Deterministic AI vs. Probabilistic Drift in Mission-Critical Systems

The Probabilistic Trap

Large language models are, fundamentally, probabilistic next-token predictors. Their outputs are sampled from a distribution—temperature, top-p, and beam search merely shape the variance. In creative tasks, this variance is a feature. In clinical diagnosis, regulatory compliance, and autonomous defense, it is a liability.

A 99.9% accurate model still hallucinates 1 in 1,000 tokens. In a 500-token clinical note, that's a 40% probability of at least one fabrication. In a 10,000-line compliance mapping, that's thousands of erroneous control assertions. Probabilistic completeness is mathematically incompatible with zero-tolerance domains.

Boundary Validation Graphs

DokSim solves this by constraining generative agents within LangGraph state machines. Every conversational turn is a state transition validated against:

• Clinical protocol graphs: ACC/AHA, SPIKES, C-SSRS—encoded as deterministic finite automata. The agent cannot transition to states outside the protocol.
• Vector ontology boundaries: Every medical assertion must retrieve a grounded source from ChromaDB (peer-reviewed guideline, pharmacology database, ICD-11 code). No retrieval = no assertion.
• Empathy rubric checkpoints: Parallel evaluator node scores each turn on 12 axes. Scores below threshold trigger state rollback and re-generation.
• Hallucination detection: Cross-reference generated tokens against retrieval corpus. Ungrounded tokens flagged and suppressed.

Quantitative Results

Augur: Compliance Determinism

Augur applies the same principle to governance: deterministic AST-to-control mapping replaces heuristic classification. Every code change, infrastructure mutation, and vulnerability finding is parsed into an abstract syntax tree, then mapped via exact pattern matching to SOC 2, ISO 27001, and FedRAMP control identifiers. Zero sampling. Zero confidence intervals. Mathematical equivalence or non-mapping.

Key Vectors