FA-44101 / Borrow checking / Open access
Struct spread overwrites an explicit borrowed field origin · case 01
Struct spread overwrites an explicit borrowed field origin.
ROOT CAUSE
The static analyzer mishandles explicit field precedence: struct spread overwrites an explicit borrowed field origin.
VERIFIED REPAIR
Apply the specified transfer or inference rule at this site: if bool(set(d['explicit'])&set(d['update_written'])): errors.append('explicit-field-precedence').
Unsuccessful approach: The partial repair uses if set(d['explicit'])==set(d['update_written']) and bool(d['explicit']): errors.append('explicit-field-precedence'), which still violates the stipulated analysis contract.
Case contract
Validate lowering of aggregate expressions containing owned and borrowed fields. Evaluation order follows source field order; struct update moves only omitted fields; explicit fields override update sources; failed construction drops initialized fields in reverse initialization order; borrowed fields preserve their source origin; spread cannot duplicate exclusive fields; tuple projection indices remain positional; enum payload loans require matching discriminant; moving an aggregate remaps every contained reference owner slot; zero-field aggregates create no synthetic borrowed field. Input is a fully explicit descriptor of the stated toy IR. Return rule identifiers in declaration order; absent optional obligations use the provided neutral defaults. No rule is an assertion about a production language.
Why this case matters
A finite offline static-analysis model of ownership and borrowing; it does not execute the analyzed program.
1 / The failure
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
N = 1
observations = []
def solve(d):
errors=[]
if d['source_order']!=d['eval_order']: errors.append('field-evaluation-order')
if set(d['omitted'])!=set(d['update_moved']): errors.append('update-omitted-only')
if False: errors.append('explicit-field-precedence')
if d['cleanup_order']!=list(reversed(d['initialized_order'])): errors.append('construction-cleanup-order')
if d['source_origins']!=d['lowered_origins']: errors.append('field-origin-preservation')
if bool(set(d['exclusive_fields'])&set(d['spread_copies'])): errors.append('spread-exclusive-duplication')
if d['tuple_source']!=d['tuple_lowered']: errors.append('tuple-slot-identity')
if d['payload_loan'] and d['actual_variant']!=d['expected_variant']: errors.append('enum-loan-discriminant')
if not set(d['reference_slots'])<=set(d['remapped_slots']): errors.append('contained-reference-remap')
if d['zero_field'] and bool(d['synthetic_loans']): errors.append('empty-aggregate-origin')
return errors
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
base={'source_order': [], 'eval_order': [], 'omitted': [], 'update_moved': [], 'explicit': [], 'update_written': [], 'initialized_order': [], 'cleanup_order': [], 'source_origins': {}, 'lowered_origins': {}, 'exclusive_fields': [], 'spread_copies': [], 'tuple_source': [], 'tuple_lowered': [], 'expected_variant': None, 'actual_variant': None, 'payload_loan': False, 'reference_slots': [], 'remapped_slots': [], 'zero_field': False, 'synthetic_loans': []}
check('well formed empty obligations',solve(base),[])
check('field-evaluation-order regression 0', solve(dict(base, **({'source_order':['a','b'],'eval_order':['b','a']}))), ['field-evaluation-order'])
check('field-evaluation-order regression 1', solve(dict(base, **({'source_order':[N,N+1],'eval_order':[N+1,N]}))), ['field-evaluation-order'])
check('update-omitted-only regression 0', solve(dict(base, **({'omitted':['a'],'update_moved':['a','b']}))), ['update-omitted-only'])
check('update-omitted-only regression 1', solve(dict(base, **({'omitted':[N],'update_moved':[N,N+1]}))), ['update-omitted-only'])
check('explicit-field-precedence regression 0', solve(dict(base, **({'explicit':['a'],'update_written':['a','b']}))), ['explicit-field-precedence'])
