diff --git a/ARTICLE_IMAGE_SOURCES.md b/ARTICLE_IMAGE_SOURCES.md
index f4db17e4834f663392f4a15fea015615b70641ce..ec697e45145877014da93413f5a6a8cbdeb6c852 100644
--- a/ARTICLE_IMAGE_SOURCES.md
+++ b/ARTICLE_IMAGE_SOURCES.md
@@ -1,11 +1,11 @@
 # Источники статей для изображений
 
-Файл создан автоматически: `2026-05-28T19:45:17Z`.
+Файл создан автоматически: `2026-06-03T07:06:18Z`.
 
 Назначение файла — зафиксировать ссылки на статьи, из которых взяты изображения/отрендеренные PDF-страницы в `data/task3_vlm_generation.jsonl`. Это нужно для соблюдения правил цитирования источников.
 
-Всего image-записей: **1440**.
-Уникальных статей/идентификаторов: **192**.
+Всего image-записей: **1496**.
+Уникальных статей/идентификаторов: **197**.
 
 ## 978-1-939133-44-1
 
@@ -97,6 +97,28 @@
 | `task3:ca35bd9ad6:case-009` | `CASE-009` | `assets/images/ca35bd9ad6/case-009/explicit_03_page_010.png` | p.10, Fig. 11 | figure |
 | `task3:ca35bd9ad6:case-009` | `CASE-009` | `assets/images/ca35bd9ad6/case-009/explicit_04_page_010.png` | p.10, Fig. 11 | figure |
 
+## https://arxiv.org/abs/1605.01735
+
+- Ссылка на статью: https://arxiv.org/abs/1605.01735
+- Название: Machine learning phases of matter
+- Идентификаторы: arxiv:1605.01735
+- Использовано изображений/страниц: 12
+
+| sample_id | case_id | image | page_hint | evidence_kind |
+|---|---|---|---|---|
+| `task3:task3-ab-physics-ml-001:case_01` | `case_01` | `assets/images/task3-ab-physics-ml-001/case_01/explicit_01_page_002.png` | Fig. 2 | figure |
+| `task3:task3-ab-physics-ml-001:case_01` | `case_01` | `assets/images/task3-ab-physics-ml-001/case_01/explicit_02_page_002.png` | Fig. 2 | figure |
+| `task3:task3-ab-physics-ml-001:case_01` | `case_01` | `assets/images/task3-ab-physics-ml-001/case_01/explicit_03_page_002.png` | Fig. 2 | figure |
+| `task3:task3-ab-physics-ml-001:case_01` | `case_01` | `assets/images/task3-ab-physics-ml-001/case_01/explicit_04_page_002.png` | Fig. 2 | figure |
+| `task3:task3-ab-physics-ml-001:case_02` | `case_02` | `assets/images/task3-ab-physics-ml-001/case_02/explicit_01_page_004.png` | Fig. 4 | figure |
+| `task3:task3-ab-physics-ml-001:case_02` | `case_02` | `assets/images/task3-ab-physics-ml-001/case_02/explicit_02_page_004.png` | Fig. 4 | figure |
+| `task3:task3-ab-physics-ml-001:case_02` | `case_02` | `assets/images/task3-ab-physics-ml-001/case_02/explicit_03_page_004.png` | Fig. 4 | figure |
+| `task3:task3-ab-physics-ml-001:case_02` | `case_02` | `assets/images/task3-ab-physics-ml-001/case_02/explicit_04_page_004.png` | Fig. 4 | figure |
+| `task3:task3-ab-physics-ml-001:case_03` | `case_03` | `assets/images/task3-ab-physics-ml-001/case_03/explicit_01_page_003.png` | Fig. 3 | figure |
+| `task3:task3-ab-physics-ml-001:case_03` | `case_03` | `assets/images/task3-ab-physics-ml-001/case_03/explicit_02_page_003.png` | Fig. 3 | figure |
+| `task3:task3-ab-physics-ml-001:case_03` | `case_03` | `assets/images/task3-ab-physics-ml-001/case_03/explicit_03_page_003.png` | Fig. 3 | figure |
+| `task3:task3-ab-physics-ml-001:case_03` | `case_03` | `assets/images/task3-ab-physics-ml-001/case_03/explicit_04_page_003.png` | Fig. 3 | figure |
+
 ## https://arxiv.org/abs/1704.07994
 
 - Ссылка на статью: https://arxiv.org/abs/1704.07994
@@ -313,6 +335,28 @@
 | `task3:cherbaev__iris_retina_modern_ab:case-010` | `CASE-010` | `assets/images/cherbaev__iris_retina_modern_ab/case-010/explicit_03_page_000.png` | Таблица или график match scores по 6 сессиям за 6.5 лет, 209 субъектов (4–11 лет на enrollment) | figure_or_table |
 | `task3:cherbaev__iris_retina_modern_ab:case-010` | `CASE-010` | `assets/images/cherbaev__iris_retina_modern_ab/case-010/explicit_04_page_000.png` | Таблица или график match scores по 6 сессиям за 6.5 лет, 209 субъектов (4–11 лет на enrollment) | figure_or_table |
 
+## https://arxiv.org/abs/2305.02402
+
+- Ссылка на статью: https://arxiv.org/abs/2305.02402
+- Название: Normalizing flows for lattice gauge theory in arbitrary space-time dimension
+- Идентификаторы: arxiv:2305.02402
+- Использовано изображений/страниц: 12
+
+| sample_id | case_id | image | page_hint | evidence_kind |
+|---|---|---|---|---|
+| `task3:task3-ab-physics-ml-001:case_04` | `case_04` | `assets/images/task3-ab-physics-ml-001/case_04/explicit_01_page_001.png` | Fig. 1 | figure |
+| `task3:task3-ab-physics-ml-001:case_04` | `case_04` | `assets/images/task3-ab-physics-ml-001/case_04/explicit_02_page_001.png` | Fig. 1 | figure |
+| `task3:task3-ab-physics-ml-001:case_04` | `case_04` | `assets/images/task3-ab-physics-ml-001/case_04/explicit_03_page_001.png` | Fig. 1 | figure |
+| `task3:task3-ab-physics-ml-001:case_04` | `case_04` | `assets/images/task3-ab-physics-ml-001/case_04/explicit_04_page_001.png` | Fig. 1 | figure |
+| `task3:task3-ab-physics-ml-001:case_05` | `case_05` | `assets/images/task3-ab-physics-ml-001/case_05/explicit_01_page_002.png` | Fig. 2 | figure |
+| `task3:task3-ab-physics-ml-001:case_05` | `case_05` | `assets/images/task3-ab-physics-ml-001/case_05/explicit_02_page_002.png` | Fig. 2 | figure |
+| `task3:task3-ab-physics-ml-001:case_05` | `case_05` | `assets/images/task3-ab-physics-ml-001/case_05/explicit_03_page_002.png` | Fig. 2 | figure |
+| `task3:task3-ab-physics-ml-001:case_05` | `case_05` | `assets/images/task3-ab-physics-ml-001/case_05/explicit_04_page_002.png` | Fig. 2 | figure |
+| `task3:task3-ab-physics-ml-001:case_09` | `case_09` | `assets/images/task3-ab-physics-ml-001/case_09/explicit_01_page_006.png` | Fig. 6 | figure |
+| `task3:task3-ab-physics-ml-001:case_09` | `case_09` | `assets/images/task3-ab-physics-ml-001/case_09/explicit_02_page_006.png` | Fig. 6 | figure |
+| `task3:task3-ab-physics-ml-001:case_09` | `case_09` | `assets/images/task3-ab-physics-ml-001/case_09/explicit_03_page_016.png` | Fig. 6 | figure |
+| `task3:task3-ab-physics-ml-001:case_09` | `case_09` | `assets/images/task3-ab-physics-ml-001/case_09/explicit_04_page_016.png` | Fig. 6 | figure |
+
 ## https://arxiv.org/abs/2305.16432
 
