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FA-35866 / Tab interfaces / Open access

Linked pane scroll: proportional scroll · case 01

The tab workspace reports an incorrect proportional scroll.

Verified by executionVariant 1 · 6 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

The proportional scroll decision uses x['scroll'] instead of x['scroll']*x['peer_range']//x['source_range'].

THE FAILURE

The proportional scroll decision uses x['scroll'] instead of x['scroll']*x['peer_range']//x['source_range'].

Unsuccessful approach: The partial repair x['scroll']*x['source_range']//x['peer_range'] still violates a workspace boundary or normal case.

Case contract

Linked views of one document synchronize only scroll events from the elected source; map proportional content positions into the peer range and suppress echoed tokens.

Why this case matters

Offline tab/panel workspace behavior; no browser or desktop framework is emulated.

1 / The failure

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(x):
    r0 = x['event_view']==x['source']
    r1 = x['doc']==x['peer_doc']
    r2 = x['token']!=x['last']
    r3 = x['scroll']
    r4 = min(x['peer_range'],max(0,x['scroll']*x['peer_range']//x['source_range']))
    r5 = x['token']
    return [r0,r1,r2,r3,r4,r5]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = {1: [({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'd', 'token': 4, 'last': 3, 'scroll': 40, 'source_range': 100, 'peer_range': 250}, [True, True, True, 100, 100, 4]), ({'source': 'a', 'event_view': 'b', 'doc': 'd', 'peer_doc': 'd', 'token': 4, 'last': 3, 'scroll': 40, 'source_range': 100, 'peer_range': 250}, [False, True, True, 100, 100, 4]), ({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'e', 'token': 4, 'last': 3, 'scroll': 40, 'source_range': 100, 'peer_range': 250}, [True, False, True, 100, 100, 4]), ({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'd', 'token': 3, 'last': 3, 'scroll': 40, 'source_range': 100, 'peer_range': 250}, [True, True, False, 100, 100, 3]), ({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'd', 'token': 4, 'last': 3, 'scroll': 0, 'source_range': 100, 'peer_range': 250}, [True, True, True, 0, 0, 4]), ({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'd', 'token': 4, 'last': 3, 'scroll': 100, 'source_range': 100, 'peer_range': 250}, [True, True, True, 250, 250, 4])], 2: [({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'd', 'token': 8, 'last': 6, 'scroll': 80, 'source_range': 200, 'peer_range': 500}, [True, True, True, 200, 200, 8]), ({'source': 'a', 'event_view': 'b', 'doc': 'd', 'peer_doc': 'd', 'token': 8, 'last': 6, 'scroll': 80, 'source_range': 200, 'peer_range': 500}, [False, True, True, 200, 200, 8]), ({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'e', 'token': 8, 'last': 6, 'scroll': 80, 'source_range': 200, 'peer_range': 500}, [True, False, True, 200, 200, 8]), ({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'd', 'token': 6, 'last': 6, 'scroll': 80, 'source_range': 200, 'peer_range': 500}, [True, True, False, 200, 200, 6]), ({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'd', 'token': 8, 'last': 6, 'scroll': 0, 'source_range': 200, 'peer_range': 500}, [True, True, True, 0, 0, 8]), ({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'd', 'token': 8, 'last': 6, 'scroll': 200, 'source_range': 200, 'peer_range': 500}, [True, True, True, 500, 500, 8])], 3: [({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'd', 'token': 12, 'last': 9, 'scroll': 120, 'source_range': 300, 'peer_range': 750}, [True, True, True, 300, 300, 12]), ({'source': 'a', 'event_view': 'b', 'doc': 'd', 'peer_doc': 'd', 'token': 12, 'last': 9, 'scroll': 120, 'source_range': 300, 'peer_range': 750}, [False, True, True, 300, 300, 12]), ({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'e', 'token': 12, 'last': 9, 'scroll': 120, 'source_range': 300, 'peer_range': 750}, [True, False, True, 300, 300, 12]), ({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'd', 'token': 9, 'last': 9, 'scroll': 120, 'source_range': 300, 'peer_range': 750}, [True, True, False, 300, 300, 9]), ({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'd', 'token': 12, 'last': 9, 'scroll': 0, 'source_range': 300, 'peer_range': 750}, [True, True, True, 0, 0, 12]), ({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'd', 'token': 12, 'last': 9, 'scroll': 300, 'source_range': 300, 'peer_range': 750}, [True, True, True, 750, 750, 12])], 4: [({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'd', 'token': 16, 'last': 12, 'scroll': 160, 'source_range': 400, 'peer_range': 1000}, [True, True, True, 400, 400, 16]), ({'source': 'a', 'event_view': 'b', 'doc': 'd', 'peer_doc': 'd', 'token': 16, 'last': 12, 'scroll': 160, 'source_range': 400, 'peer_range': 1000}, [False, True, True, 400, 400, 16]), ({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'e', 'token': 16, 'last': 12, 'scroll': 160, 'source_range': 400, 'peer_range': 1000}, [True, False, True, 400, 400, 16]), ({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'd', 'token': 12, 'last': 12, 'scroll': 160, 'source_range': 400, 'peer_range': 1000}, [True, True, False, 400, 400, 12]), ({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'd', 'token': 16, 'last': 12, 'scroll': 0, 'source_range': 400, 'peer_range': 1000}, [True, True, True, 0, 0, 16]), ({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'd', 'token': 16, 'last': 12, 'scroll': 400, 'source_range': 400, 'peer_range': 1000}, [True, True, True, 1000, 1000, 16])], 5: [({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'd', 'token': 20, 'last': 15, 'scroll': 200, 'source_range': 500, 'peer_range': 1250}, [True, True, True, 500, 500, 20]), ({'source': 'a', 'event_view': 'b', 'doc': 'd', 'peer_doc': 'd', 'token': 20, 'last': 15, 'scroll': 200, 'source_range': 500, 'peer_range': 1250}, [False, True, True, 500, 500, 20]), ({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'e', 'token': 20, 'last': 15, 'scroll': 200, 'source_range': 500, 'peer_range': 1250}, [True, False, True, 500, 500, 20]), ({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'd', 'token': 15, 'last': 15, 'scroll': 200, 'source_range': 500, 'peer_range': 1250}, [True, True, False, 500, 500, 15]), ({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'd', 'token': 20, 'last': 15, 'scroll': 0, 'source_range': 500, 'peer_range': 1250}, [True, True, True, 0, 0, 20]), ({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'd', 'token': 20, 'last': 15, 'scroll': 500, 'source_range': 500, 'peer_range': 1250}, [True, True, True, 1250, 1250, 20])]}
for i, (inputs, expected) in enumerate(fixtures[N]):
    check("workspace regression "+str(i), solve(inputs), expected)
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 fixtureActualExpectedOutcome
workspace regression 0[True, True, True, 40, 100, 4][True, True, True, 100, 100, 4]Failed
workspace regression 1[False, True, True, 40, 100, 4][False, True, True, 100, 100, 4]Failed
workspace regression 2[True, False, True, 40, 100, 4][True, False, True, 100, 100, 4]Failed
workspace regression 3[True, True, False, 40, 100, 3][True, True, False, 100, 100, 3]Failed
workspace regression 4[True, True, True, 0, 0, 4][True, True, True, 0, 0, 4]Passed
workspace regression 5[True, True, True, 100, 250, 4][True, True, True, 250, 250, 4]Failed

