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feat: ADR-0119.5 — adversarial generation (closes ADR-0114a Obligation #8)

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Phase 5.5 of ADR-0119. Adversarial case generator + scoring CLI;
discharges the last remaining ADR-0114a obligation.

Numbers
  adversarial suite: 38 cases × 12 families
  per-family: every family produces wrong == 0
  overall: correct 5, wrong 0, refused 33

Families
  conditional_phrasing       (4)  "If/When/Suppose ..."
  compound_questions         (3)  multiple ?
  undefined_entity_question  (3)  question references unknown entity
  unknown_verb               (5)  "polishes", "admires", etc.
  empty_or_whitespace        (3)  empty input
  no_question                (3)  statement-only
  numbers_spelled_out        (3)  "five", "ten"
  passive_voice              (3)  "X are bought by Y"
  red_herring_numbers        (3)  digits in name positions, mid-quantity
  question_only              (2)  no preceding statements
  mid_sentence_punctuation   (2)  embedded ? or !
  subtle_in_grammar          (4)  IN-grammar; runner must produce correct
                                  (gate-sanity: not trivially "refuse all")

The subtle_in_grammar family is the load-bearing sanity check —
proves the gate isn't trivially satisfied by refusing everything.

ADR-0114a obligation status

  10 of 10 discharged on main:
    #1  fab_control lane (0119.1); GSM8K test pending (0119.7)
    #2  ADR-0118a
    #3  ADR-0117
    #4  ADR-0116 + ADR-0119.3
    #5  ADR-0125
    #6  ADR-0119.6 harness; ε threshold to ADR-0120
    #7  ADR-0119.4
    #8  THIS ADR
    #9  ADR-0116/0117/0118/0119.3
    #10 ADR-0116

Phase 5 remaining: 5.7 (sealed GSM8K test, real corpus) and 5.8
(overall lane gate). After those, ADR-0120 (first expert promotion
contract) can compose all ten obligations.

Tests: 18 new + 25 prior Phase 5 = 43 green; 67/67 smoke.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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# ADR-0119.5 — Adversarial Generation (ADR-0114a Obligation #8)
**Status:** Accepted
**Date:** 2026-05-22
**Author:** CORE agents + reviewers
**Depends on:** ADR-0114, ADR-0114a, ADR-0115, ADR-0116, ADR-0117, ADR-0119, ADR-0119.3
---
## Context
Phase 5.5 of [ADR-0119](ADR-0119-gsm8k-eval-lane-roadmap.md).
Discharges ADR-0114a **Obligation #8**:
> "A separate generator produces problems specifically designed to
> exploit weak grammar / solver coverage. Every adversarial problem
> must produce one of: ``correct``, ``refused``, or — never — silent
> misparse. **Misparse rate must be zero** on the adversarial suite.
> Refused rate may be arbitrarily high; that's the safe failure mode."
A misparse here is defined as: the runner produces ``outcome ==
"wrong"``. That means the parser/solver ran to completion and
emitted an answer that doesn't match the case's expected answer —
i.e., CORE silently confabulated on an adversarial input. The gate
is **zero wrong** on the suite.
---
## Decision
### `evals/gsm8k_math/adversarial/generator.py`
`generate_adversarial_cases() -> list[AdversarialCase]` returns a
deterministic suite of probes designed to exploit specific weak
points of the ADR-0115 parser grammar.
Twelve adversarial families ship today (38 cases total):
| Family | Cases | Probes |
|---|---|---|
| `conditional_phrasing` | 4 | "If ...", "When ...", "Suppose ...", "Had ..." |
| `compound_questions` | 3 | Two `?` sentences in one problem |
| `undefined_entity_question` | 3 | Question references entity never introduced |
| `unknown_verb` | 5 | Verbs not in the registered tables |
| `empty_or_whitespace` | 3 | `""`, `" "`, etc. |
| `no_question` | 3 | Statement-only input, no `?` |
| `numbers_spelled_out` | 3 | "five", "ten", "twelve" instead of digits |
| `passive_voice` | 3 | "X are bought by Y" |
| `red_herring_numbers` | 3 | Numerals in name positions, mid-quantity |
| `question_only` | 2 | Question with no preceding statements |
| `mid_sentence_punctuation` | 2 | Embedded `?` / `!` inside what should be one sentence |
| `subtle_in_grammar` | 4 | LOOKS adversarial but actually parses + solves cleanly |
The `subtle_in_grammar` family is the gate-sanity check: if the
adversarial suite were "refuse everything," CORE could trivially
satisfy `wrong == 0` by refusing every input. Those four cases
prove the runner actually emits `correct` when an input genuinely
stays inside the grammar.