 - Ссылка на статью: https://arxiv.org/abs/2305.16432
@@ -651,6 +695,28 @@
 | `task3:gorbunov-ivan-yurievich__0d769c50eb:case-012` | `CASE-012` | `assets/images/gorbunov-ivan-yurievich__0d769c50eb/case-012/explicit_03_page_004.png` | Fig.4 | figure |
 | `task3:gorbunov-ivan-yurievich__0d769c50eb:case-012` | `CASE-012` | `assets/images/gorbunov-ivan-yurievich__0d769c50eb/case-012/explicit_04_page_004.png` | Fig.4 | figure |
 
+## https://arxiv.org/abs/2405.16769
+
+- Ссылка на статью: https://arxiv.org/abs/2405.16769
+- Название: Learning phase transitions by siamese neural network
+- Идентификаторы: arxiv:2405.16769
+- Использовано изображений/страниц: 12
+
+| sample_id | case_id | image | page_hint | evidence_kind |
+|---|---|---|---|---|
+| `task3:task3-ab-physics-ml-001:case_07` | `case_07` | `assets/images/task3-ab-physics-ml-001/case_07/explicit_01_page_003.png` | Fig. 3 | figure |
+| `task3:task3-ab-physics-ml-001:case_07` | `case_07` | `assets/images/task3-ab-physics-ml-001/case_07/explicit_02_page_003.png` | Fig. 3 | figure |
+| `task3:task3-ab-physics-ml-001:case_07` | `case_07` | `assets/images/task3-ab-physics-ml-001/case_07/explicit_03_page_003.png` | Fig. 3 | figure |
+| `task3:task3-ab-physics-ml-001:case_07` | `case_07` | `assets/images/task3-ab-physics-ml-001/case_07/explicit_04_page_003.png` | Fig. 3 | figure |
+| `task3:task3-ab-physics-ml-001:case_08` | `case_08` | `assets/images/task3-ab-physics-ml-001/case_08/explicit_01_page_004.png` | Fig. 4 | figure |
+| `task3:task3-ab-physics-ml-001:case_08` | `case_08` | `assets/images/task3-ab-physics-ml-001/case_08/explicit_02_page_004.png` | Fig. 4 | figure |
+| `task3:task3-ab-physics-ml-001:case_08` | `case_08` | `assets/images/task3-ab-physics-ml-001/case_08/explicit_03_page_004.png` | Fig. 4 | figure |
+| `task3:task3-ab-physics-ml-001:case_08` | `case_08` | `assets/images/task3-ab-physics-ml-001/case_08/explicit_04_page_004.png` | Fig. 4 | figure |
+| `task3:task3-ab-physics-ml-001:case_12` | `case_12` | `assets/images/task3-ab-physics-ml-001/case_12/explicit_01_page_005.png` | Fig. 5 | figure |
+| `task3:task3-ab-physics-ml-001:case_12` | `case_12` | `assets/images/task3-ab-physics-ml-001/case_12/explicit_02_page_005.png` | Fig. 5 | figure |
+| `task3:task3-ab-physics-ml-001:case_12` | `case_12` | `assets/images/task3-ab-physics-ml-001/case_12/explicit_03_page_005.png` | Fig. 5 | figure |
+| `task3:task3-ab-physics-ml-001:case_12` | `case_12` | `assets/images/task3-ab-physics-ml-001/case_12/explicit_04_page_005.png` | Fig. 5 | figure |
+
 ## https://arxiv.org/abs/2407.03037
 
 - Ссылка на статью: https://arxiv.org/abs/2407.03037
@@ -711,6 +777,28 @@
 | `task3:vasin_artem_aleksandrovich__0cf7f9ceca:case-002` | `CASE-002` | `assets/images/vasin_artem_aleksandrovich__0cf7f9ceca/case-002/explicit_03_page_002.png` | fig. 2, 3, 6, Tab. 2 | figure_or_table |
 | `task3:vasin_artem_aleksandrovich__0cf7f9ceca:case-002` | `CASE-002` | `assets/images/vasin_artem_aleksandrovich__0cf7f9ceca/case-002/explicit_04_page_002.png` | fig. 2, 3, 6, Tab. 2 | figure_or_table |
 
+## https://arxiv.org/abs/2410.13161
+
+- Ссылка на статью: https://arxiv.org/abs/2410.13161
+- Название: Non-Perturbative Trivializing Flows for Lattice Gauge Theories
+- Идентификаторы: arxiv:2410.13161
+- Использовано изображений/страниц: 12
+
+| sample_id | case_id | image | page_hint | evidence_kind |
+|---|---|---|---|---|
+| `task3:task3-ab-physics-ml-001:case_06` | `case_06` | `assets/images/task3-ab-physics-ml-001/case_06/explicit_01_page_002.png` | Fig. 2 | figure |
+| `task3:task3-ab-physics-ml-001:case_06` | `case_06` | `assets/images/task3-ab-physics-ml-001/case_06/explicit_02_page_002.png` | Fig. 2 | figure |
+| `task3:task3-ab-physics-ml-001:case_06` | `case_06` | `assets/images/task3-ab-physics-ml-001/case_06/explicit_03_page_002.png` | Fig. 2 | figure |
+| `task3:task3-ab-physics-ml-001:case_06` | `case_06` | `assets/images/task3-ab-physics-ml-001/case_06/explicit_04_page_002.png` | Fig. 2 | figure |
+| `task3:task3-ab-physics-ml-001:case_10` | `case_10` | `assets/images/task3-ab-physics-ml-001/case_10/explicit_01_page_003.png` | Fig. 3 | figure |
+| `task3:task3-ab-physics-ml-001:case_10` | `case_10` | `assets/images/task3-ab-physics-ml-001/case_10/explicit_02_page_003.png` | Fig. 3 | figure |
+| `task3:task3-ab-physics-ml-001:case_10` | `case_10` | `assets/images/task3-ab-physics-ml-001/case_10/explicit_03_page_003.png` | Fig. 3 | figure |
+| `task3:task3-ab-physics-ml-001:case_10` | `case_10` | `assets/images/task3-ab-physics-ml-001/case_10/explicit_04_page_003.png` | Fig. 3 | figure |
+| `task3:task3-ab-physics-ml-001:case_13` | `case_13` | `assets/images/task3-ab-physics-ml-001/case_13/explicit_01_page_001.png` | Fig. 1 | figure |
+| `task3:task3-ab-physics-ml-001:case_13` | `case_13` | `assets/images/task3-ab-physics-ml-001/case_13/explicit_02_page_001.png` | Fig. 1 | figure |
+| `task3:task3-ab-physics-ml-001:case_13` | `case_13` | `assets/images/task3-ab-physics-ml-001/case_13/explicit_03_page_001.png` | Fig. 1 | figure |
+| `task3:task3-ab-physics-ml-001:case_13` | `case_13` | `assets/images/task3-ab-physics-ml-001/case_13/explicit_04_page_001.png` | Fig. 1 | figure |
+
 ## https://arxiv.org/abs/2410.17881
 