check('explicit-field-precedence regression 1', solve(dict(base, **({'explicit':[N],'update_written':[N,N+1]}))), ['explicit-field-precedence'])
check('construction-cleanup-order regression 0', solve(dict(base, **({'initialized_order':['a','b'],'cleanup_order':['a','b']}))), ['construction-cleanup-order'])
check('construction-cleanup-order regression 1', solve(dict(base, **({'initialized_order':[N,N+1],'cleanup_order':[N,N+1]}))), ['construction-cleanup-order'])
check('field-origin-preservation regression 0', solve(dict(base, **({'source_origins':{'a':'r'},'lowered_origins':{'a':'s'}}))), ['field-origin-preservation'])
check('field-origin-preservation regression 1', solve(dict(base, **({'source_origins':{'a':N},'lowered_origins':{'a':N+1}}))), ['field-origin-preservation'])
check('spread-exclusive-duplication regression 0', solve(dict(base, **({'exclusive_fields':['a'],'spread_copies':['a']}))), ['spread-exclusive-duplication'])
check('spread-exclusive-duplication regression 1', solve(dict(base, **({'exclusive_fields':[N],'spread_copies':[N]}))), ['spread-exclusive-duplication'])
check('tuple-slot-identity regression 0', solve(dict(base, **({'tuple_source':['a','b'],'tuple_lowered':['b','a']}))), ['tuple-slot-identity'])
check('tuple-slot-identity regression 1', solve(dict(base, **({'tuple_source':[N,N+1],'tuple_lowered':[N+1,N]}))), ['tuple-slot-identity'])
check('enum-loan-discriminant regression 0', solve(dict(base, **({'payload_loan':True,'actual_variant':'b','expected_variant':'a'}))), ['enum-loan-discriminant'])
check('enum-loan-discriminant regression 1', solve(dict(base, **({'payload_loan':True,'actual_variant':N+1,'expected_variant':N}))), ['enum-loan-discriminant'])
check('contained-reference-remap regression 0', solve(dict(base, **({'reference_slots':['a','b'],'remapped_slots':['a']}))), ['contained-reference-remap'])
check('contained-reference-remap regression 1', solve(dict(base, **({'reference_slots':[N,N+1],'remapped_slots':[N]}))), ['contained-reference-remap'])
check('empty-aggregate-origin regression 0', solve(dict(base, **({'zero_field':True,'synthetic_loans':['r']}))), ['empty-aggregate-origin'])
check('empty-aggregate-origin regression 1', solve(dict(base, **({'zero_field':True,'synthetic_loans':[N]}))), ['empty-aggregate-origin'])
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
| Boundary fixture | Actual | Expected | Outcome |
|---|---|---|---|
| well formed empty obligations | [] | [] | Passed |
| field-evaluation-order regression 0 | ['field-evaluation-order'] | ['field-evaluation-order'] | Passed |
| field-evaluation-order regression 1 | ['field-evaluation-order'] | ['field-evaluation-order'] | Passed |
| update-omitted-only regression 0 | ['update-omitted-only'] | ['update-omitted-only'] | Passed |
| update-omitted-only regression 1 | ['update-omitted-only'] | ['update-omitted-only'] | Passed |
| explicit-field-precedence regression 0 | [] | ['explicit-field-precedence'] | Failed |
| explicit-field-precedence regression 1 | [] | ['explicit-field-precedence'] | Failed |
| construction-cleanup-order regression 0 | ['construction-cleanup-order'] | ['construction-cleanup-order'] | Passed |
| construction-cleanup-order regression 1 | ['construction-cleanup-order'] | ['construction-cleanup-order'] | Passed |
| field-origin-preservation regression 0 | ['field-origin-preservation'] | ['field-origin-preservation'] | Passed |
| field-origin-preservation regression 1 | ['field-origin-preservation'] | ['field-origin-preservation'] | Passed |
| spread-exclusive-duplication regression 0 | ['spread-exclusive-duplication'] | ['spread-exclusive-duplication'] | Passed |
| spread-exclusive-duplication regression 1 | ['spread-exclusive-duplication'] | ['spread-exclusive-duplication'] | Passed |
| tuple-slot-identity regression 0 | ['tuple-slot-identity'] | ['tuple-slot-identity'] | Passed |
| tuple-slot-identity regression 1 | ['tuple-slot-identity'] | ['tuple-slot-identity'] | Passed |
