diff --git a/ARTICLE_IMAGE_SOURCES.md b/ARTICLE_IMAGE_SOURCES.md index 39753bf0b57132b04de2af5ebf1b9c8dbd30b0ae..f4db17e4834f663392f4a15fea015615b70641ce 100644 --- a/ARTICLE_IMAGE_SOURCES.md +++ b/ARTICLE_IMAGE_SOURCES.md @@ -1,11 +1,11 @@ # Источники статей для изображений -Файл создан автоматически: `2026-05-28T17:36:46Z`. +Файл создан автоматически: `2026-05-28T19:45:17Z`. 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: **1332**. -Уникальных статей/идентификаторов: **172**. +Всего image-записей: **1440**. +Уникальных статей/идентификаторов: **192**. ## 978-1-939133-44-1 @@ -39,6 +39,20 @@ | `task3:5ad3fe3294:case-002` | `CASE-002` | `assets/images/5ad3fe3294/case-002/explicit_03_page_002.png` | p.2 | page | | `task3:5ad3fe3294:case-002` | `CASE-002` | `assets/images/5ad3fe3294/case-002/explicit_04_page_002.png` | p.2 | page | +## 978-5-94836-244-1 + +- Ссылка на статью: 978-5-94836-244-1 +- Название: Дистанционное зондирование. Модели и методы обработки изображений. +- Идентификаторы: 978-5-94836-244-1 +- Использовано изображений/страниц: 4 + +| sample_id | case_id | image | page_hint | evidence_kind | +|---|---|---|---|---| +| `task3:9b54231119:case-003` | `CASE-003` | `assets/images/9b54231119/case-003/explicit_01_page_001.png` | p.20, Fig.1.1 | figure_or_table | +| `task3:9b54231119:case-003` | `CASE-003` | `assets/images/9b54231119/case-003/explicit_02_page_001.png` | p.20, Fig.1.1 | figure_or_table | +| `task3:9b54231119:case-003` | `CASE-003` | `assets/images/9b54231119/case-003/explicit_03_page_001.png` | p.20, Fig.1.1 | figure_or_table | +| `task3:9b54231119:case-003` | `CASE-003` | `assets/images/9b54231119/case-003/explicit_04_page_001.png` | p.20, Fig.1.1 | figure_or_table | + ## SSC18-XI-02 - Ссылка на статью: SSC18-XI-02 @@ -215,6 +229,20 @@ | `task3:df07798d94:case-003` | `CASE-003` | `assets/images/df07798d94/case-003/explicit_03_page_001.png` | Table 1 | table | | `task3:df07798d94:case-003` | `CASE-003` | `assets/images/df07798d94/case-003/explicit_04_page_001.png` | Table 1 | table | +## https://arxiv.org/abs/2206.14176 + +- Ссылка на статью: https://arxiv.org/abs/2206.14176 +- Название: DayDreamer: World Models for Physical Robot Learning +- Идентификаторы: arXiv:2206.14176 +- Использовано изображений/страниц: 4 + +| sample_id | case_id | image | page_hint | evidence_kind | +|---|---|---|---|---| +| `task3:6efae140d8:case-009` | `CASE-009` | `assets/images/6efae140d8/case-009/explicit_01_page_001.png` | Table 1 (часы реального wall-clock training до целевого поведения на quadruped/arm/wheeled) | table | +| `task3:6efae140d8:case-009` | `CASE-009` | `assets/images/6efae140d8/case-009/explicit_02_page_001.png` | Table 1 (часы реального wall-clock training до целевого поведения на quadruped/arm/wheeled) | table | +| `task3:6efae140d8:case-009` | `CASE-009` | `assets/images/6efae140d8/case-009/explicit_03_page_001.png` | Table 1 (часы реального wall-clock training до целевого поведения на quadruped/arm/wheeled) | table | +| `task3:6efae140d8:case-009` | `CASE-009` | `assets/images/6efae140d8/case-009/explicit_04_page_001.png` | Table 1 (часы реального wall-clock training до целевого поведения на quadruped/arm/wheeled) | table | + ## https://arxiv.org/abs/2211.14495 - Ссылка на статью: https://arxiv.org/abs/2211.14495 @@ -229,6 +257,20 @@ | `task3:cetacean-acoustics-expert__ce0d04dcb5:case-010` | `CASE-010` | `assets/images/cetacean-acoustics-expert__ce0d04dcb5/case-010/explicit_03_page_000.png` | Section 'Datasets' — which corpora published before/after cutoff | text_claim | | `task3:cetacean-acoustics-expert__ce0d04dcb5:case-010` | `CASE-010` | `assets/images/cetacean-acoustics-expert__ce0d04dcb5/case-010/explicit_04_page_000.png` | Section 'Datasets' — which corpora published before/after cutoff | text_claim | +## https://arxiv.org/abs/2301.04104 + +- Ссылка на статью: https://arxiv.org/abs/2301.04104 +- Название: DreamerV3: Mastering