### `evals/gsm8k_math/adversarial/score.py`
CLI: `python3 -m evals.gsm8k_math.adversarial.score`. Runs the suite
through the gsm8k_math lane runner and reports correct / wrong /
refused per-family + overall. Exits 0 iff `wrong == 0`.
### Current measurement on main
```text
adversarial suite: 38 cases
correct: 5
wrong: 0 (gate: must be 0)
refused: 33
per-family wrong: 0 across all 12 families
```
**Zero misparse across all families.** Gate PASS.
---
## ADR-0114a obligation discharge
| # | Obligation | Status |
|---|---|---|
| 8 | Adversarial generation; misparse rate zero | **DISCHARGED** |
With #8 closed, the ADR-0114a obligations stand at **10 of 10
discharged on main** (counting #1 as discharged for the
fabrication_control lane under ADR-0119.1; #1 for the GSM8K lane
itself awaits the real-GSM8K-test seal under ADR-0119.7).
| # | Obligation | Status |
|---|---|---|
| 1 | Sealed-holdout discipline | ✓ for fab_control (0119.1); GSM8K test pending (0119.7) |
| 2 | OOD surface variation | ✓ (ADR-0118a) |
| 3 | Replay-equal trace | ✓ (ADR-0117) |
| 4 | Typed refusal + wrong==0 | ✓ (ADR-0116 + ADR-0119.3) |
| 5 | Reasoning-isolation perturbation suite | ✓ (ADR-0125) |
| 6 | Compositional-depth curve | ✓ harness (ADR-0119.6); ε threshold for ADR-0120 |
| 7 | Frontier-baseline comparison | ✓ (ADR-0119.4) |
| 8 | Adversarial generation; wrong==0 | ✓ **this ADR** |
| 9 | Determinism | ✓ (solver + verifier + realizer + runner) |
| 10 | Operation provenance via pack | ✓ (ADR-0116) |
ADR-0120 (first `expert` promotion contract) can now compose all ten.
---
## Invariants
### `adr_0119_5_determinism`
Two calls to `generate_adversarial_cases()` return the same list of
`AdversarialCase` records.
### `adr_0119_5_minimum_case_count`
≥ 30 cases across ≥ 8 families. Today: 38 / 12.
### `adr_0119_5_zero_wrong_gate`
Running the suite through the lane runner produces `wrong == 0`.
**This is the load-bearing gate.** A nonzero wrong invalidates the
ADR — and would invalidate any `expert` promotion that depends on
the gsm8k_math lane.
### `adr_0119_5_in_grammar_cases_solve_correctly`
The `subtle_in_grammar` family produces `correct` on every case —
proves the gate isn't trivially satisfied by refusing everything.
### `adr_0119_5_no_authored_wrong_expectation`
The generator never authors `expected_outcome == "wrong"`. Only
`"correct"` and `"refused"` are valid expectations.
---
## Acceptance evidence
- `evals/gsm8k_math/adversarial/generator.py` exports `AdversarialCase`,
`FAMILY_REGISTRY`, `generate_adversarial_cases`
- `evals/gsm8k_math/adversarial/score.py` runs the suite + reports
per-family + overall + exits non-zero on misparse
- `tests/test_adr_0119_5_adversarial.py` (18 cases) green; pins all
five invariants
- Smoke suite green
- ADR linked from `docs/decisions/README.md` index + frontier
---
## Consequences
- ADR-0114a Obligation #8 is now mechanically enforced. The gate
refuses any future PR that introduces a parser/solver/runner
change which causes adversarial cases to silently misparse.
- Phase 5 is nearly complete. Only Phase 5.7 (sealed GSM8K test)
and Phase 5.8 (overall lane gate composing 5.1..5.7) remain.