| enum-loan-discriminant regression 0 | ['enum-loan-discriminant'] | ['enum-loan-discriminant'] | Passed |
| enum-loan-discriminant regression 1 | ['enum-loan-discriminant'] | ['enum-loan-discriminant'] | Passed |
| contained-reference-remap regression 0 | ['contained-reference-remap'] | ['contained-reference-remap'] | Passed |
| contained-reference-remap regression 1 | ['contained-reference-remap'] | ['contained-reference-remap'] | Passed |
| empty-aggregate-origin regression 0 | ['empty-aggregate-origin'] | ['empty-aggregate-origin'] | Passed |
| empty-aggregate-origin regression 1 | ['empty-aggregate-origin'] | ['empty-aggregate-origin'] | Passed |
SHA-256 / 6bc9e75332957c2ea5aa39541c3a23496b11948917d8d1af3019579ae3a1fd4d
2 / The unsuccessful fix
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
N = 1
observations = []
def solve(d):
errors=[]
if d['source_order']!=d['eval_order']: errors.append('field-evaluation-order')
if set(d['omitted'])!=set(d['update_moved']): errors.append('update-omitted-only')
if set(d['explicit'])==set(d['update_written']) and bool(d['explicit']): errors.append('explicit-field-precedence')
if d['cleanup_order']!=list(reversed(d['initialized_order'])): errors.append('construction-cleanup-order')
if d['source_origins']!=d['lowered_origins']: errors.append('field-origin-preservation')
if bool(set(d['exclusive_fields'])&set(d['spread_copies'])): errors.append('spread-exclusive-duplication')
if d['tuple_source']!=d['tuple_lowered']: errors.append('tuple-slot-identity')
if d['payload_loan'] and d['actual_variant']!=d['expected_variant']: errors.append('enum-loan-discriminant')
if not set(d['reference_slots'])<=set(d['remapped_slots']): errors.append('contained-reference-remap')
if d['zero_field'] and bool(d['synthetic_loans']): errors.append('empty-aggregate-origin')
return errors
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
base={'source_order': [], 'eval_order': [], 'omitted': [], 'update_moved': [], 'explicit': [], 'update_written': [], 'initialized_order': [], 'cleanup_order': [], 'source_origins': {}, 'lowered_origins': {}, 'exclusive_fields': [], 'spread_copies': [], 'tuple_source': [], 'tuple_lowered': [], 'expected_variant': None, 'actual_variant': None, 'payload_loan': False, 'reference_slots': [], 'remapped_slots': [], 'zero_field': False, 'synthetic_loans': []}
check('well formed empty obligations',solve(base),[])
check('field-evaluation-order regression 0', solve(dict(base, **({'source_order':['a','b'],'eval_order':['b','a']}))), ['field-evaluation-order'])
check('field-evaluation-order regression 1', solve(dict(base, **({'source_order':[N,N+1],'eval_order':[N+1,N]}))), ['field-evaluation-order'])
check('update-omitted-only regression 0', solve(dict(base, **({'omitted':['a'],'update_moved':['a','b']}))), ['update-omitted-only'])
check('update-omitted-only regression 1', solve(dict(base, **({'omitted':[N],'update_moved':[N,N+1]}))), ['update-omitted-only'])
check('explicit-field-precedence regression 0', solve(dict(base, **({'explicit':['a'],'update_written':['a','b']}))), ['explicit-field-precedence'])
check('explicit-field-precedence regression 1', solve(dict(base, **({'explicit':[N],'update_written':[N,N+1]}))), ['explicit-field-precedence'])
check('construction-cleanup-order regression 0', solve(dict(base, **({'initialized_order':['a','b'],'cleanup_order':['a','b']}))), ['construction-cleanup-order'])
check('construction-cleanup-order regression 1', solve(dict(base, **({'initialized_order':[N,N+1],'cleanup_order':[N,N+1]}))), ['construction-cleanup-order'])
check('field-origin-preservation regression 0', solve(dict(base, **({'source_origins':{'a':'r'},'lowered_origins':{'a':'s'}}))), ['field-origin-preservation'])
check('field-origin-preservation regression 1', solve(dict(base, **({'source_origins':{'a':N},'lowered_origins':{'a':N+1}}))), ['field-origin-preservation'])