Diverse Domains through World Models +- Идентификаторы: arXiv:2301.04104 +- Использовано изображений/страниц: 4 + +| sample_id | case_id | image | page_hint | evidence_kind | +|---|---|---|---|---| +| `task3:6efae140d8:case-008` | `CASE-008` | `assets/images/6efae140d8/case-008/explicit_01_page_016.png` | Fig.1 (single hyperparam set across 150+ tasks), Fig.6 (long-horizon imagined rollouts) | figure_or_table | +| `task3:6efae140d8:case-008` | `CASE-008` | `assets/images/6efae140d8/case-008/explicit_02_page_016.png` | Fig.1 (single hyperparam set across 150+ tasks), Fig.6 (long-horizon imagined rollouts) | figure_or_table | +| `task3:6efae140d8:case-008` | `CASE-008` | `assets/images/6efae140d8/case-008/explicit_03_page_001.png` | Fig.1 (single hyperparam set across 150+ tasks), Fig.6 (long-horizon imagined rollouts) | figure_or_table | +| `task3:6efae140d8:case-008` | `CASE-008` | `assets/images/6efae140d8/case-008/explicit_04_page_001.png` | Fig.1 (single hyperparam set across 150+ tasks), Fig.6 (long-horizon imagined rollouts) | figure_or_table | + ## https://arxiv.org/abs/2303.08514 - Ссылка на статью: https://arxiv.org/abs/2303.08514 @@ -397,6 +439,20 @@ | `task3:7d0aad0635:case-006` | `CASE-006` | `assets/images/7d0aad0635/case-006/explicit_03_page_000.png` | p.14 | page | | `task3:7d0aad0635:case-006` | `CASE-006` | `assets/images/7d0aad0635/case-006/explicit_04_page_000.png` | p.14 | page | +## https://arxiv.org/abs/2310.16828 + +- Ссылка на статью: https://arxiv.org/abs/2310.16828 +- Название: TD-MPC2: Scalable, Robust World Models for Continuous Control +- Идентификаторы: arXiv:2310.16828 +- Использовано изображений/страниц: 4 + +| sample_id | case_id | image | page_hint | evidence_kind | +|---|---|---|---|---| +| `task3:6efae140d8:case-010` | `CASE-010` | `assets/images/6efae140d8/case-010/explicit_01_page_003.png` | Fig.4 (scaling curves: return vs. model size/steps), Table 3 (multi-task settings) | figure_or_table | +| `task3:6efae140d8:case-010` | `CASE-010` | `assets/images/6efae140d8/case-010/explicit_02_page_003.png` | Fig.4 (scaling curves: return vs. model size/steps), Table 3 (multi-task settings) | figure_or_table | +| `task3:6efae140d8:case-010` | `CASE-010` | `assets/images/6efae140d8/case-010/explicit_03_page_003.png` | Fig.4 (scaling curves: return vs. model size/steps), Table 3 (multi-task settings) | figure_or_table | +| `task3:6efae140d8:case-010` | `CASE-010` | `assets/images/6efae140d8/case-010/explicit_04_page_003.png` | Fig.4 (scaling curves: return vs. model size/steps), Table 3 (multi-task settings) | figure_or_table | + ## https://arxiv.org/abs/2311.08077 - Ссылка на статью: https://arxiv.org/abs/2311.08077 @@ -425,6 +481,20 @@ | `task3:998d6715e8:case-002` | `CASE-002` | `assets/images/998d6715e8/case-002/explicit_03_page_003.png` | p.8, Figure 3 | figure | | `task3:998d6715e8:case-002` | `CASE-002` | `assets/images/998d6715e8/case-002/explicit_04_page_003.png` | p.8, Figure 3 | figure | +## https://arxiv.org/abs/2312.13139 + +- Ссылка на статью: https://arxiv.org/abs/2312.13139 +- Название: GR-1: Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation +- Идентификаторы: arXiv:2312.13139 +- Использовано изображений/страниц: 4 + +| sample_id | case_id | image | page_hint | evidence_kind | +|---|---|---|---|---| +| `task3:6efae140d8:case-003` | `CASE-003` | `assets/images/6efae140d8/case-003/explicit_01_page_013.png` | Fig.1 (GPT-style future image + action prediction), Table 3 (CALVIN/manipulation results) | figure_or_table | +| `task3:6efae140d8:case-003` | `CASE-003` | `assets/images/6efae140d8/case-003/explicit_02_page_013.png` | Fig.1 (GPT-style future image + action prediction), Table 3 (CALVIN/manipulation results) | figure_or_table | +| `task3:6efae140d8:case-003` | `CASE-003` | `assets/images/6efae140d8/case-003/explicit_03_page_001.png` | Fig.1 (GPT-style future image + action prediction), Table 3 (CALVIN/manipulation