SEEMR (Self-Evolving Model Router)
VDF AI's architecture for governed, observable, adaptive LLM routing across providers and local models.
What is SEEMR (Self-Evolving Model Router)?
SEEMR formalises model selection as a runtime decision shaped by policy, cost, latency, energy, and outcome feedback — rather than a static configuration. See the SEEMR architecture page and the white paper The Self-Evolving Model Router.
What is an example of SEEMR (Self-Evolving Model Router)?
A regulated extraction task is restricted to local models. SEEMR selects the smallest local model that meets the measured quality threshold, but routes a difficult approved research task to a larger model when policy and expected outcome justify it.
How is SEEMR (Self-Evolving Model Router) different from related concepts?
A static router follows fixed rules. A self-evolving router updates selection using evaluation and outcome evidence while remaining inside explicit governance constraints.
What should enterprises evaluate for SEEMR (Self-Evolving Model Router)?
- Establish hard policy gates and quality thresholds before optimizing cost, latency, or energy.
- Use representative task feedback and guard against learning from noisy, biased, or manipulated outcomes.
- Keep route changes versioned, explainable, reversible, and covered by regression tests.
Read the full guide: SEEMR (Self-Evolving Model Router) — in-depth article →
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Putting SEEMR (Self-Evolving Model Router) to work?
VDF AI runs governed AI agents on your own infrastructure — on-premises, sovereign cloud, or air-gapped. Book a working session to map the architecture.
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