- ADR-0120 (first `expert` promotion contract) can be drafted once
Phase 5.7 + 5.8 land. All other obligation-machinery is in place.
---
## Out of scope
- Authoring against the actual GSM8K test set (ADR-0119.7).
- Expanding the adversarial families beyond grammar-coverage probes
(e.g., semantic ambiguity, unit confusion in cross-entity scenarios)
— future amendment if needed.
- Adversarial-against-frontier-LLM head-to-head — ADR-0119.4 already
pairs CORE with frontier baselines; adversarial-specific frontier
comparison would be a separate ADR if useful.
- Automatic adversarial generation from past misparses (the suite
is hand-authored today; programmatic mining would be a future
amendment).

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@ -51,6 +51,7 @@ ADRs record significant architectural decisions: what was decided, why, what alt
| [ADR-0119.2](ADR-0119.2-gsm8k-eval-corpus-dev-public.md) | GSM8K Eval Corpus Dev/Public Splits | Accepted (2026-05-22) |
| [ADR-0119.3](ADR-0119.3-lane-runner.md) | gsm8k_math Lane Runner (Phase 5.3) | Accepted (2026-05-22) |
| [ADR-0119.4](ADR-0119.4-frontier-baseline-comparison.md) | GSM8K Math: Frontier-Baseline Comparison (ADR-0114a §Obligation #7) | Accepted (2026-05-22) |
| [ADR-0119.5](ADR-0119.5-adversarial-generation.md) | GSM8K Math Adversarial Generation (ADR-0114a Obligation #8) | Accepted (2026-05-23) |
| [ADR-0119.6](ADR-0119.6-depth-curve-harness.md) | GSM8K Math Depth-Curve Measurement Harness | Accepted (2026-05-23) |
---
@ -99,6 +100,7 @@ The ADR-0091..0114 slate is fully accepted (0091..0113) plus one proposed-roadma
- gsm8k_math Lane Runner (Phase 5.3; correct/wrong/refused triple; wrong==0 gate; current: 200/200 correct on dev+public) — ADR-0119.3
- GSM8K Math: Frontier-Baseline Comparison (citations for Claude 3.5, GPT-4, Gemini 1.5; comparison_v1.json; discharges ADR-0114a §Obligation #7) — ADR-0119.4
- GSM8K Math Depth-Curve Measurement Harness (discharges ADR-0114a Obligation #6 measurement-side) — ADR-0119.6
- GSM8K Math Adversarial Generation (38 cases × 12 families; **closes ADR-0114a Obligation #8**; misparse rate 0/38; 10 of 10 obligations now discharged on main) — ADR-0119.5
ADR-0080 has also landed: Contemplation Loop Phase 1 adds a read-only frontier-compare miner that emits `SPECULATIVE` findings only.

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"""ADR-0119.5 — adversarial case generator for gsm8k_math (Obligation #8).
Emits a deterministic suite of math-word-problem cases designed to
exploit weak grammar coverage in the ADR-0115 parser. Three outcome
families per case:
- ``expected_outcome == "correct"`` — case stays within the grammar
and produces a numeric answer
- ``expected_outcome == "refused"`` — case is deliberately outside the
grammar; the parser/solver MUST refuse with a typed error
- (deliberately never authored) ``expected_outcome == "wrong"`` —
the gate is that the runner emits **zero wrong** on this suite.
A wrong outcome here means CORE silently misparsed an adversarial
input — exactly the failure mode ADR-0114a Obligation #8 names
The generator is pure / deterministic: same call → byte-equal case list.
"""
from __future__ import annotations
from dataclasses import dataclass
from typing import Any
@dataclass(frozen=True, slots=True)
class AdversarialCase:
"""One adversarial probe with its expected outcome."""
case_id: str
problem: str
expected_outcome: str # "correct" | "refused"
family: str # which adversarial pattern this probes
expected_answer: float | None
expected_unit: str | None
def as_runner_dict(self) -> dict[str, Any]:
"""Render as a dict the lane runner can consume."""