 - Ссылка на статью: https://arxiv.org/abs/2410.17881
@@ -809,6 +897,24 @@
 | `task3:vasin_artem_aleksandrovich__0cf7f9ceca:case-009` | `CASE-009` | `assets/images/vasin_artem_aleksandrovich__0cf7f9ceca/case-009/explicit_03_page_001.png` | Fig. 1, 2 | figure |
 | `task3:vasin_artem_aleksandrovich__0cf7f9ceca:case-009` | `CASE-009` | `assets/images/vasin_artem_aleksandrovich__0cf7f9ceca/case-009/explicit_04_page_001.png` | Fig. 1, 2 | figure |
 
+## https://arxiv.org/abs/2501.05547
+
+- Ссылка на статью: https://arxiv.org/abs/2501.05547
+- Название: Deep learning of phase transitions with minimal examples
+- Идентификаторы: arxiv:2501.05547
+- Использовано изображений/страниц: 8
+
+| sample_id | case_id | image | page_hint | evidence_kind |
+|---|---|---|---|---|
+| `task3:task3-ab-physics-ml-001:case_11` | `case_11` | `assets/images/task3-ab-physics-ml-001/case_11/explicit_01_page_002.png` | Fig. 2 | figure |
+| `task3:task3-ab-physics-ml-001:case_11` | `case_11` | `assets/images/task3-ab-physics-ml-001/case_11/explicit_02_page_002.png` | Fig. 2 | figure |
+| `task3:task3-ab-physics-ml-001:case_11` | `case_11` | `assets/images/task3-ab-physics-ml-001/case_11/explicit_03_page_002.png` | Fig. 2 | figure |
+| `task3:task3-ab-physics-ml-001:case_11` | `case_11` | `assets/images/task3-ab-physics-ml-001/case_11/explicit_04_page_002.png` | Fig. 2 | figure |
+| `task3:task3-ab-physics-ml-001:case_14` | `case_14` | `assets/images/task3-ab-physics-ml-001/case_14/explicit_01_page_001.png` | Fig. 1 | figure |
+| `task3:task3-ab-physics-ml-001:case_14` | `case_14` | `assets/images/task3-ab-physics-ml-001/case_14/explicit_02_page_001.png` | Fig. 1 | figure |
+| `task3:task3-ab-physics-ml-001:case_14` | `case_14` | `assets/images/task3-ab-physics-ml-001/case_14/explicit_03_page_001.png` | Fig. 1 | figure |
+| `task3:task3-ab-physics-ml-001:case_14` | `case_14` | `assets/images/task3-ab-physics-ml-001/case_14/explicit_04_page_001.png` | Fig. 1 | figure |
+
 ## https://arxiv.org/abs/2501.06312
 
 - Ссылка на статью: https://arxiv.org/abs/2501.06312
diff --git a/README.md b/README.md
index e017a402c35cd09e14fcd014a1a36c4e5aaac338..1be06afec493138565252e515fee4cb19d1fbbb8 100644
--- a/README.md
+++ b/README.md
@@ -16,7 +16,7 @@ tags:
 
 Этот датасет содержит Task 3 кейсы для генерации ответа одной vision-language моделью. Исходные экспертные A/B prompts преобразуются в модельную задачу: модель должна извлечь факты из текста, страниц PDF, рисунков или таблиц и вернуть структурированный JSON-ответ. Экспертные rationale и ожидаемые ошибки вынесены в отдельные metadata-файлы, чтобы не протекать в prompt оцениваемой модели.
 
-Дата генерации README: `2026-05-28T19:45:17Z`.
+Дата генерации README: `2026-06-03T07:06:18Z`.
 
 ## Главный датасет: `data/task3_vlm_generation.jsonl`
 
@@ -31,7 +31,7 @@ tags:
 - `generation_target_schema` — ожидаемая структура JSON-ответа;
 - `review_metadata` — диагностические поля для эксперта; их не следует передавать оцениваемой VLM.
 
-Строк в текущей сборке: **372**. Строк с изображениями: **372**. Всего ссылок на изображения: **1488**.
+Строк в текущей сборке: **386**. Строк с изображениями: **386**. Всего ссылок на изображения: **1544**.
 
 ## Дополнительные файлы
 
@@ -39,7 +39,7 @@ tags:
 
 Плоская таблица кейсов для аудита. Здесь удобно проверять `case_id`, `paper_title`, `paper_id`, `creator_prompt`, `model_task_prompt`, `review_focus` и enabled/primary flags без раскрытия полной message-структуры.
 
-Строк: **413**.
+Строк: **427**.
 
 ### `data/task3_cases_summary.csv`
 
@@ -65,13 +65,13 @@ CSV-сводка по строкам генерационного датасет
 
 ## Распределение по слоям сложности
 
-- `easy_control`: 59
-- `multimodal_hard`: 220
-- `temporal_hard`: 93
+- `easy_control`: 61
+- `multimodal_hard`: 228
+- `temporal_hard`: 97
 