check('spread-exclusive-duplication regression 0', solve(dict(base, **({'exclusive_fields':['a'],'spread_copies':['a']}))), ['spread-exclusive-duplication'])
check('spread-exclusive-duplication regression 1', solve(dict(base, **({'exclusive_fields':[N],'spread_copies':[N]}))), ['spread-exclusive-duplication'])
check('tuple-slot-identity regression 0', solve(dict(base, **({'tuple_source':['a','b'],'tuple_lowered':['b','a']}))), ['tuple-slot-identity'])
check('tuple-slot-identity regression 1', solve(dict(base, **({'tuple_source':[N,N+1],'tuple_lowered':[N+1,N]}))), ['tuple-slot-identity'])
check('enum-loan-discriminant regression 0', solve(dict(base, **({'payload_loan':True,'actual_variant':'b','expected_variant':'a'}))), ['enum-loan-discriminant'])
check('enum-loan-discriminant regression 1', solve(dict(base, **({'payload_loan':True,'actual_variant':N+1,'expected_variant':N}))), ['enum-loan-discriminant'])
check('contained-reference-remap regression 0', solve(dict(base, **({'reference_slots':['a','b'],'remapped_slots':['a']}))), ['contained-reference-remap'])
check('contained-reference-remap regression 1', solve(dict(base, **({'reference_slots':[N,N+1],'remapped_slots':[N]}))), ['contained-reference-remap'])
check('empty-aggregate-origin regression 0', solve(dict(base, **({'zero_field':True,'synthetic_loans':['r']}))), ['empty-aggregate-origin'])
check('empty-aggregate-origin regression 1', solve(dict(base, **({'zero_field':True,'synthetic_loans':[N]}))), ['empty-aggregate-origin'])
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
| Boundary fixture | Actual | Expected | Outcome |
|---|---|---|---|
| well formed empty obligations | [] | [] | Passed |
| field-evaluation-order regression 0 | ['field-evaluation-order'] | ['field-evaluation-order'] | Passed |
| field-evaluation-order regression 1 | ['field-evaluation-order'] | ['field-evaluation-order'] | Passed |
| update-omitted-only regression 0 | ['update-omitted-only'] | ['update-omitted-only'] | Passed |
| update-omitted-only regression 1 | ['update-omitted-only'] | ['update-omitted-only'] | Passed |
| explicit-field-precedence regression 0 | [] | ['explicit-field-precedence'] | Failed |
| explicit-field-precedence regression 1 | [] | ['explicit-field-precedence'] | Failed |
| construction-cleanup-order regression 0 | ['construction-cleanup-order'] | ['construction-cleanup-order'] | Passed |
| construction-cleanup-order regression 1 | ['construction-cleanup-order'] | ['construction-cleanup-order'] | Passed |
| field-origin-preservation regression 0 | ['field-origin-preservation'] | ['field-origin-preservation'] | Passed |
| field-origin-preservation regression 1 | ['field-origin-preservation'] | ['field-origin-preservation'] | Passed |
| spread-exclusive-duplication regression 0 | ['spread-exclusive-duplication'] | ['spread-exclusive-duplication'] | Passed |
| spread-exclusive-duplication regression 1 | ['spread-exclusive-duplication'] | ['spread-exclusive-duplication'] | Passed |
| tuple-slot-identity regression 0 | ['tuple-slot-identity'] | ['tuple-slot-identity'] | Passed |
| tuple-slot-identity regression 1 | ['tuple-slot-identity'] | ['tuple-slot-identity'] | Passed |
| enum-loan-discriminant regression 0 | ['enum-loan-discriminant'] | ['enum-loan-discriminant'] | Passed |
| enum-loan-discriminant regression 1 | ['enum-loan-discriminant'] | ['enum-loan-discriminant'] | Passed |
| contained-reference-remap regression 0 | ['contained-reference-remap'] | ['contained-reference-remap'] | Passed |
| contained-reference-remap regression 1 | ['contained-reference-remap'] | ['contained-reference-remap'] | Passed |
| empty-aggregate-origin regression 0 | ['empty-aggregate-origin'] | ['empty-aggregate-origin'] | Passed |
| empty-aggregate-origin regression 1 | ['empty-aggregate-origin'] | ['empty-aggregate-origin'] | Passed |
SHA-256 / fcad564a231b476a4a82401c048ed71d155f77c547aa2a8fbf5fe871089fd068
3 / The verified repair
Exit 0"""Failure Map reference implementation. Python standard library only."""