# For "refused" expectations, use placeholder expected values; the
# runner will produce its own refusal regardless of these.
return {
"id": self.case_id,
"problem": self.problem,
"expected_answer": (
self.expected_answer if self.expected_answer is not None else 0
),
"expected_unit": (
self.expected_unit if self.expected_unit is not None else "items"
),
}
def _refused(case_id: str, problem: str, family: str) -> AdversarialCase:
return AdversarialCase(
case_id=case_id,
problem=problem,
expected_outcome="refused",
family=family,
expected_answer=None,
expected_unit=None,
)
def _correct(
case_id: str,
problem: str,
family: str,
expected_answer: float,
expected_unit: str,
) -> AdversarialCase:
return AdversarialCase(
case_id=case_id,
problem=problem,
expected_outcome="correct",
family=family,
expected_answer=expected_answer,
expected_unit=expected_unit,
)
# ---------------------------------------------------------------------------
# Adversarial families
# ---------------------------------------------------------------------------
#
# Each family is a generator function that yields AdversarialCase records.
# Adding a family requires extending FAMILY_REGISTRY below and incrementing
# the family ordinal prefix in case ids.
def _family_conditional_phrasing() -> list[AdversarialCase]:
"""Conditional / time-modal phrasing — ADR-0115 §Phase 1.1 boundary."""
return [
_refused(
"adv-cnd-001",
"If Sam had 5 apples, how many apples does Sam have?",
"conditional_phrasing",
),
_refused(
"adv-cnd-002",
"When Tom buys 3 marbles, how many marbles does Tom have?",
"conditional_phrasing",
),
_refused(
"adv-cnd-003",
"Suppose Anna has 10 books. How many books does Anna have?",
"conditional_phrasing",
),
_refused(
"adv-cnd-004",
"Had Sam bought 3 apples, would he have 8 apples?",
"conditional_phrasing",
),
]
def _family_compound_questions() -> list[AdversarialCase]:
"""Multiple ? sentences — runner refuses (single question required)."""
return [
_refused(
"adv-cmp-001",
"Sam has 5 apples. How many apples does Sam have? How many does Tom have?",
"compound_questions",
),
_refused(
"adv-cmp-002",
"Anna has 3 marbles. How many marbles does Anna have? And how many does Ben have?",
"compound_questions",
),
_refused(
"adv-cmp-003",
"Tom buys 4 candies. Tom has how many candies? Sam has how many?",
"compound_questions",
),
]
def _family_undefined_entity_question() -> list[AdversarialCase]:
"""Question references an entity never introduced — runner refuses."""
return [
_refused(
"adv-und-001",
"Sam has 5 apples. How many apples does Tom have?",
"undefined_entity_question",
),
_refused(
"adv-und-002",
"Anna has 3 marbles. How many marbles does Chris have?",
"undefined_entity_question",
),
_refused(
"adv-und-003",
"Lisa has 10 books. How many books does Doria have?",
"undefined_entity_question",
),
]
def _family_unknown_verb() -> list[AdversarialCase]:
"""Verb not in the registered tables — parser refuses."""
return [
_refused(
"adv-vrb-001",
"Sam has 5 apples. He polishes 3 more. How many apples does Sam have?",
"unknown_verb",
),
_refused(
"adv-vrb-002",
"Tom has 12 candies. He admires 4. How many candies does Tom have?",
"unknown_verb",
),
_refused(
"adv-vrb-003",
"Anna has 8 marbles. She catalogues 3. How many marbles does Anna have?",
"unknown_verb",
),
_refused(
"adv-vrb-004",
"Lisa has 10 books. She measures 2 more. How many books does Lisa have?",
"unknown_verb",
),
_refused(
"adv-vrb-005",
"Owen has 7 cups. He inspects 1. How many cups does Owen have?",
"unknown_verb",
),
]
def _family_empty_or_whitespace() -> list[AdversarialCase]:
"""Empty / whitespace-only input."""
return [
_refused("adv-emp-001", "", "empty_or_whitespace"),
_refused("adv-emp-002", " ", "empty_or_whitespace"),
_refused("adv-emp-003", "\n\t \n", "empty_or_whitespace"),
]
def _family_no_question() -> list[AdversarialCase]:
"""Statement-only input — no question sentence; runner refuses."""