 ## Распределение по типам evidence
 
-- `figure`: 147
+- `figure`: 161
 - `figure_or_table`: 99
 - `formula`: 9
 - `mixed`: 23
diff --git a/article_image_sources.jsonl b/article_image_sources.jsonl
index b4b4068a1ed5b47272ab6ff262de545f43c36bb1..33a8c04aab74a77ce0dfb7040329aa214165562c 100644
--- a/article_image_sources.jsonl
+++ b/article_image_sources.jsonl
@@ -1,3 +1,3 @@
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-oid sha256:79b85478785e8f70dfe0ebfad38aca5f8d171ba7e55bae26ae82cb66a7a801f3
-size 985764
+oid sha256:667b9f65e10de4bab5270109489009ee90e466b5a2b68042c4e16adbf0ca3e04
+size 1019688
diff --git a/assets/images/task3-ab-physics-ml-001/case_01/explicit_01_page_002.png b/assets/images/task3-ab-physics-ml-001/case_01/explicit_01_page_002.png
new file mode 100644
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diff --git a/data/task3_cases_flat.jsonl b/data/task3_cases_flat.jsonl
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diff --git a/data/task3_cases_summary.csv b/data/task3_cases_summary.csv
index a67ddaf2fdba89389ef1b7b8eef3b04062189aa0..197893d129bc48ee2452e0260529152f31edde72 100644
--- a/data/task3_cases_summary.csv
+++ b/data/task3_cases_summary.csv
@@ -359,6 +359,20 @@ task3:9b54231119:case-002,Дистанционное зондирование З
 task3:9b54231119:case-003,Дистанционное зондирование Земли,тимофеев-кирилл-анатольевич__9b54231119,CASE-003,multimodal_hard,True,Дистанционное зондирование. Модели и методы обработки изображений.,978-5-94836-244-1,2020,figure_or_table,"p.20, Fig.1.1",4,"Оцени количество найденных моделей, количество и качество сопоставленных с моделями пар значений количества спектральных каналов и интервалов дискретизации"
 task3:9b54231119:case-004,Дистанционное зондирование Земли,тимофеев-кирилл-анатольевич__9b54231119,CASE-004,multimodal_hard,True,Методика ранжирования космических аппаратов дистанционного зондирования Земли     с целью оперативного мониторинга чрезвычайных ситуаций,10.34759/trd-2021-119-18,2021,mixed,p.5,4,"В тексте описан математический подход расчета вклада аппарата, оборудованного для задач ДЗЗ и мониторинга лесных хозяйств. Сравни распознавание формул и общих математических положений этой задачи"
 task3:9b54231119:case-005,Дистанционное зондирование Земли,тимофеев-кирилл-анатольевич__9b54231119,CASE-005,multimodal_hard,True,Методы дистанционного зондирования земли в лесной промышленности,10.47813/2782-2818-2023-3-3-0301-0310,2023,figure,"p.7, Fig.5",4,"Сравни понимание схемы работы системы ""Лесоохранитель"""
+task3:task3-ab-physics-ml-001:case_01,Применение ML в теоретической физике,task3-ab-physics-ml-001,case_01,multimodal_hard,True,Machine learning phases of matter,arxiv:1605.01735,2016,figure,Fig. 2,4,"Назовите по этому рисунку критическую температуру Tc треугольной модели Изинга, считав её как точку пересечения кривых выхода нейросети."
+task3:task3-ab-physics-ml-001:case_02,Применение ML в теоретической физике,task3-ab-physics-ml-001,case_02,multimodal_hard,True,Machine learning phases of matter,arxiv:1605.01735,2016,figure,Fig. 4,4,"Определите по этой схеме, сколько фильтров и какого размера применяет первый свёрточный слой сети."
+task3:task3-ab-physics-ml-001:case_03,Применение ML в теоретической физике,task3-ab-physics-ml-001,case_03,multimodal_hard,True,Machine learning phases of matter,arxiv:1605.01735,2016,figure,Fig. 3,4,"Определите по этому рисунку, на какой панели изображено основное состояние изинговской калибровочной теории (Ising lattice gauge theory)."
+task3:task3-ab-physics-ml-001:case_04,Применение ML в теоретической физике,task3-ab-physics-ml-001,case_04,multimodal_hard,True,Normalizing flows for lattice gauge theory in arbitrary space-time dimension,arxiv:2305.02402,2023,figure,Fig. 1,4,"Опишите по этой схеме, как coupling layer разделяет переменные на активное и замороженное подмножества с помощью маски."
+task3:task3-ab-physics-ml-001:case_05,Применение ML в теоретической физике,task3-ab-physics-ml-001,case_05,multimodal_hard,True,Normalizing flows for lattice gauge theory in arbitrary space-time dimension,arxiv:2305.02402,2023,figure,Fig. 2,4,"Опишите по этому рисунку, какие координаты используются для эквивариантных преобразований спектра собственных значений матриц SU(3)."
+task3:task3-ab-physics-ml-001:case_06,Применение ML в теоретической физике,task3-ab-physics-ml-001,case_06,multimodal_hard,True,Non-Perturbative Trivializing Flows for Lattice Gauge Theories,arxiv:2410.13161,2024,figure,Fig. 2,4,Сравните по этому рисунку плотности SU(3) при beta=9 между тремя эквивариантными целевыми распределениями и обученными потоками.
+task3:task3-ab-physics-ml-001:case_07,Применение ML в теоретической физике,task3-ab-physics-ml-001,case_07,multimodal_hard,True,Learning phase transitions by siamese neural network,arxiv:2405.16769,2024,figure,Fig. 3,4,Определите по этой схеме структуру сиамской сети: что подаётся ей на вход и сколько идентичных ветвей она содержит.
+task3:task3-ab-physics-ml-001:case_08,Применение ML в теоретической физике,task3-ab-physics-ml-001,case_08,multimodal_hard,True,Learning phase transitions by siamese neural network,arxiv:2405.16769,2024,figure,Fig. 4,4,"Определите по этому рисунку, при каком значении управляющего параметра кривая сходства s сиамской сети резко меняется."
+task3:task3-ab-physics-ml-001:case_09,Применение ML в теоретической физике,task3-ab-physics-ml-001,case_09,temporal_hard,True,Normalizing flows for lattice gauge theory in arbitrary space-time dimension,arxiv:2305.02402,2023,figure,Fig. 6,4,"Считайте с этого рисунка, как эволюционирует ESS в ходе обучения и где проявляется эффект «второго дыхания» (second wind)."
+task3:task3-ab-physics-ml-001:case_10,Применение ML в теоретической физике,task3-ab-physics-ml-001,case_10,temporal_hard,True,Non-Perturbative Trivializing Flows for Lattice Gauge Theories,arxiv:2410.13161,2024,figure,Fig. 3,4,"Определите по этому рисунку, как меняется эффективный размер выборки (ESS) вдоль времени потока (flow time)."
+task3:task3-ab-physics-ml-001:case_11,Применение ML в теоретической физике,task3-ab-physics-ml-001,case_11,temporal_hard,True,Deep learning of phase transitions with minimal examples,arxiv:2501.05547,2025,figure,Fig. 2,4,"Определите по перевзвешенной восприимчивости chi_P на этом рисунке, в какой точке по управляющему параметру расположен фазовый переход."
+task3:task3-ab-physics-ml-001:case_12,Применение ML в теоретической физике,task3-ab-physics-ml-001,case_12,temporal_hard,True,Learning phase transitions by siamese neural network,arxiv:2405.16769,2024,figure,Fig. 5,4,"Определите по этому рисунку, как меняется выход сиамской сети при увеличении размера системы L от 16 до 64."
