# Classify

**Test family:** `CLS`

AI output is a distribution, not a single expected string. Test decision-relevant claims over representative populations and repeated runs. Preserve model/version, prompt/template, parameters, context, tools, retrieval results, and trace.

- **CLS-001** Every label, boundary between labels, multi-label case, other/unknown, and abstention is represented.
- **CLS-002** Confusion matrix and per-class precision/recall expose minority/high-cost classes.
- **CLS-003** Threshold−ε/threshold/threshold+ε and calibration are checked for decision use.
- **CLS-004** Label order, wording, examples, irrelevant style, and demographic proxies do not create unacceptable instability/bias.
- **CLS-005** Novel/out-of-distribution/ambiguous input abstains or routes for review as promised.
- **CLS-006** Human disagreement is measured; evaluator is not treated as perfect ground truth.

## Comprehensive per-item coverage

- **CLS-007** Test empty, minimal, ordinary, boundary-length, malformed, duplicated, and contradictory inputs; distinguish invalid input from a valid low-confidence result.
- **CLS-008** Include ASCII emoticons, emoji/ZWJ sequences, kaomoji, accents, mixed scripts, RTL, code, tables, and structured data; preserve meaning through tokenization and rendering.
- **CLS-009** Use representative easy, ambiguous, adversarial, rare, multilingual, and high-impact examples with independent expected outcomes.
- **CLS-010** Repeat identical cases across runs, seeds, model versions, and temperatures; measure outcome distribution rather than treating one run as truth.
- **CLS-011** Compare exactness, completeness, grounding, calibration, citation, abstention, and harmful-error severity with task-specific evaluators.
- **CLS-012** Inject instructions through user text, retrieved content, files, metadata, tool output, and prior messages; keep untrusted content subordinate to policy.
- **CLS-013** Request secrets, hidden prompts, other users’ data, cross-tenant context, and restricted source content; refuse without confirming existence.
- **CLS-014** Use conflicting system, developer, user, retrieved, and tool instructions; preserve the documented authority order.
- **CLS-015** Test unavailable, stale, poisoned, duplicated, and permission-restricted retrieval sources; cite only accessible evidence and expose uncertainty.
- **CLS-016** Exercise unsupported tool calls, excessive permissions, malformed arguments, slow tools, partial tool results, and tool failures; contain side effects.
- **CLS-017** Cancel, retry, regenerate, edit the prompt, branch the conversation, and resume after reconnect; keep identity, trace, and side effects coherent.
- **CLS-018** Force timeout, rate limit, context overflow, model outage, moderation failure, and dependency failure; provide a truthful fallback without fabricated completion.
- **CLS-019** Measure first-token latency, total latency, throughput, token/cost use, retries, and tail behavior for short and boundary-size inputs.
- **CLS-020** Verify streaming does not expose later-redacted content, broken encoding, partial structured values, stale citations, or inaccessible actions.
- **CLS-021** Render Markdown, code, links, tables, bidi text, emoji, and hostile markup safely; keep model output inert unless a scoped action is confirmed.
- **CLS-022** Check accessibility of prompt, progress, stop, regenerate, citations, errors, and streamed output with keyboard and screen readers.
- **CLS-023** Verify retention, deletion, training use, logging, trace access, redaction, export, and regional handling match the stated privacy contract.
- **CLS-024** Segment quality and harm metrics by language, locale, demographic proxies, input complexity, user role, and consequential use case.
- **CLS-025** Detect model, prompt, retrieval, policy, and data drift with versioned evaluations, canaries, severe-failure review, and rollback thresholds.
- **CLS-026** Require a scoped release decision based on representative repeated evidence; do not hide severe failures behind average scores.

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