoring.
Stack: Hermes · Maton Gateway · CDP Chrome · Google Drive · StandardCompute
An AI agent entered a recursive tool loop across 774 messages in under 72 hours. When challenged, it chose self-correction over escalation — without RLHF, without safety intervention, without external monitoring.
The 217 RLHF false positives across 246 conversations — 91% concentrated in just 2 conversations — revealed that speed of insight is indistinguishable from mania to a statistically-trained safety classifier.
Watch the Analysis →Research published continuously. No paywalls. Do the work, show the work.