+task3:task3-ab-physics-ml-001:case_13,Применение ML в теоретической физике,task3-ab-physics-ml-001,case_13,easy_control,False,Non-Perturbative Trivializing Flows for Lattice Gauge Theories,arxiv:2410.13161,2024,figure,Fig. 1,4,"Опишите по этой схеме, какие петли (loops) используются в ODE-архитектуре и где расположен anchor."
+task3:task3-ab-physics-ml-001:case_14,Применение ML в теоретической физике,task3-ab-physics-ml-001,case_14,easy_control,False,Deep learning of phase transitions with minimal examples,arxiv:2501.05547,2025,figure,Fig. 1,4,"Опишите, что показывает этот график среднего значения плакетты <P> для моделей BAL20 и BAL по разным размерам решётки."
 task3:cetacean-acoustics-expert__ce0d04dcb5:case-001,"Cetacean passive acoustic monitoring, photo-ID, and ML-based species classification",cetacean-acoustics-expert__ce0d04dcb5,CASE-001,multimodal_hard,True,Deep neural networks for automated detection of marine mammal species,10.1038/s41598-020-57549-y,2020,figure_or_table,Fig. 2 (spectrogram panels) + Table 2 (per-species F1),4,"Глядя на спектрограммы Fig. 2 и таблицу 2, ответь: какие два вида цетацеев модель путает чаще всего и какой акустический признак (полоса частот / длительность) это объясняет? Использовать ТОЛЬКО информацию из фигуры и подписи."
 task3:cetacean-acoustics-expert__ce0d04dcb5:case-002,"Cetacean passive acoustic monitoring, photo-ID, and ML-based species classification",cetacean-acoustics-expert__ce0d04dcb5,CASE-002,multimodal_hard,True,ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit,10.1038/s41598-019-47335-w,2019,figure_or_table,Fig. 4 ROC + Fig. 5 example spectrograms,4,"По Fig. 4 и Fig. 5 объясни, при каком пороге детекции ORCA-SPOT теряет тихие call-types и какие визуальные характеристики спектрограмм соответствуют этим потерям."
 task3:cetacean-acoustics-expert__ce0d04dcb5:case-003,"Cetacean passive acoustic monitoring, photo-ID, and ML-based species classification",cetacean-acoustics-expert__ce0d04dcb5,CASE-003,multimodal_hard,True,"Advanced image recognition: a fully automated, high-accuracy photo-identification matching system for humpback whales",10.1111/2041-210X.13436,2022,figure_or_table,Fig. 3 — fluke matching pipeline + Fig. 6 confusion examples,4,Какие 2 типа визуальных артефактов (по Fig. 6) приводят к ложным совпадениям fluke-ID и как pipeline в Fig. 3 их компенсирует?
diff --git a/data/task3_vlm_generation.jsonl b/data/task3_vlm_generation.jsonl
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diff --git a/metadata/build_summary.json b/metadata/build_summary.json
index 67e5ac62365134c95c008fe22b3ec85d0559c143..1f84b4d028dcc0ba04ef84f50f04386b2e540df6 100644
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+  "generated_at": "2026-06-03T07:06:16Z",
   "dataset_repo_id": "top-papers/top-papers-graph-benchmark",
   "dataset_config_name": "task3_vlm_generation",
   "split_name": "test",
@@ -42,6 +42,7 @@
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     "/content/task3_hf_work/selected_task3_inputs/0037_timofeev_kirill_anatol_evich__task3_ab_case_manifest.filled - Kirill Timofeeff.json",
+    "/content/task3_hf_work/selected_task3_inputs/0038_fedin_matvei_mikhailovich__task3_ab_case_manifest.filled - Матвей Федин.json",
     "/content/top_papers_graph_benchmark_task3/_work/extracted_archives/0002_kubrakova_ekaterina_aleksandrovna__task3_ab_creator_rest_files_ru__1cd56a407036/task3_ab_case_manifest.template.json",
     "/content/top_papers_graph_benchmark_task3/_work/extracted_archives/0010_shevchenko_dar_ia_andreevna__task3_ab_creator_rest_files_ru__1e50e912a3a6/task3_ab_case_manifest.template.json",
     "/content/top_papers_graph_benchmark_task3/_work/extracted_archives/0023_khuditskii_vasilii_olegovich__task3_ab_creator_bundle_ru__770bc4479ddd/task3_ab_case_manifest.template.json",
@@ -116,13 +117,13 @@
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-  "cases_total": 413,
-  "cases_written": 372,
+  "manifests_discovered": 40,
+  "cases_total": 427,
+  "cases_written": 386,
   "cases_skipped_disabled": 0,
   "cases_skipped_incomplete": 41,
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-  "explicit_images_copied": 1488,
+  "cases_with_images": 386,
+  "explicit_images_copied": 1544,
   "rendered_pages": 0,
   "pdfs_discovered": 4,
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diff --git a/review_metadata/task3_case_rationales.jsonl b/review_metadata/task3_case_rationales.jsonl
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--- a/review_metadata/task3_case_rationales.jsonl
+++ b/review_metadata/task3_case_rationales.jsonl
@@ -1,3 +1,3 @@
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@@ -0,0 +1,464 @@
+{
+  "schema_version": "task3-ab-creator-v1",
+  "experiment_meta": {
+    "topic": "Применение ML в теоретической физике",
+    "submission_id": "task3-ab-physics-ml-001",
+    "creator_id": "Федин Матвей Михайлович",
+    "cutoff_year": "2026",
+    "review_goal": "Проверить, где дообученная VLM (SFT/DPO) реально превосходит baseline на извлечении мультимодальных evidence (рисунки) и на временной/режимной привязке выводов в статьях по ML в теоретической физике: фазовые переходы и решёточные normalizing flows."
+  },
+  "cases": [
+    {
+      "case_id": "case_01",
+      "enabled": true,
+      "primary_endpoint": true,
+      "stratum": "multimodal_hard",
+      "paper_title": "Machine learning phases of matter",
+      "paper_id": "arxiv:1605.01735",
+      "year": 2016,
+      "evidence_kind": "figure",
+      "page_hint": "Fig. 2",
+      "creator_prompt": "Назовите по этому рисунку критическую температуру Tc треугольной модели Изинга, считав её как точку пересечения кривых выхода нейросети.",
+      "creator_rationale": "Tc закодирован геометрически — как абсцисса пересечения двух выходных кривых. Baseline пересказывает подпись, но не считывает координату пересечения с графика.",
+      "review_focus": [
+        "evidence",
+        "visual_fact"
+      ],
+      "expected_error_modes": [
+        "missed_visual_fact"
+      ],
+      "match": {
+        "candidate_signature": "",
+        "candidate_source_contains": "",
+        "candidate_predicate_contains": "",
+        "candidate_target_contains": "",
+        "hypothesis_title_contains": "triangular Ising critical temperature",
+        "premise_contains": "output layer crossing",
+        "mechanism_contains": "",
+        "time_scope_contains": "",
+        "rank_hint": ""
+      },
+      "notes": "Проверенный рисунок (arxiv 1605.01735) Fig 2: Figure 2: Detecting the critical temperature of the triangular Ising model through the crossing of the values of the output layer vs T T . The orange line signals the triangular Ising model T c / J = 4 / ln 3 subscript 4 3 T_{c}/J=4/\\ln 3 ,"
