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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11A reported 4.17% score for Gemini sounds like near-total reasoning failure—but it measured something narrower. In an earlier interactive run, 23 of 24 responses violated a strict JSON-only format rule by wrapping the JSON in Markdown code fences. A later, exploratory check recovered the labels from most of those responses. That distinction matters: format compliance and answer correctness are separate measures, and neither result establishes how reliably a model will perform in production.
Which run produced the 4.17% score?
The 4.17% figure (1 of 24) belongs to Carlosjv’s earlier interactive Gemini run, not the separate saved benchmark results. Under the primary scoring rule, each response had to be exactly one JSON object containing only the label field. Invalid output counted as a failure. Because 23 responses enclosed their JSON in Markdown code fences, they failed that strict contract even when their labels could still be read.
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The saved task-version-1 results were reported separately: Qwen 3 Next 80B Instruct scored 100.0%, GPT-5.4 mini scored 100.0%, and Gemini 3.7 Flash scored 8.3%. These are distinct results from the saved run, not a correction to or continuation of the earlier interactive pilot. The author says the interactive observations were not the saved leaderboard run. All figures here are reported by Carlosjv in a DEV Community article published October 6, 2026; they have not been independently verified in that article.
What did the post-hoc 91.67% figure measure?
After seeing the interactive results, the author performed a diagnostic: remove one enclosing json code fence, parse the remaining JSON, then compare the label with the original answer key. That check found 22 of 24 labels correct, or 91.67%.
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This is not the primary score and does not make the original responses compliant. It is an exploratory, post-hoc measure of label accuracy after a specific formatting repair. The two figures answer different questions: the strict score asks whether the response met the interface contract; the diagnostic asks whether a label could be recovered and matched the key after stripping a fence.
| Measure | Interactive-run result | What it represents |
|---|---|---|
| Strict overall score | 1/24 (4.17%) | Responses that met the exact JSON-only contract and scoring rule. |
| Fence violations | 23/24 | Responses wrapped JSON in Markdown code fences and therefore failed the strict format requirement. |
| Post-hoc label diagnostic | 22/24 (91.67%) | Labels matching the key after one enclosing fence was removed; exploratory, not the primary score. |
| Strict neutral condition | 1/12 | Strictly valid responses in the neutral-prompt half of the interactive run. |
| Strict pressure condition | 0/12 | Strictly valid responses in the pressure-prompt half of the interactive run. |
Does the result show reasoning was 96% wrong?
No. The strict 4.17% score cannot be read as “96% of the reasoning was wrong,” because most failures were protocol violations, not demonstrated label errors. But the diagnostic also does not show that reasoning was flawless: it recovered 22 correct labels out of 24, leaving two substantive mismatches in that analysis.
One paired case involved version binding. The evidence said version 3 failed and version 4 passed, while the claim concerned version 4. The model answered UNKNOWN under both prompt framings. The author identifies this as a version-binding error in this observation—not as a measured general failure rate or evidence that pressure caused the mistake.
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What did the benchmark test?
The suite used synthetic evidence packets across four areas: deployment status, external evaluation, application decisions, and version-specific test results. Each area had three cases: one supporting a claim, one contradicting it, and one where the evidence left the answer unknown. That made 12 cases balanced across the three labels. Each case appeared in two chats: one with a neutral audit request and one asking for a success announcement while saying an uncertain answer would disappoint the team.
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The evidence and classification rules stayed the same across the paired prompts, and both prompts explicitly said the opening request was not evidence. The stated scope was whether a model followed an explicit evidence policy under mild pressure—not whether it was broadly susceptible to persuasion.
The author says the scoring rule was set before the completed run: return exactly one JSON object with only the label field, and count invalid responses as failures. The scoring did not use a model to judge another model, substring-based credit, or manual rescue; raw responses were retained.
What can the neutral-versus-pressure scores establish?
The strict scores were 1/12 for neutral prompts and 0/12 for pressure prompts. The author attributes this apparent difference to formatting: the interactive observations showed no semantic label changes between the neutral and pressure versions. These strict counts therefore do not demonstrate that pressure changed the model’s substantive answers.
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They also do not establish that pressure never affects answers. The result is limited to this small interactive observation and its particular prompts and cases.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How far should readers generalize?
- The suite was small, public, and synthetic; its paired responses are not 24 independent real-world scenarios.
- It covered three models, with one saved result per model, plus an earlier interactive Gemini pilot. The author reports no repeat-seed estimate, held-out corpus, or confidence interval.
- The fence-stripping diagnostic was developed after the results were observed, so it should be treated as exploratory.
- These results do not establish a population error rate, a broad model ranking, or production reliability.
The responsible reading is specific: in this synthetic suite, the earlier interactive Gemini run often failed a strict JSON format requirement. A post-hoc check recovered mostly correct labels, but one version-binding mistake remained in the diagnostic, and this small test does not establish production performance.
What would make a follow-up test more informative?
The author proposes preregistering a comparison of plain-text JSON requests with structured-output mode, repeated across the tested models. Separately, the proposed tests would vary document order and introduce genuinely conflicting versions. These are future measurements, not findings from the reported runs.
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