A research project on distributed AI specialization.
SmenaAI is a working proof of concept for a distributed reasoning architecture built from specialist nodes. The research question is simple: can routed specialists retain domain depth and surface disagreement better than one broad model with the same compute? Those specialists can include custom foxlings with domain-specific adapters, memory, and tooling.
Why study a different architecture.
Consensus hides risk
Polished answers often flatten objections that should still shape the final decision. Once dissent disappears into a smooth summary, the system can sound aligned while dropping the exact risk signal a human needed to see.
Durable expertise is hard
Adding context helps temporarily, but durable expertise is still fragile. A system updated for one domain may quietly lose what it previously knew and not surface that loss when it matters.
Broad models flatten in narrow domains
General systems sound confident across many topics, but they often stay shallow where legal, compliance, security, medical, and technical diligence demand depth.
What the PoC is testing.
The core bet is that domain depth should come from adding and adapting the right specialists, not from repeatedly rewriting one shared model. The PoC focuses on routing, bounded participation, structured disagreement, and local adaptation.
Selective Activation
Only the relevant foxlings and clusters should activate for a query, instead of waking the full system every time.
Specialist Growth
Rapid domain expansion should come from adding the right specialists, not from layering more generic critique onto the same broad model.
Built-In Error Detection
Critique has an explicit role in how answers are formed. Objections surface before synthesis, not after, which matters in high-stakes work.
Adaptive Learning
RAG changes what the model sees. Local adaptation changes how a node behaves. Smena tests when retrieval stops being enough.
Why the architecture is different.
The goal is not just to improve one answer. It is to extend a reasoning system into new domains while preserving visible disagreement, local adaptation, and inspectable synthesis.
Current PoC Snapshot.
cognitive clusters, each with a distinct reasoning role
foxlings in the core cognitive pool
foxlings activated per query - selective, not full-system
adaptation depths: memory, LoRA, fine-tuning
Current Status.
SmenaAI is a research program with a working PoC. The current focus is Stage A: testing whether routing specialization and disciplined memory beat simpler baselines on retention, stability, and cost.
What exists now
A working PoC with routed execution, cognitive clusters, bounded activation, inspectable debate traces, early dataset evaluation workflows, and support for custom foxlings with domain-specific adapters and tooling.
What is being tested
Retention under sequential multi-domain learning, routing specialization versus simpler baselines, and whether disciplined memory beats naive accumulation.
What is not being claimed
This is not AGI, not a finished enterprise product, and not proof yet that local adaptation beats every monolithic baseline.