Architecture & AI Research Models
AEG and SHIELD explore two architecture boundaries for autonomous systems. PallattuLM is a separate experimental language-model research track exploring whether useful learning can continue without repeatedly updating the entire neural network.
Relationship
AEG can govern actions without SHIELD. SHIELD can establish confidence without AEG. PallattuLM addresses a different question entirely: how a language system might acquire, retain, consolidate, and selectively encode new knowledge after deployment. Each track has its own claims, evidence, and limitations.
Choose where to start
Each model has its own scope, evidence, versions, and adoption path.
The Agentic–Event–Governed Architecture Model separates autonomous intelligence from execution authority through structured intent, observable coordination, and independent runtime governance.
SHIELD explores Independent Evidence Reinforcement: building confidence from diverse, genuinely independent observations before progressively stronger remediation is justified.
PallattuLM combines a transformer baseline with experiments in persistent memory, semantic consolidation, autonomous pattern discovery, and sparse routed neural plasticity.
A consistent research journey
The work is published with explicit claims, implementation artifacts, measurements, and limitations.
Start with the problem, central principle, scope, and canonical definition.
Use applicability guidance, review questions, and known failure modes.
Follow worked examples, implementation patterns, diagrams, and public code.
Inspect evidence, limitations, version history, and unresolved questions.
Public artifacts