import json
N = 1
observations = []
def solve(d):
errors=[]
if d['source_order']!=d['eval_order']: errors.append('field-evaluation-order')
if set(d['omitted'])!=set(d['update_moved']): errors.append('update-omitted-only')
if bool(set(d['explicit'])&set(d['update_written'])): errors.append('explicit-field-precedence')
if d['cleanup_order']!=list(reversed(d['initialized_order'])): errors.append('construction-cleanup-order')
if d['source_origins']!=d['lowered_origins']: errors.append('field-origin-preservation')
if bool(set(d['exclusive_fields'])&set(d['spread_copies'])): errors.append('spread-exclusive-duplication')
if d['tuple_source']!=d['tuple_lowered']: errors.append('tuple-slot-identity')
if d['payload_loan'] and d['actual_variant']!=d['expected_variant']: errors.append('enum-loan-discriminant')
if not set(d['reference_slots'])<=set(d['remapped_slots']): errors.append('contained-reference-remap')
if d['zero_field'] and bool(d['synthetic_loans']): errors.append('empty-aggregate-origin')
return errors
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
base={'source_order': [], 'eval_order': [], 'omitted': [], 'update_moved': [], 'explicit': [], 'update_written': [], 'initialized_order': [], 'cleanup_order': [], 'source_origins': {}, 'lowered_origins': {}, 'exclusive_fields': [], 'spread_copies': [], 'tuple_source': [], 'tuple_lowered': [], 'expected_variant': None, 'actual_variant': None, 'payload_loan': False, 'reference_slots': [], 'remapped_slots': [], 'zero_field': False, 'synthetic_loans': []}
check('well formed empty obligations',solve(base),[])
check('field-evaluation-order regression 0', solve(dict(base, **({'source_order':['a','b'],'eval_order':['b','a']}))), ['field-evaluation-order'])
check('field-evaluation-order regression 1', solve(dict(base, **({'source_order':[N,N+1],'eval_order':[N+1,N]}))), ['field-evaluation-order'])
check('update-omitted-only regression 0', solve(dict(base, **({'omitted':['a'],'update_moved':['a','b']}))), ['update-omitted-only'])
check('update-omitted-only regression 1', solve(dict(base, **({'omitted':[N],'update_moved':[N,N+1]}))), ['update-omitted-only'])
check('explicit-field-precedence regression 0', solve(dict(base, **({'explicit':['a'],'update_written':['a','b']}))), ['explicit-field-precedence'])
check('explicit-field-precedence regression 1', solve(dict(base, **({'explicit':[N],'update_written':[N,N+1]}))), ['explicit-field-precedence'])
check('construction-cleanup-order regression 0', solve(dict(base, **({'initialized_order':['a','b'],'cleanup_order':['a','b']}))), ['construction-cleanup-order'])
check('construction-cleanup-order regression 1', solve(dict(base, **({'initialized_order':[N,N+1],'cleanup_order':[N,N+1]}))), ['construction-cleanup-order'])
check('field-origin-preservation regression 0', solve(dict(base, **({'source_origins':{'a':'r'},'lowered_origins':{'a':'s'}}))), ['field-origin-preservation'])
check('field-origin-preservation regression 1', solve(dict(base, **({'source_origins':{'a':N},'lowered_origins':{'a':N+1}}))), ['field-origin-preservation'])
check('spread-exclusive-duplication regression 0', solve(dict(base, **({'exclusive_fields':['a'],'spread_copies':['a']}))), ['spread-exclusive-duplication'])
check('spread-exclusive-duplication regression 1', solve(dict(base, **({'exclusive_fields':[N],'spread_copies':[N]}))), ['spread-exclusive-duplication'])
check('tuple-slot-identity regression 0', solve(dict(base, **({'tuple_source':['a','b'],'tuple_lowered':['b','a']}))), ['tuple-slot-identity'])
check('tuple-slot-identity regression 1', solve(dict(base, **({'tuple_source':[N,N+1],'tuple_lowered':[N+1,N]}))), ['tuple-slot-identity'])
check('enum-loan-discriminant regression 0', solve(dict(base, **({'payload_loan':True,'actual_variant':'b','expected_variant':'a'}))), ['enum-loan-discriminant'])
