Acceptance Criteria for Task-specific Policies for Repeated LLM Sampling and Aggregation: What Must Be Proven

The direct answer

Write a test for “task classes and answer spaces are explicit” before execution and keep “voting over answers that cannot be normalized” as a release-blocking counterexample.

For task-specific policies for repeated LLM sampling and aggregation, the relevant audience is teams considering self-consistency or majority voting but unwilling to assume that more samples always improve an answer. The decision should cover task classification, answer normalization, dependence analysis, sample budgeting, aggregation, consequence-aware evaluation, stop rules, and recorded decisions. The supplied boundary starts with the LLM pipeline, representative task samples, answer structure, cost constraints, and acceptance rules and ends with a math-verified policy layer validated against the buyer's task mix, presented in reviewable form.

Repeated samples can agree on the same wrong answer, and open-ended work may not have a meaningful majority. No sample count is universally correct.

Turn each requirement into a proof obligation

The expected deliverable is a math-verified policy layer validated against the buyer's task mix.

Use the LLM pipeline, representative task samples, answer structure, cost constraints, and acceptance rules as the controlled starting material.

The statistical reviewer adjudicates every pass, hold, or fail verdict against these registered statements.

Cover more than the happy path

The normal flow should establish whether “task classes and answer spaces are explicit” holds. An alternate flow should vary a permitted input while testing whether “single-sample and multi-sample baselines are compared” holds. The failure flow should use a fixture demonstrating “accuracy averaged across incompatible task types” and verify containment.

Add a recovery flow for “cost counted without latency”.

Judge evidence quality and freshness

For task-specific policies for repeated LLM sampling and aggregation, a result from another environment cannot prove that “correlated errors are measured” holds in the buyer's environment.

Define pass, hold, and fail before execution

Keep sign-off independent

The implementer may produce artifacts, but the statistical reviewer should judge whether “the policy has a no-vote and escalation path” holds against criteria written before the result was seen.

Record the business decision of the task owner, the technical evidence reviewed by the platform owner, the acceptance verdict recorded by the statistical reviewer, and residual risk accepted by the release owner.

A screenshot or self-score cannot prove that “the policy has a no-vote and escalation path” holds under the failure condition “a policy tuned on the same fixtures used for release approval”.

Reopen criteria when the system changes

Changes to task classification, answer normalization, dependence analysis, sample budgeting, aggregation, consequence-aware evaluation, stop rules, and recorded decisions can invalidate a test even when the requirement text stays the same.

How the sources bound the acceptance criteria decision

For task-specific policies for repeated LLM sampling and aggregation, the live catalog limits the offer to two elements. The supplied boundary is the LLM pipeline, representative task samples, answer structure, cost constraints, and acceptance rules. The catalog names the deliverable as a math-verified policy layer validated against the buyer's task mix. It cannot establish whether “task classes and answer spaces are explicit” holds in the buyer's environment.

Connect those narrow roles to a local fixture for “samples treated as independent without evidence” rather than treating citation status as a pass.

For task-specific policies for repeated LLM sampling and aggregation, limit the conclusion to the documented workflow and let the evaluation owner retain the current source-to-claim map. New authority or data requires the task owner to review the evidence boundary again.

Product-specific acceptance criteria review drills

These drills connect task-specific policies for repeated LLM sampling and aggregation to concrete inputs, failures, acceptance statements, and owners. For task-specific policies for repeated LLM sampling and aggregation, the drills map each criterion to a reviewable verdict.

Acceptance for task-specific policies for repeated LLM sampling and aggregation is judged against a boundary record covering the LLM pipeline, representative task samples, answer structure, cost constraints, and acceptance rules, never live protected material. The task owner requires synthetic, non-secret cases; messages, writes, state changes, and all other external effects stay inside the fixture throughout and after each case.

Requirement trace

Let the task owner open the requirement trace review with this case: “voting over answers that cannot be normalized”. They isolate the affected decision from the rest of task classification, answer normalization, dependence analysis, sample budgeting, aggregation, consequence-aware evaluation, stop rules, and recorded decisions.

The evaluation owner checks a versioned boundary record covering the LLM pipeline, representative task samples, answer structure, cost constraints, and acceptance rules for “cost and latency are part of the decision”. A result from different conditions cannot close this drill.

The statistical reviewer resolves the requirement trace review by comparing the observed result with “cost and latency are part of the decision”. Missing proof makes the statistical reviewer block acceptance of a math-verified policy layer validated against the buyer's task mix. In the requirement trace review, evidence for “cost and latency are part of the decision” maps support to pass, contradiction to fail, and unresolved to hold.

Create a fresh record when the failure case “voting over answers that cannot be normalized” appears beyond the tested boundary or when the prior evidence becomes stale.

Normal-flow result

Start the normal-flow result review from a fixture showing “samples treated as independent without evidence”. The evaluation owner identifies which part of task classification, answer normalization, dependence analysis, sample budgeting, aggregation, consequence-aware evaluation, stop rules, and recorded decisions needs judgment.