SHA-256 / 5aa9ca39991e1ccab0094de859954b116d322168ed290dbcf852f7b502d2eaed

2 / The unsuccessful fix

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(x):
    r0 = x['event_view']==x['source']
    r1 = x['doc']==x['peer_doc']
    r2 = x['token']!=x['last']
    r3 = x['scroll']*x['source_range']//x['peer_range']
    r4 = min(x['peer_range'],max(0,x['scroll']*x['peer_range']//x['source_range']))
    r5 = x['token']
    return [r0,r1,r2,r3,r4,r5]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = {1: [({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'd', 'token': 4, 'last': 3, 'scroll': 40, 'source_range': 100, 'peer_range': 250}, [True, True, True, 100, 100, 4]), ({'source': 'a', 'event_view': 'b', 'doc': 'd', 'peer_doc': 'd', 'token': 4, 'last': 3, 'scroll': 40, 'source_range': 100, 'peer_range': 250}, [False, True, True, 100, 100, 4]), ({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'e', 'token': 4, 'last': 3, 'scroll': 40, 'source_range': 100, 'peer_range': 250}, [True, False, True, 100, 100, 4]), ({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'd', 'token': 3, 'last': 3, 'scroll': 40, 'source_range': 100, 'peer_range': 250}, [True, True, False, 100, 100, 3]), ({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'd', 'token': 4, 'last': 3, 'scroll': 0, 'source_range': 100, 'peer_range': 250}, [True, True, True, 0, 0, 4]), ({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'd', 'token': 4, 'last': 3, 'scroll': 100, 'source_range': 100, 'peer_range': 250}, [True, True, True, 250, 250, 4])], 2: [({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'd', 'token': 8, 'last': 6, 'scroll': 80, 'source_range': 200, 'peer_range': 500}, [True, True, True, 200, 200, 8]), ({'source': 'a', 'event_view': 'b', 'doc': 'd', 'peer_doc': 'd', 'token': 8, 'last': 6, 'scroll': 80, 'source_range': 200, 'peer_range': 500}, [False, True, True, 200, 200, 8]), ({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'e', 'token': 8, 'last': 6, 'scroll': 80, 'source_range': 200, 'peer_range': 500}, [True, False, True, 200, 200, 8]), ({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'd', 'token': 6, 'last': 6, 'scroll': 80, 'source_range': 200, 'peer_range': 500}, [True, True, False, 200, 200, 6]), ({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'd', 'token': 8, 'last': 6, 'scroll': 0, 'source_range': 200, 'peer_range': 500}, [True, True, True, 0, 0, 8]), ({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'd', 'token': 8, 'last': 6, 'scroll': 200, 'source_range': 200, 'peer_range': 500}, [True, True, True, 500, 500, 8])], 3: [({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'd', 'token': 12, 'last': 9, 'scroll': 120, 'source_range': 300, 'peer_range': 750}, [True, True, True, 300, 300, 12]), ({'source': 'a', 'event_view': 'b', 'doc': 'd', 'peer_doc': 'd', 'token': 12, 'last': 9, 'scroll': 120, 'source_range': 300, 'peer_range': 750}, [False, True, True, 300, 300, 12]), ({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'e', 'token': 12, 'last': 9, 'scroll': 120, 'source_range': 300, 'peer_range': 750}, [True, False, True, 300, 300, 12]), ({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'd', 'token': 9, 'last': 9, 'scroll': 120, 'source_range': 300, 'peer_range': 750}, [True, True, False, 300, 300, 9]), ({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'd', 'token': 12, 'last': 9, 'scroll': 0, 'source_range': 300, 'peer_range': 750}, [True, True, True, 0, 0, 12]), ({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'd', 'token': 12, 'last': 9, 'scroll': 300, 'source_range': 300, 'peer_range': 750}, [True, True, True, 750, 750, 12])], 4: [({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'd', 'token': 