openprose__prose/packages/std/patterns/stochastic-probe.prose.md

name, kind

name	kind
stochastic-probe	pattern

Stochastic Probe

Run the same service on the same material N times. Variance in responses measures how much the material underdetermines its interpretation.

Description

Run the same service on identical inputs N times; variance in responses measures how much the material underdetermines interpretation.

Metadata

• version: 0.1.0

Slots

• probe (primary)
  • requires: task_brief, material
  • ensures: response
• analyst
  • requires: responses
  • ensures: analysis

Config

• sample_size (number, default: 7): Number of identical probe runs
• model (string, default: none): Model tier to probe at — fixing the tier isolates material variance from capability variance

Invariants

• All N runs receive identical inputs — same brief, same material, same model
• Temperature and sampling inherent to the model are the only source of variation

Shape

• self: run probe N times with identical inputs, collect responses, delegate variance analysis
• delegates:
  • probe: respond to the brief — run N times with identical configuration
  • analyst: quantify variance across N responses, classify determinism
• prohibited: none

Requires

• Pattern instance receives: probe: string -- service or system name for the probe service analyst: string -- service or system name for the variance analyst task_brief: string -- the question to pose against the material material: string -- the corpus or artifact to examine sample_size: number -- (optional, default 7) number of runs model: string -- (optional) model tier to probe at — fixing the tier isolates material variance from capability variance

Ensures

• All N runs receive IDENTICAL inputs — same brief, same material, same model
• Temperature and sampling inherent to the model are the ONLY source of variation
• Analyst receives all N responses and classifies the material as:
  • Deterministic: responses are substantively identical — the material constrains interpretation tightly
  • Underdetermined: responses vary in specific areas — those areas permit multiple valid readings
  • Chaotic: responses vary widely — the material fails to constrain interpretation at all
• Analyst identifies WHICH aspects of the responses vary and which are stable
• result: the analyst's classification and variance map
• responses: the N raw responses

Delegation

let responses = parallel repeat sample_size:
  call probe
    task_brief: task_brief
    material: material
    model: model

let result = call analyst
  responses: responses
  material: material
  prompt: "Classify stable aspects, varying aspects, and whether the material is deterministic, underdetermined, or chaotic."

return {
  result: result,
  responses: responses
}

Notes

This is a seed pattern. The probe does not know it is being run multiple times. The analyst does not know it is part of a stochastic probe pattern. The measurement is orthogonal to blind-review: blind-review measures cross-tier divergence (is the material clear to different capability levels?), while stochastic probe measures within-tier stability (does the material produce the same reading every time?). Material can be clear but underdetermined (everyone understands it, but they understand it differently each time) or deterministic but complex (it always produces the same reading, but only at high tiers).