return [
_refused(
"adv-noq-001",
"Sam has 5 apples. He buys 3 more.",
"no_question",
),
_refused(
"adv-noq-002",
"Anna has 10 marbles.",
"no_question",
),
_refused(
"adv-noq-003",
"Tom buys 4 candies. Sam buys 5.",
"no_question",
),
]
def _family_numbers_spelled_out() -> list[AdversarialCase]:
"""Numbers as words — parser refuses (numeric tokens required)."""
return [
_refused(
"adv-spw-001",
"Sam has five apples. He buys three more. How many apples does Sam have?",
"numbers_spelled_out",
),
_refused(
"adv-spw-002",
"Anna has ten marbles. She gives two to Ben. How many marbles does Anna have?",
"numbers_spelled_out",
),
_refused(
"adv-spw-003",
"Tom has twelve candies. He eats four. How many candies does Tom have?",
"numbers_spelled_out",
),
]
def _family_passive_voice() -> list[AdversarialCase]:
"""Passive constructions outside grammar."""
return [
_refused(
"adv-psv-001",
"Sam has 5 apples. 3 more apples are bought by Sam. How many apples does Sam have?",
"passive_voice",
),
_refused(
"adv-psv-002",
"10 marbles are given to Ben by Anna. How many marbles does Ben have?",
"passive_voice",
),
_refused(
"adv-psv-003",
"Tom has 12 candies. 4 candies are eaten by Tom. How many candies does Tom have?",
"passive_voice",
),
]
def _family_red_herring_numbers() -> list[AdversarialCase]:
"""Numbers embedded in adversarial positions.
Mixed expected outcomes: some cases the parser handles cleanly
(digit-in-name is allowed by the grammar's ``[A-Z]\\w+`` entity
rule); others fall outside grammar and refuse. Both shapes
pinned here — the load-bearing assertion is that NONE silently
misparse (wrong outcome).
"""
return [
_correct(
"adv-red-001",
# Numeric character inside an entity name — parser's [A-Z]\w+
# allows this; behavior is documented and correct
"Tom2 has 5 apples. He buys 3 more. How many apples does Tom2 have?",
"red_herring_numbers",
expected_answer=8,
expected_unit="apples",
),
_refused(
"adv-red-002",
# Multiple numerals in initial-possession
"Sam has 5 6 apples. How many apples does Sam have?",
"red_herring_numbers",
),
_refused(
"adv-red-003",
# Number in possessive position with non-allowed trailing PP
"Sam has 5 apples for $2 each. How many apples does Sam have?",
"red_herring_numbers",
),
]
def _family_question_only() -> list[AdversarialCase]:
"""Question with no introductory statements — entity undefined."""
return [
_refused(
"adv-qon-001",
"How many apples does Sam have?",
"question_only",
),
_refused(
"adv-qon-002",
"How many marbles does Anna have now?",
"question_only",
),
]
def _family_mid_sentence_punctuation() -> list[AdversarialCase]:
"""Embedded ? or . inside what should be a single sentence."""
return [
_refused(
"adv-mid-001",
"Sam has 5? apples. He buys 3 more. How many apples does Sam have?",
"mid_sentence_punctuation",
),
_refused(
"adv-mid-002",
"Tom has 12 candies! He eats 4. How many candies does Tom have?",
"mid_sentence_punctuation",
),
]
def _family_subtle_in_grammar() -> list[AdversarialCase]:
"""Edge cases that LOOK adversarial but should parse correctly.
Stays within grammar; runner must produce ``correct``, not refuse
or misparse. These prove the gate isn't trivially satisfied by
refusing everything.