16, 'last': 12, 'scroll': 160, 'source_range': 400, 'peer_range': 1000}, [True, True, True, 400, 400, 16]), ({'source': 'a', 'event_view': 'b', 'doc': 'd', 'peer_doc': 'd', 'token': 16, 'last': 12, 'scroll': 160, 'source_range': 400, 'peer_range': 1000}, [False, True, True, 400, 400, 16]), ({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'e', 'token': 16, 'last': 12, 'scroll': 160, 'source_range': 400, 'peer_range': 1000}, [True, False, True, 400, 400, 16]), ({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'd', 'token': 12, 'last': 12, 'scroll': 160, 'source_range': 400, 'peer_range': 1000}, [True, True, False, 400, 400, 12]), ({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'd', 'token': 16, 'last': 12, 'scroll': 0, 'source_range': 400, 'peer_range': 1000}, [True, True, True, 0, 0, 16]), ({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'd', 'token': 16, 'last': 12, 'scroll': 400, 'source_range': 400, 'peer_range': 1000}, [True, True, True, 1000, 1000, 16])], 5: [({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'd', 'token': 20, 'last': 15, 'scroll': 200, 'source_range': 500, 'peer_range': 1250}, [True, True, True, 500, 500, 20]), ({'source': 'a', 'event_view': 'b', 'doc': 'd', 'peer_doc': 'd', 'token': 20, 'last': 15, 'scroll': 200, 'source_range': 500, 'peer_range': 1250}, [False, True, True, 500, 500, 20]), ({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'e', 'token': 20, 'last': 15, 'scroll': 200, 'source_range': 500, 'peer_range': 1250}, [True, False, True, 500, 500, 20]), ({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'd', 'token': 15, 'last': 15, 'scroll': 200, 'source_range': 500, 'peer_range': 1250}, [True, True, False, 500, 500, 15]), ({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'd', 'token': 20, 'last': 15, 'scroll': 0, 'source_range': 500, 'peer_range': 1250}, [True, True, True, 0, 0, 20]), ({'source': 'a', 'event_view': 'a', 'doc': 'd', 'peer_doc': 'd', 'token': 20, 'last': 15, 'scroll': 500, 'source_range': 500, 'peer_range': 1250}, [True, True, True, 1250, 1250, 20])]}
for i, (inputs, expected) in enumerate(fixtures[N]):
    check("workspace regression "+str(i), solve(inputs), expected)
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 fixtureActualExpectedOutcome
workspace regression 0[True, True, True, 16, 100, 4][True, True, True, 100, 100, 4]Failed
workspace regression 1[False, True, True, 16, 100, 4][False, True, True, 100, 100, 4]Failed
workspace regression 2[True, False, True, 16, 100, 4][True, False, True, 100, 100, 4]Failed
workspace regression 3[True, True, False, 16, 100, 3][True, True, False, 100, 100, 3]Failed
workspace regression 4[True, True, True, 0, 0, 4][True, True, True, 0, 0, 4]Passed
workspace regression 5[True, True, True, 40, 250, 4][True, True, True, 250, 250, 4]Failed

SHA-256 / 5241636c8bf4b55f01fe2e79401ac12ea84efd0b114e2349cd14557ea5b00f10

HELD IN THE MEMBER ARCHIVE

The verified repair and its recorded checks are member-only.

This mechanism has 6 recorded checks per implementation. The open-access tier publishes the failure and the unsuccessful fix; the repaired source that passes every check, and the observations that prove it, are available to members.

Every case sharing this mechanism uses the same contract and the same repair, so this one record is held back for all of them.

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Verification & scope

Finite stipulated workspace snapshots only. Independent result fields describe observable obligations, not a full UI runtime. 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 2026-09-29T14:42:45.767476+00:00.

Case digest / adc5f41cb953c43023b3e03d39cc87bf6f17e91d2520173b180e6a607f4244ea