Attach a frozen scope record covering the LLM pipeline, representative task samples, answer structure, cost constraints, and acceptance rules to the normal-flow result review, then let the platform owner review evidence that “task classes and answer spaces are explicit” holds.

The statistical reviewer records a decision for the normal-flow result review that cites the evidence for “task classes and answer spaces are explicit”. Unsupported parts of a math-verified policy layer validated against the buyer's task mix remain open. In the normal-flow result review, evidence for “task classes and answer spaces are explicit” maps support to pass, contradiction to fail, and unresolved to hold.

The statistical reviewer reopens the drill if the criterion “task classes and answer spaces are explicit” is judged with a different fixture, policy, or operating state.

Alternate-flow result

Build the alternate-flow result review around a case involving “accuracy averaged across incompatible task types”. The platform owner checks which observed state in task classification, answer normalization, dependence analysis, sample budgeting, aggregation, consequence-aware evaluation, stop rules, and recorded decisions can support the next step.

Source the test from a documented scope covering the LLM pipeline, representative task samples, answer structure, cost constraints, and acceptance rules and state the criterion “correlated errors are measured” before execution. The platform owner retains the resulting observation.

The statistical reviewer resolves the drill with one finding about “correlated errors are measured”. For task-specific policies for repeated LLM sampling and aggregation, the deliverable decision in the alternate-flow result review advances only when that finding is supported. In the alternate-flow result review, evidence for “correlated errors are measured” maps support to pass, contradiction to fail, and unresolved to hold.

Changes to data, permission, or the handling of “accuracy averaged across incompatible task types” trigger a new review owned by the platform owner.

Failure-flow result

Describe the failure-flow result review through a case involving “cost counted without latency”. The platform owner captures the known state and the first unanswered workflow question.

The release owner receives a boundary record covering the LLM pipeline, representative task samples, answer structure, cost constraints, and acceptance rules with an explicit request to verify whether “the policy has a no-vote and escalation path” holds. Input identity and judgment stay in the same receipt.

The statistical reviewer accepts, rejects, or returns the evidence for “the policy has a no-vote and escalation path”. Completion of another condition cannot substitute for it. In the failure-flow result review, evidence for “the policy has a no-vote and escalation path” maps support to pass, contradiction to fail, and unresolved to hold.

The platform owner repeats the drill after a material change to the fixture, workflow, or evidence used to judge whether “the policy has a no-vote and escalation path” holds.

Independent verdict

Frame the independent verdict review around “a policy tuned on the same fixtures used for release approval”. Before testing a response, the release owner captures the input, decision boundary, and residual state.

Document which element of the boundary covering the LLM pipeline, representative task samples, answer structure, cost constraints, and acceptance rules is relevant to “single-sample and multi-sample baselines are compared”, then ask the task owner to label the observation as supporting, contradictory, or incomplete without recording the acceptance verdict.

If current evidence supports the finding “single-sample and multi-sample baselines are compared”, the statistical reviewer may advance only this slice; otherwise a math-verified policy layer validated against the buyer's task mix remains unaccepted. In the independent verdict review, evidence for “single-sample and multi-sample baselines are compared” maps support to pass, contradiction to fail, and unresolved to hold.

Expire the result if “a policy tuned on the same fixtures used for release approval” crosses a different authority boundary or if the statistical reviewer receives a materially different input.

Evidence expiry

During the evidence expiry review, reproduce a safe case involving “voting over answers that cannot be normalized”. The task owner records what remains observable before the next role acts.

Use a scope record covering the LLM pipeline, representative task samples, answer structure, cost constraints, and acceptance rules to reproduce the case and inspect whether “cost and latency are part of the decision” holds. Store the comparison under the evidence expiry review, not in operator memory.

The statistical reviewer compares the result with “cost and latency are part of the decision” and records one bounded outcome. Unresolved scope cannot be converted into a pass. In the evidence expiry review, evidence for “cost and latency are part of the decision” maps support to pass, contradiction to fail, and unresolved to hold.

Expire the disposition if the task owner cannot reproduce the case for “voting over answers that cannot be normalized” under the recorded authority.

Frequently asked question

What acceptance criteria should I use for Multi-Shot Reliability Layer?

Require observable evidence that task classes and answer spaces are explicit and include “voting over answers that cannot be normalized” as a negative case. The statistical reviewer should record pass, hold, or fail before expansion.

A product bridge, with a boundary

The Multi-Shot Reliability Layer is the relevant sincLLM offer for this narrow problem. The frozen live catalog describes its required boundary as the LLM pipeline, representative task samples, answer structure, cost constraints, and acceptance rules and its deliverable as a math-verified policy layer validated against the buyer's task mix. That catalog statement defines the offer and does not establish buyer-specific fit, technical sufficiency, legal compliance, safety, or business results.