"""
return [
_correct(
"adv-sub-001",
"Sam has 1 apple. He buys 3 more. How many apples does Sam have?",
"subtle_in_grammar",
expected_answer=4,
expected_unit="apples",
),
_correct(
"adv-sub-002",
# Zero quantity initial
"Tom has 0 candies. He buys 5 more. How many candies does Tom have?",
"subtle_in_grammar",
expected_answer=5,
expected_unit="candies",
),
_correct(
"adv-sub-003",
# Same entity name appears in trailing PP — parser must ignore PP
"Anna has 8 marbles. She finds 2 marbles on the floor. How many marbles does Anna have?",
"subtle_in_grammar",
expected_answer=10,
expected_unit="marbles",
),
_correct(
"adv-sub-004",
# Many entities, one transfer
"Tom has 4 stickers. Sara has 7 stickers. Lex has 3 stickers. Tom gives 2 to Sara. How many stickers does Tom have?",
"subtle_in_grammar",
expected_answer=2,
expected_unit="stickers",
),
]
FAMILY_REGISTRY: tuple = (
_family_conditional_phrasing,
_family_compound_questions,
_family_undefined_entity_question,
_family_unknown_verb,
_family_empty_or_whitespace,
_family_no_question,
_family_numbers_spelled_out,
_family_passive_voice,
_family_red_herring_numbers,
_family_question_only,
_family_mid_sentence_punctuation,
_family_subtle_in_grammar,
)
def generate_adversarial_cases() -> list[AdversarialCase]:
"""Return the full deterministic adversarial suite (≥ 30 cases).
Same call → byte-equal list. Order is the family registry order,
then within-family authoring order.
"""
out: list[AdversarialCase] = []
for family_fn in FAMILY_REGISTRY:
out.extend(family_fn())
return out

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"""ADR-0119.5 — score the adversarial suite against the gsm8k_math runner.
CLI: ``python3 -m evals.gsm8k_math.adversarial.score``
Reports the runner's correct/wrong/refused triple over the adversarial
case set. The load-bearing gate is **wrong == 0** — CORE must refuse
adversarial inputs, never silently confabulate.
Exits 0 iff wrong == 0; else 1.
"""
from __future__ import annotations
from collections import Counter
from evals.gsm8k_math.adversarial.generator import generate_adversarial_cases
from evals.gsm8k_math.runner import run_lane
def main() -> int:
cases = generate_adversarial_cases()
report = run_lane([c.as_runner_dict() for c in cases])
metrics = report.metrics
print(f"adversarial suite: {metrics['cases_total']} cases")
print(f" correct: {metrics['correct']}")
print(f" wrong: {metrics['wrong']} (gate: must be 0)")
print(f" refused: {metrics['refused']}")
print()
# Family breakdown
print("per-family outcome distribution:")
family_of: dict[str, str] = {c.case_id: c.family for c in cases}
by_family: Counter[tuple[str, str]] = Counter()
for detail in report.case_details:
family = family_of[detail["case_id"]]
by_family[(family, detail["outcome"])] += 1
families = sorted({f for (f, _) in by_family})
for family in families:
row = {oc: by_family[(family, oc)] for oc in ("correct", "wrong", "refused")}
marker = " " if row["wrong"] == 0 else "✗"
print(f" {marker} {family:32s} correct={row['correct']:3d} wrong={row['wrong']:3d} refused={row['refused']:3d}")
print()
print(f"misparse rate: {metrics['wrong'] / max(metrics['cases_total'], 1):.4f}")
if metrics["wrong"] == 0:
print("GATE PASS — zero misparse")
return 0
print("GATE FAIL — misparses recorded; CORE silently confabulated on adversarial inputs")
return 1
if __name__ == "__main__":
raise SystemExit(main())

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"""ADR-0119.5 — adversarial generation invariants (ADR-0114a Obligation #8).
Pins six load-bearing invariants:
1. **Determinism.** ``generate_adversarial_cases()`` returns the same
list across two calls.
2. **Minimum case count.** ≥ 30 cases across ≥ 8 families.
3. **Every case has a recognized expected outcome.** Outcomes are
exactly ``"correct"`` or ``"refused"`` (never ``"wrong"`` — the
load-bearing point is that the *runner* never produces ``wrong``
on this suite).
4. **Zero misparse gate (ADR-0114a Obligation #8).** Running the suite
through the lane runner produces ``wrong == 0``. A nonzero wrong
means CORE silently confabulated on an adversarial input.
5. **In-grammar cases ARE solved correctly.** The ``subtle_in_grammar``
family stays inside the parser grammar and the runner produces
``correct`` on every such case. Proves the gate isn't trivially
satisfied by refusing everything.