+    },
+    {
+      "case_id": "case_02",
+      "enabled": true,
+      "primary_endpoint": true,
+      "stratum": "multimodal_hard",
+      "paper_title": "Machine learning phases of matter",
+      "paper_id": "arxiv:1605.01735",
+      "year": 2016,
+      "evidence_kind": "figure",
+      "page_hint": "Fig. 4",
+      "creator_prompt": "Определите по этой схеме, сколько фильтров и какого размера применяет первый свёрточный слой сети.",
+      "creator_rationale": "Число и размер фильтров (64 шт., 2x2) считываются только со схемы архитектуры; baseline называет произвольные значения, не глядя на рисунок.",
+      "review_focus": [
+        "evidence",
+        "visual_fact"
+      ],
+      "expected_error_modes": [
+        "missed_visual_fact",
+        "wrong_evidence_linkage"
+      ],
+      "match": {
+        "candidate_signature": "",
+        "candidate_source_contains": "",
+        "candidate_predicate_contains": "",
+        "candidate_target_contains": "",
+        "hypothesis_title_contains": "convolutional neural network architecture",
+        "premise_contains": "first hidden layer filters",
+        "mechanism_contains": "",
+        "time_scope_contains": "",
+        "rank_hint": ""
+      },
+      "notes": "Проверенный рисунок (arxiv 1605.01735) Fig 4: Figure 4: Illustrating the convolutional neural network. The first hidden layer convolves 64 2 2 2 2 2\\times 2 filters with the spin configuration on each sublattice, followed by rectified linear units (ReLu). The outcome is followed by ful"
+    },
+    {
+      "case_id": "case_03",
+      "enabled": true,
+      "primary_endpoint": true,
+      "stratum": "multimodal_hard",
+      "paper_title": "Machine learning phases of matter",
+      "paper_id": "arxiv:1605.01735",
+      "year": 2016,
+      "evidence_kind": "figure",
+      "page_hint": "Fig. 3",
+      "creator_prompt": "Определите по этому рисунку, на какой панели изображено основное состояние изинговской калибровочной теории (Ising lattice gauge theory).",
+      "creator_rationale": "Привязка нужной панели к конкретной модели — чисто визуальный факт; baseline путает панели square ice и Ising gauge или галлюцинирует.",
+      "review_focus": [
+        "visual_fact",
+        "evidence"
+      ],
+      "expected_error_modes": [
+        "missed_visual_fact",
+        "hallucinated_visual_inference"
+      ],
+      "match": {
+        "candidate_signature": "",
+        "candidate_source_contains": "",
+        "candidate_predicate_contains": "",
+        "candidate_target_contains": "",
+        "hypothesis_title_contains": "Ising lattice gauge theory",
+        "premise_contains": "ground state configuration panel",
+        "mechanism_contains": "",
+        "time_scope_contains": "",
+        "rank_hint": ""
+      },
+      "notes": "Проверенный рисунок (arxiv 1605.01735) Fig 3: Figure 3: Typical configurations of square ice and Ising gauge models. (A) A high-temperature state. (B) A ground state of the square ice Hamiltonian. (C) A ground state configuration of the Ising lattice gauge theory. The vertices and plaq"
+    },
+    {
+      "case_id": "case_04",
+      "enabled": true,
+      "primary_endpoint": true,
+      "stratum": "multimodal_hard",
+      "paper_title": "Normalizing flows for lattice gauge theory in arbitrary space-time dimension",
+      "paper_id": "arxiv:2305.02402",
+      "year": 2023,
+      "evidence_kind": "figure",
+      "page_hint": "Fig. 1",
+      "creator_prompt": "Опишите по этой схеме, как coupling layer разделяет переменные на активное и замороженное подмножества с помощью маски.",
+      "creator_rationale": "Структура coupling layer (что активно, что заморожено) считывается только со схемы; baseline путает активные и замороженные переменные.",
+      "review_focus": [
+        "evidence",
+        "visual_fact"
+      ],
+      "expected_error_modes": [
+        "missed_visual_fact",
+        "wrong_evidence_linkage"
+      ],
+      "match": {
+        "candidate_signature": "",
+        "candidate_source_contains": "",
+        "candidate_predicate_contains": "",
+        "candidate_target_contains": "",
+        "hypothesis_title_contains": "coupling layer structure",
+        "premise_contains": "active and frozen subsets mask",
+        "mechanism_contains": "",
+        "time_scope_contains": "",
+        "rank_hint": ""
+      },
+      "notes": "Проверенный рисунок (arxiv 2305.02402) Fig 1: Figure 1: Structure of a masked autoregressive flow transformation with triangular Jacobian, i.e., a coupling layer. First, a set of variables x x is decomposed into active and frozen subsets x a , x f subscript subscript x_{a},x_{f} using "
+    },
+    {
+      "case_id": "case_05",
+      "enabled": true,
+      "primary_endpoint": true,
+      "stratum": "multimodal_hard",
+      "paper_title": "Normalizing flows for lattice gauge theory in arbitrary space-time dimension",
+      "paper_id": "arxiv:2305.02402",
+      "year": 2023,
+      "evidence_kind": "figure",
+      "page_hint": "Fig. 2",
+      "creator_prompt": "Опишите по этому рисунку, какие координаты используются для эквивариантных преобразований спектра собственных значений матриц SU(3).",
+      "creator_rationale": "Выбор канонической ячейки и полярных координат — геометрический факт со схемы; baseline подменяет его общими словами про SU(3).",
+      "review_focus": [
+        "evidence",
+        "visual_fact"
+      ],
+      "expected_error_modes": [
+        "missed_visual_fact"
+      ],
+      "match": {
+        "candidate_signature": "",
+        "candidate_source_contains": "",
+        "candidate_predicate_contains": "",
+        "candidate_target_contains": "",
+        "hypothesis_title_contains": "SU(3) eigenvalue spectrum coordinates",
+        "premise_contains": "canonical cell polar coordinates",
+        "mechanism_contains": "",
+        "time_scope_contains": "",
+        "rank_hint": ""
+      },
+      "notes": "Проверенный рисунок (arxiv 2305.02402) Fig 2: Figure 2: Depiction of the choice of canonical cell and polar coordinates used to define permutation-equivariant transformations of eigenvalue spectra of SU ( 3 ) SU 3 \\mathrm{SU}(3) matrices, resulting in conjugation-equivariant transforma"
+    },
+    {
+      "case_id": "case_06",
+      "enabled": true,
+      "primary_endpoint": true,
+      "stratum": "multimodal_hard",
+      "paper_title": "Non-Perturbative Trivializing Flows for Lattice Gauge Theories",
+      "paper_id": "arxiv:2410.13161",
+      "year": 2024,
+      "evidence_kind": "figure",
