| # What the first feedback snapshot tells us |
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| Evidence cutoff: **2026-09-26 18:45:09 UTC / 20:45:09 Warsaw**. Analysis uses the frozen |
| [snapshot 02 report](gemma4-h100-p4-v1-snapshot-02/report.md) and its hashed input manifest. |
| Grading continued during inspection; this report deliberately stays at 37/40 exams. |
| Model: Gemma 4 12B Q4, thinking off, temperature 0, seed 42. Judge: Astra, medium. |
| This is one solver configuration, not a comparison of models. |
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| ## Measured results |
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| Of 1,289 items, 1,268 have a score: 643 full-credit, 97 partial-credit and 528 zero-credit. |
| The assessed score is **1,011/1,910 (52.93%)**. There are **899 lost assessed points**. |
| Twenty-one items remain unassessed: 17 tagged rubric ambiguity and four infrastructure failures. |
| Their combined maximum is 40 points. Three exams are absent from this snapshot. Neither unknown |
| items nor missing exams have been treated as zero. |
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| | Existing judge tag | Scored failures | Share of the 625 scored failures | Associated lost points | |
| |---|---:|---:|---:| |
| | Knowledge | 530 | 84.8% | 778 | |
| | Source reading | 337 | 53.9% | 459 | |
| | Reasoning | 189 | 30.2% | 430 | |
| | Instruction following | 57 | 9.1% | 123 | |
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| Labels overlap. These are descriptions assigned by the same judge that produced the grades, not |
| independent causal findings. A knowledge tag does not establish that memorizing more facts will fix |
| the answer: source selection or a misleading interpretation may produce the same wrong identity. |
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| **Essays deserve a dedicated workstream:** all 36 assessed essays lost points, scoring 222/494 |
| (44.94%) and losing 272 points, **30.3% of all assessed loss**. The remaining essay is unassessed. |
| Across the inspected essay rationales, the recurring pattern is fluent but general prose with too few |
| specific events, weak chronological development, shallow coverage of requested dimensions, and |
| occasional factual errors. Length compliance also fails in some cases, but is not the general explanation. |
| For example, the 2018 May old-format essay scores 2/20 and omits the concrete events needed to develop |
| the Chrobry/Otto III relationship; the 2023 May essay exceeds 300 words but still scores only 4/15. |
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| ## Actionable mechanisms seen in examples |
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| These examples illustrate mechanisms; their prevalence has not yet been manually annotated. |
| Item IDs refer to full records in snapshot 02 `items.jsonl`. |
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| | Candidate mechanism | Evidence example | What to investigate | |
| |---|---|---| |
| | Wrong historical identity with confident elaboration | 2017 June, 6: Sobieski/1686 instead of Kazimierz Jagiellończyk and the Thirteen Years' War | Fact retrieval versus use of discriminating source clues | |
| | Evidence noticed, then explained away | 2017 May, 2: answer observes a ship's bow but chooses the land battle of Thermopylae; judge expects Salamis | Evidence-to-conclusion consistency | |
| | Required answer elements missing | 2017 June, 5.2: Kraków and Wrocław given, Kołobrzeg omitted; all three required | Completeness checking and exact rubric conditions | |
| | Repetition or paraphrase instead of reasoning | 2019 Nowa Era, 24.1: names Operation Tempest but repeats the order instead of explaining why not to fight the USSR | Causal explanation versus restating the source | |
| | Incorrect chart interpretation and non-distinct arguments | 2019 Nowa Era, 22: regional shares of industry employment treated as a region's industrial composition; two arguments repeat one claim | Denominator reading and independent evidence | |
| | Answer outside an explicit time boundary | 2019 May, 20.1: supplies 1806 for a question asking about the late eighteenth century | Constraint checking | |
| | Wrong source selection | 2017 May old-format, 25: compares E/F instead of H/I; 2019 May old-format, 23: compares A/B instead of F/G | Audit source-to-question packaging before attributing this to the model | |
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| Visual interpretations above are reported judge findings; this analysis has not independently regraded |
| the original images. Short-answer quotations and source/answer relationships were inspected directly. |
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| ## Input and evaluation quality need their own lane |
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| The wrong-source examples have a concrete confound confirmed in the **saved solver prompts**: |
| 2017 May old-format item 25 receives sources A–J together, and 2019 May old-format item 23 receives |
| A–I. The question text does not explicitly bind the required pair. Several source-specific instructions |
| appear inside the aggregated source text without their original question adjacency. Current |
| `harness/matura.py:128` serializes that entire `source_text` into the single-item prompt. The answer |
| chooses a wrong pair, but this evidence is insufficient to assign the whole error to instruction following. |
| The next step is an original-page mapping audit and a controlled rerun with explicit source binding; |
| do not silently alter existing grades or prompts. |
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| There are also **130 exact question/source-text duplicate groups** across this snapshot. This is only |
| a text match: images can differ, and paraphrases are missed. Exam counts therefore overstate the amount |
| of independent task evidence. Resolve exam families and near-duplicates before building train/holdout |
| splits or estimating uncertainty. Current percentages are descriptive item/point aggregates; no |
| independence-based confidence interval or model superiority claim has been made. |
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| ## Pipeline now available |
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| `saved attempts + validated grades → versioned joined item records → normalized tags and loss tables |
| → evidence-linked review queues → confirmed mechanisms → paired probes → regression and training splits` |
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| The implemented stage ends at the review queues: **609 model-diagnosis cases, 40 grade-adjudication |
| cases and four infrastructure cases**. All annotations are pending. Original tags, scores, complete |
| answers, rubric text, evidence paths and model identities remain available locally. Human-reviewed |
| mechanisms, cross-model comparisons and diagnostic model reruns are proposed next steps, not completed work. |
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| Start by auditing the source-binding examples and resolving the 21 unknown items. In parallel with |
| that work, review a small stratified set of failures and successful controls, including essays, before |
| expanding the taxonomy. Prioritize essay specificity, source grounding and answer completeness after |
| the review confirms each mechanism. Do not train on these evaluation answers and then report gains on |
| the same papers as held-out improvement. |
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| Five offline tests passed. The production run freshly revalidated every selected exam's input hashes, |
| quotation evidence, coverage and score arithmetic. Semantic grading correctness remains provisional. |
| Aggregate metrics and an aggregate-only report were published to |
| [ClearML](http://X.X.X.X:18080/projects/6e5b0adc9583493dac37858470667d42/tasks/2f63bcc0dd5a434a801b8b640280c114/output/log). |
| Detailed evidence and review records remain local. |
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