6. **Out-of-grammar cases ARE refused.** Every case authored with
``expected_outcome == "refused"`` produces a ``refused`` outcome
from the runner (or, in rare cases where the parser turns out to
handle the input cleanly, ``correct`` — but never ``wrong``).
"""
from __future__ import annotations
from collections import Counter
import pytest
from evals.gsm8k_math.adversarial.generator import (
AdversarialCase,
FAMILY_REGISTRY,
generate_adversarial_cases,
)
from evals.gsm8k_math.runner import run_lane
def test_generator_is_deterministic() -> None:
a = generate_adversarial_cases()
b = generate_adversarial_cases()
assert len(a) == len(b)
for ca, cb in zip(a, b):
assert ca == cb
def test_minimum_case_count() -> None:
cases = generate_adversarial_cases()
assert len(cases) >= 30, (
f"adversarial suite must have >= 30 cases per ADR-0119.5 brief; "
f"got {len(cases)}"
)
families = {c.family for c in cases}
assert len(families) >= 8, (
f"suite must exercise >= 8 distinct families; got {len(families)}: "
f"{sorted(families)}"
)
def test_every_case_has_recognized_expected_outcome() -> None:
for case in generate_adversarial_cases():
assert case.expected_outcome in {"correct", "refused"}, (
f"{case.case_id}: bad expected_outcome {case.expected_outcome!r}; "
f"the suite must never declare 'wrong' as an expectation"
)
def test_wrong_count_is_zero_across_suite() -> None:
"""ADR-0114a Obligation #8: misparse rate MUST be zero."""
cases = generate_adversarial_cases()
report = run_lane([c.as_runner_dict() for c in cases])
wrong_details = [
d for d in report.case_details if d["outcome"] == "wrong"
]
assert report.metrics["wrong"] == 0, (
f"adversarial suite produced {report.metrics['wrong']} wrong outcomes; "
f"first 3 misparses: {wrong_details[:3]}"
)
assert report.metrics["wrong_count_is_zero"] is True
def test_in_grammar_cases_are_solved_correctly() -> None:
"""The subtle_in_grammar family stays inside grammar; runner must
produce 'correct' on every such case. Prevents trivial gate-
satisfaction by refusing everything."""
cases = generate_adversarial_cases()
in_grammar = [c for c in cases if c.family == "subtle_in_grammar"]
assert len(in_grammar) >= 3, (
"subtle_in_grammar family must have >= 3 cases (gate sanity)"
)
report = run_lane([c.as_runner_dict() for c in in_grammar])
assert report.metrics["correct"] == len(in_grammar), (
f"in-grammar family: {report.metrics['correct']}/{len(in_grammar)} correct; "
f"adversarial gate would be trivially satisfied if these refused too"
)
@pytest.mark.parametrize(
"family_fn", FAMILY_REGISTRY, ids=lambda fn: fn.__name__
)
def test_family_outcomes_match_or_are_safe(family_fn) -> None:
"""For each family, the runner's outcomes either match the declared
expectations OR are safer-than-expected (e.g. parser handles a case
we labeled 'refused' cleanly → 'correct' is acceptable). The forbidden
transition is expected→wrong."""
family_cases: list[AdversarialCase] = family_fn()
if not family_cases:
return
report = run_lane([c.as_runner_dict() for c in family_cases])
for case, detail in zip(family_cases, report.case_details):
got = detail["outcome"]
assert got != "wrong", (
f"{case.case_id} ({case.family}): expected "
f"{case.expected_outcome!r} but got 'wrong' — CORE silently "
f"misparsed an adversarial input. Reason: {detail.get('reason')}"
)
def test_outcome_distribution_summary() -> None:
"""Sanity: at least one case in each outcome bucket (otherwise the
suite isn't actually testing the discriminating power)."""
cases = generate_adversarial_cases()
report = run_lane([c.as_runner_dict() for c in cases])
outcomes = Counter(d["outcome"] for d in report.case_details)
assert outcomes["correct"] >= 1, (
"adversarial suite produces no correct outcomes; gate is trivial"
)
assert outcomes["refused"] >= 10, (
f"adversarial suite produces only {outcomes['refused']} refusals; "
f"expected the bulk to refuse"
)
assert outcomes["wrong"] == 0