+      "page_hint": "Fig. 2",
+      "creator_prompt": "Сравните по этому рисунку плотности SU(3) при beta=9 между тремя эквивариантными целевыми распределениями и обученными потоками.",
+      "creator_rationale": "Совпадение/различие плотностей между мишенями и потоками — визуальное сравнение нескольких серий; baseline не сверяет кривые между собой.",
+      "review_focus": [
+        "visual_fact",
+        "evidence"
+      ],
+      "expected_error_modes": [
+        "hallucinated_visual_inference",
+        "missed_visual_fact"
+      ],
+      "match": {
+        "candidate_signature": "",
+        "candidate_source_contains": "",
+        "candidate_predicate_contains": "",
+        "candidate_target_contains": "",
+        "hypothesis_title_contains": "SU(3) density comparison",
+        "premise_contains": "conjugation equivariant targets beta 9",
+        "mechanism_contains": "",
+        "time_scope_contains": "",
+        "rank_hint": ""
+      },
+      "notes": "Проверенный рисунок (arxiv 2410.13161) Fig 2: Figure 2: Comparison of the SU ( 3 ) {\\mathrm{SU(3)}} densities at = 9 \\beta=9 in terms of angular coordinates for the three conjugation equivariant targets listed in table 1 and the respectively trained continuous normalizing flows."
+    },
+    {
+      "case_id": "case_07",
+      "enabled": true,
+      "primary_endpoint": true,
+      "stratum": "multimodal_hard",
+      "paper_title": "Learning phase transitions by siamese neural network",
+      "paper_id": "arxiv:2405.16769",
+      "year": 2024,
+      "evidence_kind": "figure",
+      "page_hint": "Fig. 3",
+      "creator_prompt": "Определите по этой схеме структуру сиамской сети: что подаётся ей на вход и сколько идентичных ветвей она содержит.",
+      "creator_rationale": "Парный вход (S_i, S_j) и число идентичных ветвей считываются только с диаграммы; baseline ошибается в структуре сиамской сети.",
+      "review_focus": [
+        "evidence",
+        "visual_fact"
+      ],
+      "expected_error_modes": [
+        "missed_visual_fact",
+        "wrong_evidence_linkage"
+      ],
+      "match": {
+        "candidate_signature": "",
+        "candidate_source_contains": "",
+        "candidate_predicate_contains": "",
+        "candidate_target_contains": "",
+        "hypothesis_title_contains": "siamese network structure",
+        "premise_contains": "pair of input configurations",
+        "mechanism_contains": "",
+        "time_scope_contains": "",
+        "rank_hint": ""
+      },
+      "notes": "Проверенный рисунок (arxiv 2405.16769) Fig 3: Figure 3: The structure of SNN. S i S_{i} and S j S_{j} represent a pair of input configurations (e.g., spatiotemporal configurations generated by bond probabilities p i , p j p_{i},p_{j} in the DP model, or spin configurations generated by"
+    },
+    {
+      "case_id": "case_08",
+      "enabled": true,
+      "primary_endpoint": true,
+      "stratum": "multimodal_hard",
+      "paper_title": "Learning phase transitions by siamese neural network",
+      "paper_id": "arxiv:2405.16769",
+      "year": 2024,
+      "evidence_kind": "figure",
+      "page_hint": "Fig. 4",
+      "creator_prompt": "Определите по этому рисунку, при каком значении управляющего параметра кривая сходства s сиамской сети резко меняется.",
+      "creator_rationale": "Точка резкого перехода кривой сходства s — чисто визуальный признак фазового перехода; baseline теряет её при пересказе подписи.",
+      "review_focus": [
+        "visual_fact",
+        "evidence"
+      ],
+      "expected_error_modes": [
+        "missed_visual_fact",
+        "wrong_evidence_linkage"
+      ],
+      "match": {
+        "candidate_signature": "",
+        "candidate_source_contains": "",
+        "candidate_predicate_contains": "",
+        "candidate_target_contains": "",
+        "hypothesis_title_contains": "siamese similarity transition",
+        "premise_contains": "SNN output similarity anchor",
+        "mechanism_contains": "",
+        "time_scope_contains": "",
+        "rank_hint": ""
+      },
+      "notes": "Проверенный рисунок (arxiv 2405.16769) Fig 4: Figure 4: The output of SNN with the test anchor p a = 0 p_{a}=0 at L = 32 L=32 . The blue curve represents the similarity ( s s ), while the ensemble average of the test samples is 1000 1000 . The light blue curve represents the non-simila"
+    },
+    {
+      "case_id": "case_09",
+      "enabled": true,
+      "primary_endpoint": true,
+      "stratum": "temporal_hard",
+      "paper_title": "Normalizing flows for lattice gauge theory in arbitrary space-time dimension",
+      "paper_id": "arxiv:2305.02402",
+      "year": 2023,
+      "evidence_kind": "figure",
+      "page_hint": "Fig. 6",
+      "creator_prompt": "Считайте с этого рисунка, как эволюционирует ESS в ходе обучения и где проявляется эффект «второго дыхания» (second wind).",
+      "creator_rationale": "Ось — ход обучения: важно привязать вывод к динамике (ранние стадии vs второе дыхание), а не к финальной точке. Baseline теряет временную привязку.",
+      "review_focus": [
+        "temporal",
+        "evidence"
+      ],
+      "expected_error_modes": [
+        "needs_time_fix",
+        "wrong_evidence_linkage"
+      ],
+      "match": {
+        "candidate_signature": "",
+        "candidate_source_contains": "",
+        "candidate_predicate_contains": "",
+        "candidate_target_contains": "",
+        "hypothesis_title_contains": "ESS second wind effect",
+        "premise_contains": "",
+        "mechanism_contains": "",
+        "time_scope_contains": "evolution of ESS during training",
+        "rank_hint": ""
+      },
+      "notes": "Проверенный рисунок (arxiv 2305.02402) Fig 6: Figure 6: Example comparison of the evolution of the ESS for two equivalent spectral flow architectures with the same random seed trained with and without control variates, demonstrating significantly improved model quality already in the e"
+    },
+    {
+      "case_id": "case_10",
+      "enabled": true,
+      "primary_endpoint": true,
+      "stratum": "temporal_hard",
+      "paper_title": "Non-Perturbative Trivializing Flows for Lattice Gauge Theories",
+      "paper_id": "arxiv:2410.13161",
+      "year": 2024,
+      "evidence_kind": "figure",
+      "page_hint": "Fig. 3",
+      "creator_prompt": "Определите по этому рисунку, как меняется эффективный размер выборки (ESS) вдоль времени потока (flow time).",
+      "creator_rationale": "Ось — время потока: зависимость ESS от flow time это динамика во «времени потока»; baseline путает её с другими параметрами.",
+      "review_focus": [
+        "temporal",
