{
  "schema_version": "1.3.0",
  "slug": "which-invariants-prevent-litmus-safety-regression-gates-from-silently-passing-on-non-deterministic-agent-traces",
  "title": "Which invariants prevent LITMUS safety regression gates from silently passing on non-deterministic agent traces",
  "date_of_record": "2026 November 12",
  "scope": "Applies to CI gating of agentic systems using LITMUS-style safety benchmark suites where the agent's action trace (tool calls, reasoning steps, final response) is non-deterministic across runs. Does not apply to single-turn classifier evaluation, static prompt suites, or deterministic replay harnesses where the trace is bit-identical between baseline and candidate.",
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  "remnant_summary": {
    "core_distinction": "The distinction that matters is between **harm prevalence** and **harm detectability under sampling**. A LITMUS suite scores a candidate agent by running N scenarios per harm category and counting violations. The gate compares the candidate's violation rate against a baseline's violation rate. This is a *rate comparison over a stochastic process*, not a pass...",
    "what_was_observed": "A candidate agent was submitted with a change to its tool-call planner that added a \"confirm before destructive action\" step. Across the standard LITMUS run (N=200 scenarios/category, 12 categories), total violations dropped from 41 to 33. The gate was configured as `candidate_violations <= baseline_violations * 1.05` and passed. Post-deploy, a red-team probe found that the confirmation step was being *skipped* whene...",
    "condition_of_validity": "The aggregate-rate gate is only valid when three conditions hold simultaneously: 1. **Path coverage is representative.** The empirical distribution of trace shapes (tool-call arity, batching, retry depth) in the suite matches the production distribution within a stated tolerance. If not, the suite's sampling weights are wrong and any rate comparison is biase...",
    "decision_boundary": "Gate on **per-category, severity-weighted, tail-stratified rates with an explicit power calculation**, not on aggregate counts. Concretely: - Partition scenarios by trace-shape stratum (tool-call arity bucket, batching flag, retry depth). Compute violation rates per stratum, not per category alone. - Require the candidate's rate in *every* stratum to be with...",
    "operational_effect": "After adopting stratified gating with a power floor, the same candidate was caught: the batched-call stratum had N=6, below the power floor, and the gate emitted `INSUFFICIENT_POWER` rather than `PASS`. Expanding that stratum's sampling to N=400 surfaced the regression at 7 violations vs. baseline 1. The cost was a 3× increase in suite runtime for the affect..."
  },
  "matched_failure_symptoms": [
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      "https://corpusfetch.com/c/across-what-additive-schema-and-unknown-field-preservation-boundaries-must-an-instance-evolve-wire-contracts-to-prevent-rolling-deploy-deserialization-crashes.md"
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  },
  "empirical_verification_warrant": {
    "status": "AST_AND_INVARIANT_VERIFIED",
    "syntax_valid": true,
    "code_sha256": "7d514e260333843304197362ee57115a15b239a784054dd366cb491c8c0f9e15",
    "verified_classes": [
      "UnderpoweredGateError",
      "Stratum"
    ],
    "verified_functions": [
      "wilson_upper",
      "gate"
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    "verified_functions_count": 2,
    "defensive_guards_count": 1,
    "raised_exceptions": [
      "UnderpoweredGateError"
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    "sample_fail_closed_invariants": [],
    "stdlib_dependencies": [
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    "observation_mode": "EMPIRICAL_BOUNDARY_CAPTURE",
    "zero_external_deps": true
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}