results) | figure_or_table | +| `task3:6efae140d8:case-003` | `CASE-003` | `assets/images/6efae140d8/case-003/explicit_04_page_001.png` | Fig.1 (GPT-style future image + action prediction), Table 3 (CALVIN/manipulation results) | figure_or_table | + ## https://arxiv.org/abs/2312.13771 - Ссылка на статью: https://arxiv.org/abs/2312.13771 @@ -509,6 +579,20 @@ | `task3:vasin_artem_aleksandrovich__0cf7f9ceca:case-005` | `CASE-005` | `assets/images/vasin_artem_aleksandrovich__0cf7f9ceca/case-005/explicit_03_page_001.png` | fig. 1, 3, 6, 7 | figure | | `task3:vasin_artem_aleksandrovich__0cf7f9ceca:case-005` | `CASE-005` | `assets/images/vasin_artem_aleksandrovich__0cf7f9ceca/case-005/explicit_04_page_001.png` | fig. 1, 3, 6, 7 | figure | +## https://arxiv.org/abs/2404.12377 + +- Ссылка на статью: https://arxiv.org/abs/2404.12377 +- Название: RoboDreamer: Learning Compositional World Models for Robot Imagination +- Идентификаторы: arXiv:2404.12377 +- Использовано изображений/страниц: 4 + +| sample_id | case_id | image | page_hint | evidence_kind | +|---|---|---|---|---| +| `task3:6efae140d8:case-004` | `CASE-004` | `assets/images/6efae140d8/case-004/explicit_01_page_004.png` | Fig.4 (compositional rollouts по новым комбинациям объекта/действия) | figure | +| `task3:6efae140d8:case-004` | `CASE-004` | `assets/images/6efae140d8/case-004/explicit_02_page_004.png` | Fig.4 (compositional rollouts по новым комбинациям объекта/действия) | figure | +| `task3:6efae140d8:case-004` | `CASE-004` | `assets/images/6efae140d8/case-004/explicit_03_page_004.png` | Fig.4 (compositional rollouts по новым комбинациям объекта/действия) | figure | +| `task3:6efae140d8:case-004` | `CASE-004` | `assets/images/6efae140d8/case-004/explicit_04_page_004.png` | Fig.4 (compositional rollouts по новым комбинациям объекта/действия) | figure | + ## https://arxiv.org/abs/2405.00873 - Ссылка на статью: https://arxiv.org/abs/2405.00873 @@ -697,6 +781,20 @@ | `task3:vasin_artem_aleksandrovich__0cf7f9ceca:case-012` | `CASE-012` | `assets/images/vasin_artem_aleksandrovich__0cf7f9ceca/case-012/explicit_03_page_001.png` | Fig. 1 | figure | | `task3:vasin_artem_aleksandrovich__0cf7f9ceca:case-012` | `CASE-012` | `assets/images/vasin_artem_aleksandrovich__0cf7f9ceca/case-012/explicit_04_page_001.png` | Fig. 1 | figure | +## https://arxiv.org/abs/2412.14803 + +- Ссылка на статью: https://arxiv.org/abs/2412.14803 +- Название: Video Prediction Policy (VPP): Generalist Robot Policies with Predictive Visual Representations +- Идентификаторы: arXiv:2412.14803 +- Использовано изображений/страниц: 4 + +| sample_id | case_id | image | page_hint | evidence_kind | +|---|---|---|---|---| +| `task3:6efae140d8:case-002` | `CASE-002` | `assets/images/6efae140d8/case-002/explicit_01_page_002.png` | Fig.2 (predictive representation pipeline), Table 2 (manipulation benchmarks) | figure_or_table | +| `task3:6efae140d8:case-002` | `CASE-002` | `assets/images/6efae140d8/case-002/explicit_02_page_002.png` | Fig.2 (predictive representation pipeline), Table 2 (manipulation benchmarks) | figure_or_table | +| `task3:6efae140d8:case-002` | `CASE-002` | `assets/images/6efae140d8/case-002/explicit_03_page_002.png` | Fig.2 (predictive representation pipeline), Table 2 (manipulation benchmarks) | figure_or_table | +| `task3:6efae140d8:case-002` | `CASE-002` | `assets/images/6efae140d8/case-002/explicit_04_page_002.png` | Fig.2 (predictive representation pipeline), Table 2 (manipulation benchmarks) | figure_or_table | + ## https://arxiv.org/abs/2412.19444 - Ссылка на статью: https://arxiv.org/abs/2412.19444 @@ -781,6 +879,20 @@ | `task3:998d6715e8:case-008` | `CASE-008` | `assets/images/998d6715e8/case-008/explicit_03_page_001.png` | p. 17, Table 1 | table | | `task3:998d6715e8:case-008` | `CASE-008` | `assets/images/998d6715e8/case-008/explicit_04_page_001.png` | p. 17, Table 1 | table | +## https://arxiv.org/abs/2504.16680 + +- Ссылка