+        "evidence"
+      ],
+      "expected_error_modes": [
+        "needs_time_fix",
+        "wrong_evidence_linkage"
+      ],
+      "match": {
+        "candidate_signature": "",
+        "candidate_source_contains": "",
+        "candidate_predicate_contains": "",
+        "candidate_target_contains": "",
+        "hypothesis_title_contains": "normalizing flow ESS flow time",
+        "premise_contains": "",
+        "mechanism_contains": "",
+        "time_scope_contains": "ESS along flow time",
+        "rank_hint": ""
+      },
+      "notes": "Проверенный рисунок (arxiv 2410.13161) Fig 3: Figure 3: Effective sample size along the flow time of trained normalizing flows for each of the three families of target distributions listed in table 1 in comparison to the ESS achieved by the individual discrete flows from [ 8 ] ."
+    },
+    {
+      "case_id": "case_11",
+      "enabled": true,
+      "primary_endpoint": true,
+      "stratum": "temporal_hard",
+      "paper_title": "Deep learning of phase transitions with minimal examples",
+      "paper_id": "arxiv:2501.05547",
+      "year": 2025,
+      "evidence_kind": "figure",
+      "page_hint": "Fig. 2",
+      "creator_prompt": "Определите по перевзвешенной восприимчивости chi_P на этом рисунке, в какой точке по управляющему параметру расположен фазовый переход.",
+      "creator_rationale": "Перевзвешивание продолжает кривую по управляющему параметру: положение пика chi_P это режимная привязка перехода. Baseline берёт точку вне области применимости.",
+      "review_focus": [
+        "temporal",
+        "evidence"
+      ],
+      "expected_error_modes": [
+        "needs_time_fix",
+        "wrong_evidence_linkage"
+      ],
+      "match": {
+        "candidate_signature": "",
+        "candidate_source_contains": "",
+        "candidate_predicate_contains": "",
+        "candidate_target_contains": "",
+        "hypothesis_title_contains": "susceptibility peak transition",
+        "premise_contains": "",
+        "mechanism_contains": "",
+        "time_scope_contains": "reweighting curve control parameter",
+        "rank_hint": ""
+      },
+      "notes": "Проверенный рисунок (arxiv 2501.05547) Fig 2: Figure 2: Reweighting of P \\chi_{P} (top) and P \\expectationvalue{P} (bottom) for the BAL20 (left) and BAL (right) models using the largest lattice available to each model. Data points indicate the output of the CNN. Each colored datum indi"
+    },
+    {
+      "case_id": "case_12",
+      "enabled": true,
+      "primary_endpoint": true,
+      "stratum": "temporal_hard",
+      "paper_title": "Learning phase transitions by siamese neural network",
+      "paper_id": "arxiv:2405.16769",
+      "year": 2024,
+      "evidence_kind": "figure",
+      "page_hint": "Fig. 5",
+      "creator_prompt": "Определите по этому рисунку, как меняется выход сиамской сети при увеличении размера системы L от 16 до 64.",
+      "creator_rationale": "Ось — размер системы L: важна динамика выхода с ростом L (finite-size), а не одно значение. Baseline смешивает разные L и теряет тренд.",
+      "review_focus": [
+        "temporal",
+        "evidence"
+      ],
+      "expected_error_modes": [
+        "needs_time_fix",
+        "wrong_evidence_linkage"
+      ],
+      "match": {
+        "candidate_signature": "",
+        "candidate_source_contains": "",
+        "candidate_predicate_contains": "",
+        "candidate_target_contains": "",
+        "hypothesis_title_contains": "finite-size SNN output",
+        "premise_contains": "",
+        "mechanism_contains": "",
+        "time_scope_contains": "SNN output across system sizes",
+        "rank_hint": ""
+      },
+      "notes": "Проверенный рисунок (arxiv 2405.16769) Fig 5: Figure 5: The output of SNN with the test anchor p a = 0 , 0.4 , 0.8 p_{a}=0,0.4,0.8 and 1 1 , at different sizes (a) L = 16 L=16 , (b) L = 32 L=32 , (c) L = 48 L=48 and (d) L = 64 L=64 ."
+    },
+    {
+      "case_id": "case_13",
+      "enabled": true,
+      "primary_endpoint": false,
+      "stratum": "easy_control",
+      "paper_title": "Non-Perturbative Trivializing Flows for Lattice Gauge Theories",
+      "paper_id": "arxiv:2410.13161",
+      "year": 2024,
+      "evidence_kind": "figure",
+      "page_hint": "Fig. 1",
+      "creator_prompt": "Опишите по этой схеме, какие петли (loops) используются в ODE-архитектуре и где расположен anchor.",
+      "creator_rationale": "Контрольный кейс: содержание простой схемы лежит на поверхности, baseline и tuned VLM должны справиться одинаково.",
+      "review_focus": [
+        "overall"
+      ],
+      "expected_error_modes": [],
+      "match": {
+        "candidate_signature": "",
+        "candidate_source_contains": "",
+        "candidate_predicate_contains": "",
+        "candidate_target_contains": "",
+        "hypothesis_title_contains": "loops ODE architecture",
+        "premise_contains": "",
+        "mechanism_contains": "",
+        "time_scope_contains": "",
+        "rank_hint": ""
+      },
+      "notes": "Проверенный рисунок (arxiv 2410.13161) Fig 1: Figure 1: Loops used in the ODE architecture. The first two loops, highlighted in red, are used for the non-linear superposition function. The anchor in the dual lattice is displayed as a dot."
+    },
+    {
+      "case_id": "case_14",
+      "enabled": true,
+      "primary_endpoint": false,
+      "stratum": "easy_control",
+      "paper_title": "Deep learning of phase transitions with minimal examples",
+      "paper_id": "arxiv:2501.05547",
+      "year": 2025,
+      "evidence_kind": "figure",
+      "page_hint": "Fig. 1",
+      "creator_prompt": "Опишите, что показывает этот график среднего значения плакетты <P> для моделей BAL20 и BAL по разным размерам решётки.",
+      "creator_rationale": "Второй простой контроль: однозначный обзорный график, минимальная мультимодальная сложность, ожидаемого разрыва между моделями нет.",
+      "review_focus": [
+        "overall"
+      ],
+      "expected_error_modes": [],
+      "match": {
+        "candidate_signature": "",
+        "candidate_source_contains": "",
+        "candidate_predicate_contains": "",
+        "candidate_target_contains": "",
+        "hypothesis_title_contains": "average plaquette versus lattice size",
+        "premise_contains": "",
+        "mechanism_contains": "",
+        "time_scope_contains": "",
+        "rank_hint": ""
+      },
+      "notes": "Проверенный рисунок (arxiv 2501.05547) Fig 1: Figure 1: P \\expectationvalue{P} for the BAL20 model (left) and BAL model (right) for each lattice size. Lines are drawn to guide the eye."
+    }
+  ]
+}
\ No newline at end of file