check('enum-loan-discriminant regression 1', solve(dict(base, **({'payload_loan':True,'actual_variant':N+1,'expected_variant':N}))), ['enum-loan-discriminant'])
check('contained-reference-remap regression 0', solve(dict(base, **({'reference_slots':['a','b'],'remapped_slots':['a']}))), ['contained-reference-remap'])
check('contained-reference-remap regression 1', solve(dict(base, **({'reference_slots':[N,N+1],'remapped_slots':[N]}))), ['contained-reference-remap'])
check('empty-aggregate-origin regression 0', solve(dict(base, **({'zero_field':True,'synthetic_loans':['r']}))), ['empty-aggregate-origin'])
check('empty-aggregate-origin regression 1', solve(dict(base, **({'zero_field':True,'synthetic_loans':[N]}))), ['empty-aggregate-origin'])
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
| Boundary fixture | Actual | Expected | Outcome |
|---|---|---|---|
| well formed empty obligations | [] | [] | Passed |
| field-evaluation-order regression 0 | ['field-evaluation-order'] | ['field-evaluation-order'] | Passed |
| field-evaluation-order regression 1 | ['field-evaluation-order'] | ['field-evaluation-order'] | Passed |
| update-omitted-only regression 0 | ['update-omitted-only'] | ['update-omitted-only'] | Passed |
| update-omitted-only regression 1 | ['update-omitted-only'] | ['update-omitted-only'] | Passed |
| explicit-field-precedence regression 0 | ['explicit-field-precedence'] | ['explicit-field-precedence'] | Passed |
| explicit-field-precedence regression 1 | ['explicit-field-precedence'] | ['explicit-field-precedence'] | Passed |
| construction-cleanup-order regression 0 | ['construction-cleanup-order'] | ['construction-cleanup-order'] | Passed |
| construction-cleanup-order regression 1 | ['construction-cleanup-order'] | ['construction-cleanup-order'] | Passed |
| field-origin-preservation regression 0 | ['field-origin-preservation'] | ['field-origin-preservation'] | Passed |
| field-origin-preservation regression 1 | ['field-origin-preservation'] | ['field-origin-preservation'] | Passed |
| spread-exclusive-duplication regression 0 | ['spread-exclusive-duplication'] | ['spread-exclusive-duplication'] | Passed |
| spread-exclusive-duplication regression 1 | ['spread-exclusive-duplication'] | ['spread-exclusive-duplication'] | Passed |
| tuple-slot-identity regression 0 | ['tuple-slot-identity'] | ['tuple-slot-identity'] | Passed |
| tuple-slot-identity regression 1 | ['tuple-slot-identity'] | ['tuple-slot-identity'] | Passed |
| enum-loan-discriminant regression 0 | ['enum-loan-discriminant'] | ['enum-loan-discriminant'] | Passed |
| enum-loan-discriminant regression 1 | ['enum-loan-discriminant'] | ['enum-loan-discriminant'] | Passed |
| contained-reference-remap regression 0 | ['contained-reference-remap'] | ['contained-reference-remap'] | Passed |
| contained-reference-remap regression 1 | ['contained-reference-remap'] | ['contained-reference-remap'] | Passed |
| empty-aggregate-origin regression 0 | ['empty-aggregate-origin'] | ['empty-aggregate-origin'] | Passed |
| empty-aggregate-origin regression 1 | ['empty-aggregate-origin'] | ['empty-aggregate-origin'] | Passed |
SHA-256 / c099b0886398412751b6867d8a2b8d8b49996e6f47b89d03d02e8c3ba7b8af31
Verification & scope
The explicitly stated toy language is the complete scope; this is not a production compiler or a claim about Rust semantics. This reproducer isolates one failure mechanism. Results cover the supplied fixtures. Variants within a family share a test contract and should remain grouped when constructing evaluation splits. Related mechanisms with a shared evaluation_group must also remain together; these controlled models are not independent production incidents.
Observations recorded using Python 3.12.14 at 2026-09-29T14:44:08.812141+00:00.
Case digest / 434d5cf2676f7dfacd018400aeb5b1c8a669d0292f81da16ae380440ef592bfa