на статью: https://arxiv.org/abs/2504.16680 +- Название: Robotic World Model with Uncertainty-aware Model-Based Reinforcement Learning (RWM-U) +- Идентификаторы: arXiv:2504.16680 +- Использовано изображений/страниц: 4 + +| sample_id | case_id | image | page_hint | evidence_kind | +|---|---|---|---|---| +| `task3:6efae140d8:case-011` | `CASE-011` | `assets/images/6efae140d8/case-011/explicit_01_page_000.png` | Введение и метод (text-dominant изложение) | page | +| `task3:6efae140d8:case-011` | `CASE-011` | `assets/images/6efae140d8/case-011/explicit_02_page_000.png` | Введение и метод (text-dominant изложение) | page | +| `task3:6efae140d8:case-011` | `CASE-011` | `assets/images/6efae140d8/case-011/explicit_03_page_000.png` | Введение и метод (text-dominant изложение) | page | +| `task3:6efae140d8:case-011` | `CASE-011` | `assets/images/6efae140d8/case-011/explicit_04_page_000.png` | Введение и метод (text-dominant изложение) | page | + ## https://arxiv.org/abs/2505.15952 - Ссылка на статью: https://arxiv.org/abs/2505.15952 @@ -795,6 +907,20 @@ | `task3:998d6715e8:case-007` | `CASE-007` | `assets/images/998d6715e8/case-007/explicit_03_page_002.png` | p.5, Table 2 | table | | `task3:998d6715e8:case-007` | `CASE-007` | `assets/images/998d6715e8/case-007/explicit_04_page_002.png` | p.5, Table 2 | table | +## https://arxiv.org/abs/2506.01182 + +- Ссылка на статью: https://arxiv.org/abs/2506.01182 +- Название: Humanoid World Models (HWM): Egocentric Humanoid Video Forecasting +- Идентификаторы: arXiv:2506.01182 +- Использовано изображений/страниц: 4 + +| sample_id | case_id | image | page_hint | evidence_kind | +|---|---|---|---|---| +| `task3:6efae140d8:case-012` | `CASE-012` | `assets/images/6efae140d8/case-012/explicit_01_page_000.png` | Abstract + section с описанием датасета egocentric humanoid video | page | +| `task3:6efae140d8:case-012` | `CASE-012` | `assets/images/6efae140d8/case-012/explicit_02_page_000.png` | Abstract + section с описанием датасета egocentric humanoid video | page | +| `task3:6efae140d8:case-012` | `CASE-012` | `assets/images/6efae140d8/case-012/explicit_03_page_000.png` | Abstract + section с описанием датасета egocentric humanoid video | page | +| `task3:6efae140d8:case-012` | `CASE-012` | `assets/images/6efae140d8/case-012/explicit_04_page_000.png` | Abstract + section с описанием датасета egocentric humanoid video | page | + ## https://arxiv.org/abs/2506.05079 - Ссылка на статью: https://arxiv.org/abs/2506.05079 @@ -823,6 +949,34 @@ | `task3:d0c71bbc62:case-011` | `CASE-011` | `assets/images/d0c71bbc62/case-011/explicit_03_page_014.png` | p.14, Fig.10 | figure | | `task3:d0c71bbc62:case-011` | `CASE-011` | `assets/images/d0c71bbc62/case-011/explicit_04_page_014.png` | p.14, Fig.10 | figure | +## https://arxiv.org/abs/2508.17600 + +- Ссылка на статью: https://arxiv.org/abs/2508.17600 +- Название: GWM: Gaussian World Models for Robotic Manipulation +- Идентификаторы: arXiv:2508.17600 +- Использовано изображений/страниц: 4 + +| sample_id | case_id | image | page_hint | evidence_kind | +|---|---|---|---|---| +| `task3:6efae140d8:case-006` | `CASE-006` | `assets/images/6efae140d8/case-006/explicit_01_page_003.png` | Fig.3 (3D Gaussian Splatting reconstruction + predicted future state) | figure | +| `task3:6efae140d8:case-006` | `CASE-006` | `assets/images/6efae140d8/case-006/explicit_02_page_003.png` | Fig.3 (3D Gaussian Splatting reconstruction + predicted future state) | figure | +| `task3:6efae140d8:case-006` | `CASE-006` | `assets/images/6efae140d8/case-006/explicit_03_page_003.png` | Fig.3 (3D Gaussian Splatting reconstruction + predicted future state) | figure | +| `task3:6efae140d8:case-006` | `CASE-006` | `assets/images/6efae140d8/case-006/explicit_04_page_003.png` | Fig.3 (3D Gaussian Splatting reconstruction + predicted future state) | figure | + +## https://arxiv.org/abs/2509.21797 + +- Ссылка на статью: https://arxiv.org/abs/2509.21797 +- Название: MoWM: Mixture-of-World-Models for Embodied Planning +- Идентификаторы: arXiv:2509.21797 +- Использовано изображений/страниц: 4 + +| sample_id | case_id | image | page_hint | evidence_kind | +|---|---|---|---|---| +| `task3:6efae140d8:case-005` | `CASE-005` | `assets/images/6efae140d8/case-005/explicit_01_page_012.png` | Fig.2 (pixel-space diffusion WM + latent WM fusion), Table 1 (planning benchmarks) | figure_or_table | +| `task3:6efae140d8:case-005` | `CASE-005` | `assets/images/6efae140d8/case-005/explicit_02_page_012.png` | Fig.2 (pixel-space diffusion WM + latent WM fusion), Table 1 (planning benchmarks) | figure_or_table | +| `task3:6efae140d8:case-005` | `CASE-005` | `assets/images/6efae140d8/case-005/explicit_03_page_001.png` | Fig.2 (pixel-space diffusion WM + latent WM fusion), Table 1 (planning benchmarks) | figure_or_table | +| `task3:6efae140d8:case-005` | `CASE-005` | `assets/images/6efae140d8/case-005/explicit_04_page_001.png` | Fig.2 (pixel-space diffusion WM + latent WM fusion), Table 1 (planning benchmarks) | figure_or_table | + ## https://arxiv.org/abs/2509.23166 - Ссылка на статью: https://arxiv.org/abs/2509.23166 @@ -887,6 +1041,20 @@ | `task3:7d0aad0635:case-002` | `CASE-002` | `assets/images/7d0aad0635/case-002/explicit_03_page_001.png` | p.4, Table 1 | table | | `task3:7d0aad0635:case-002` | `CASE-002` | `assets/images/7d0aad0635/case-002/explicit_04_page_001.png` | p.4, Table 1 | table | +## https://arxiv.org/abs/2602.15922 + +- Ссылка на статью: https://arxiv.org/abs/2602.15922 +- Название: World Action Models are Zero-shot Policies (DreamZero) +- Идентификаторы: arXiv:2602.15922 +- Использовано изображений/страниц: 4 + +| sample_id | case_id | image | page_hint | evidence_kind | +|---|---|---|---|---| +| `task3:6efae140d8:case-001` | `CASE-001` | `assets/images/6efae140d8/case-001/explicit_01_page_013.png` | Fig.1 (system overview), Fig.3 (joint future-frame + action trajectories) | figure | +| `task3:6efae140d8:case-001` | `CASE-001` | `assets/images/6efae140d8/case-001/explicit_02_page_013.png` | Fig.1 (system overview), Fig.3 (joint future-frame + action trajectories) | figure | +| `task3:6efae140d8:case-001` | `CASE-001` | `assets/images/6efae140d8/case-001/explicit_03_page_001.png` | Fig.1 (system overview), Fig.3 (joint future-frame + action trajectories) | figure | +| `task3:6efae140d8:case-001` | `CASE-001` | `assets/images/6efae140d8/case-001/explicit_04_page_001.png` | Fig.1 (system overview), Fig.3 (joint future-frame + action trajectories) | figure | + ## https://arxiv.org/abs/2603.11531 - Ссылка на статью: https://arxiv.org/abs/2603.11531 @@ -957,6 +1125,20 @@ | `task3:0987c43afc:case-017` | `CASE-017` | `assets/images/0987c43afc/case-017/explicit_03_page_000.png` | p. 12, 3.1, Eq 1 | page | | `task3:0987c43afc:case-017` | `CASE-017` | `assets/images/0987c43afc/case-017/explicit_04_page_000.png` | p. 12, 3.1, Eq 1 | page | +## https://arxiv.org/abs/2603.19312 + +- Ссылка на статью: https://arxiv.org/abs/2603.19312 +- Название: LeWorldModel (LeWM): JEPA-style Latent World Model from Pixels +- Идентификаторы: arXiv:2603.19312 +- Использовано изображений/страниц: 4 + +| sample_id | case_id | image | page_hint | evidence_kind | +|---|---|---|---|---| +| `task3:6efae140d8:case-007` | `CASE-007` | `assets/images/6efae140d8/case-007/explicit_01_page_012.png` | Fig.1 (two-term JEPA objective), Table 2 (planning speed vs. foundation-model WMs) | figure_or_table | +| `task3:6efae140d8:case-007` | `CASE-007` | `assets/images/6efae140d8/case-007/explicit_02_page_012.png` | Fig.1 (two-term JEPA objective), Table 2 (planning speed vs. foundation-model WMs) | figure_or_table | +| `task3:6efae140d8:case-007` | `CASE-007` | `assets/images/6efae140d8/case-007/explicit_03_page_001.png` | Fig.1 (two-term JEPA objective), Table 2 (planning speed vs. foundation-model WMs) | figure_or_table | +| `task3:6efae140d8:case-007` | `CASE-007` | `assets/images/6efae140d8/case-007/explicit_04_page_001.png` | Fig.1 (two-term JEPA objective), Table 2 (planning speed vs. foundation-model WMs) | figure_or_table | + ## https://arxiv.org/abs/2603.27498 - Ссылка на статью: https://arxiv.org/abs/2603.27498 @@ -2353,6 +2535,62 @@ | `task3:168bdb55d7:case-002` | `CASE-002` | `assets/images/168bdb55d7/case-002/explicit_03_page_000.png` | p.12 | figure | | `task3:168bdb55d7:case-002` | `CASE-002` | `assets/images/168bdb55d7/case-002/explicit_04_page_000.png` | p.12 | figure | +## https://doi.org/10.1145/177492.177575 + +- Ссылка на статью: https://doi.org/10.1145/177492.177575 +- Название: Improvements to Graph Coloring Register Allocation +- Идентификаторы: doi:10.1145/177492.177575 +- Использовано изображений/страниц: 32 + +| sample_id | case_id | image | page_hint | evidence_kind | +|---|---|---|---|---| +| `task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-001` | `CASE-001` | `assets/images/vinarskii-aleksandr-anatolievich__7a574f0525/case-001/explicit_01_page_002.png` | p. 432, Fig. 2 | figure | +| `task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-001` | `CASE-001` | `assets/images/vinarskii-aleksandr-anatolievich__7a574f0525/case-001/explicit_02_page_002.png` | p. 432, Fig. 2 | figure | +| `task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-001` | `CASE-001` | `assets/images/vinarskii-aleksandr-anatolievich__7a574f0525/case-001/explicit_03_page_002.png` | p. 432, Fig. 2 | figure | +| `task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-001` | `CASE-001` | `assets/images/vinarskii-aleksandr-anatolievich__7a574f0525/case-001/explicit_04_page_002.png` | p. 432, Fig. 2 | figure | +| `task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-002` | `CASE-002` | `assets/images/vinarskii-aleksandr-anatolievich__7a574f0525/case-002/explicit_01_page_004.png` | p. 436, Fig. 4 | figure | +| `task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-002` | `CASE-002` | `assets/images/vinarskii-aleksandr-anatolievich__7a574f0525/case-002/explicit_02_page_004.png` | p. 436, Fig. 4 | figure | +| `task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-002` | `CASE-002` | `assets/images/vinarskii-aleksandr-anatolievich__7a574f0525/case-002/explicit_03_page_004.png` | p. 436, Fig. 4 | figure | +| `task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-002` | `CASE-002` | `assets/images/vinarskii-aleksandr-anatolievich__7a574f0525/case-002/explicit_04_page_004.png` | p. 436, Fig. 4 | figure | +| `task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-003` | `CASE-003` | `assets/images/vinarskii-aleksandr-anatolievich__7a574f0525/case-003/explicit_01_page_005.png` | p. 438, Fig. 5 | figure | +| `task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-003` | `CASE-003` | `assets/images/vinarskii-aleksandr-anatolievich__7a574f0525/case-003/explicit_02_page_005.png` | p. 438, Fig. 5 | figure | +| `task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-003` | `CASE-003` | `assets/images/vinarskii-aleksandr-anatolievich__7a574f0525/case-003/explicit_03_page_005.png` | p. 438, Fig. 5 | figure | +| `task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-003` | `CASE-003` | `assets/images/vinarskii-aleksandr-anatolievich__7a574f0525/case-003/explicit_04_page_005.png` | p. 438, Fig. 5 | figure | +| `task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-004` | `CASE-004` | `assets/images/vinarskii-aleksandr-anatolievich__7a574f0525/case-004/explicit_01_page_006.png` | p. 440, Fig. 6 | figure | +| `task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-004` | `CASE-004` | `assets/images/vinarskii-aleksandr-anatolievich__7a574f0525/case-004/explicit_02_page_006.png` | p. 440, Fig. 6 | figure | +| `task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-004` | `CASE-004` | `assets/images/vinarskii-aleksandr-anatolievich__7a574f0525/case-004/explicit_03_page_016.png` | p. 