Why the deterministic policy layer remains authoritative
A CelestyxAI evidence synthesis on separating safety-critical clinical constraints from probabilistic language-model behavior.
The strongest evidence does not support asking an LLM to be its own safety boundary. When a clinical constraint can be formalized, it should live in a versioned, testable control outside the model; the model can assist around that control, not replace it.
Failure mode
Benchmark strength does not guarantee knowledge integrity. Data-poisoning work showed harmful medical behavior can remain hidden behind conventional benchmark scores, while red-team studies expose failures that average-case tests miss.
Architectural response
CelestyxAI therefore treats deterministic policy as the authority for explicit escalation criteria, exclusions and safety-critical workflow constraints. LLM orchestration remains bounded to tasks such as structuring, synthesis and contextual assistance.
Validation consequence
Every policy path can be unit-tested, versioned and replayed independently of the model. Model upgrades can then be evaluated without silently changing the clinical boundary itself.
This brief is a CelestyxAI synthesis of the cited external evidence and accumulated project design decisions. It is not a peer-reviewed scientific publication and does not establish clinical efficacy.