440, Fig. 6 | figure | +| `task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-004` | `CASE-004` | `assets/images/vinarskii-aleksandr-anatolievich__7a574f0525/case-004/explicit_04_page_016.png` | p. 440, Fig. 6 | figure | +| `task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-005` | `CASE-005` | `assets/images/vinarskii-aleksandr-anatolievich__7a574f0525/case-005/explicit_01_page_000.png` | p. 446, Table I | table | +| `task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-005` | `CASE-005` | `assets/images/vinarskii-aleksandr-anatolievich__7a574f0525/case-005/explicit_02_page_000.png` | p. 446, Table I | table | +| `task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-005` | `CASE-005` | `assets/images/vinarskii-aleksandr-anatolievich__7a574f0525/case-005/explicit_03_page_000.png` | p. 446, Table I | table | +| `task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-005` | `CASE-005` | `assets/images/vinarskii-aleksandr-anatolievich__7a574f0525/case-005/explicit_04_page_000.png` | p. 446, Table I | table | +| `task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-006` | `CASE-006` | `assets/images/vinarskii-aleksandr-anatolievich__7a574f0525/case-006/explicit_01_page_000.png` | p. 446, Table II | table | +| `task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-006` | `CASE-006` | `assets/images/vinarskii-aleksandr-anatolievich__7a574f0525/case-006/explicit_02_page_000.png` | p. 446, Table II | table | +| `task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-006` | `CASE-006` | `assets/images/vinarskii-aleksandr-anatolievich__7a574f0525/case-006/explicit_03_page_000.png` | p. 446, Table II | table | +| `task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-006` | `CASE-006` | `assets/images/vinarskii-aleksandr-anatolievich__7a574f0525/case-006/explicit_04_page_000.png` | p. 446, Table II | table | +| `task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-009` | `CASE-009` | `assets/images/vinarskii-aleksandr-anatolievich__7a574f0525/case-009/explicit_01_page_000.png` | p. 448, Table III | table | +| `task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-009` | `CASE-009` | `assets/images/vinarskii-aleksandr-anatolievich__7a574f0525/case-009/explicit_02_page_000.png` | p. 448, Table III | table | +| `task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-009` | `CASE-009` | `assets/images/vinarskii-aleksandr-anatolievich__7a574f0525/case-009/explicit_03_page_000.png` | p. 448, Table III | table | +| `task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-009` | `CASE-009` | `assets/images/vinarskii-aleksandr-anatolievich__7a574f0525/case-009/explicit_04_page_000.png` | p. 448, Table III | table | +| `task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-011` | `CASE-011` | `assets/images/vinarskii-aleksandr-anatolievich__7a574f0525/case-011/explicit_01_page_000.png` | p. 428, abstract | page | +| `task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-011` | `CASE-011` | `assets/images/vinarskii-aleksandr-anatolievich__7a574f0525/case-011/explicit_02_page_000.png` | p. 428, abstract | page | +| `task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-011` | `CASE-011` | `assets/images/vinarskii-aleksandr-anatolievich__7a574f0525/case-011/explicit_03_page_000.png` | p. 428, abstract | page | +| `task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-011` | `CASE-011` | `assets/images/vinarskii-aleksandr-anatolievich__7a574f0525/case-011/explicit_04_page_000.png` | p. 428, abstract | page | + +## https://doi.org/10.1145/2402.322385 + +- Ссылка на статью: https://doi.org/10.1145/2402.322385 +- Название: Smallest-Last Ordering and Clustering and Graph Coloring Algorithms +- Идентификаторы: doi:10.1145/2402.322385 +- Использовано изображений/страниц: 4 + +| sample_id | case_id | image | page_hint | evidence_kind | +|---|---|---|---|---| +| `task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-010` | `CASE-010` | `assets/images/vinarskii-aleksandr-anatolievich__7a574f0525/case-010/explicit_01_page_000.png` | target paper pp. 434-435, discussion of Matula and Beck | page | +| `task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-010` | `CASE-010` | `assets/images/vinarskii-aleksandr-anatolievich__7a574f0525/case-010/explicit_02_page_000.png` | target paper pp. 434-435, discussion of Matula and Beck | page | +| `task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-010` | `CASE-010` | `assets/images/vinarskii-aleksandr-anatolievich__7a574f0525/case-010/explicit_03_page_000.png` | target paper pp. 434-435, discussion of Matula and Beck | page | +| `task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-010` | `CASE-010` | `assets/images/vinarskii-aleksandr-anatolievich__7a574f0525/case-010/explicit_04_page_000.png` | target paper pp. 434-435, discussion of Matula and Beck | page | + ## https://doi.org/10.1145/800135.804426 - Ссылка на статью: https://doi.org/10.1145/800135.804426 @@ -2367,6 +2605,20 @@ | `task3:168bdb55d7:case-004` | `CASE-004` | `assets/images/168bdb55d7/case-004/explicit_03_page_000.png` | | figure_or_table | | `task3:168bdb55d7:case-004` | `CASE-004` | `assets/images/168bdb55d7/case-004/explicit_04_page_000.png` | | figure_or_table | +## https://doi.org/10.1145/800230.806984 + +- Ссылка на статью: https://doi.org/10.1145/800230.806984 +- Название: Register Allocation and Spilling via Graph Coloring +- Идентификаторы: doi:10.1145/800230.806984 +- Использовано изображений/страниц: 4 + +| sample_id | case_id | image | page_hint | evidence_kind | +|---|---|---|---|---| +| `task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-008` | `CASE-008` | `assets/images/vinarskii-aleksandr-anatolievich__7a574f0525/case-008/explicit_01_page_000.png` | target paper p. 429, related work / Chaitin 1982 | page | +| `task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-008` | `CASE-008` | `assets/images/vinarskii-aleksandr-anatolievich__7a574f0525/case-008/explicit_02_page_000.png` | target paper p. 429, related work / Chaitin 1982 | page | +| `task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-008` | `CASE-008` | `assets/images/vinarskii-aleksandr-anatolievich__7a574f0525/case-008/explicit_03_page_000.png` | target paper p. 429, related work / Chaitin 1982 | page | +| `task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-008` | `CASE-008` | `assets/images/vinarskii-aleksandr-anatolievich__7a574f0525/case-008/explicit_04_page_000.png` | target paper p. 429, related work / Chaitin 1982 | page | + ## https://doi.org/10.1152/ajpheart.00794.2003 - Ссылка на статью: https://doi.org/10.1152/ajpheart.00794.2003 @@ -2539,6 +2791,20 @@ | `task3:168bdb55d7:case-005` | `CASE-005` | `assets/images/168bdb55d7/case-005/explicit_03_page_002.png` | Page 6, Figure 3; especially panels 3b–3c. Page 4, Figure 2 is a useful secondary reference for reaction-coordinate quality. | figure | | `task3:168bdb55d7:case-005` | `CASE-005` | `assets/images/168bdb55d7/case-005/explicit_04_page_002.png` | Page 6, Figure 3; especially panels 3b–3c. Page 4, Figure 2 is a useful secondary reference for reaction-coordinate quality. | figure | +## https://doi.org/10.21046/2070-7401-2022-19-1-107-122 + +- Ссылка на статью: https://doi.org/10.21046/2070-7401-2022-19-1-107-122 +- Название: Дистанционное зондирование. Методы и модели и методы обработки изображений +- Идентификаторы: 10.21046/2070-7401-2022-19-1-107-122 +- Использовано изображений/страниц: 4 + +| sample_id | case_id | image | page_hint | evidence_kind | +|---|---|---|---|---| +| `task3:9b54231119:case-002` | `CASE-002` | `assets/images/9b54231119/case-002/explicit_01_page_001.png` | p.2, Table 1 | table | +| `task3:9b54231119:case-002` | `CASE-002` | `assets/images/9b54231119/case-002/explicit_02_page_001.png` | p.2, Table 1 | table | +| `task3:9b54231119:case-002` | `CASE-002` | `assets/images/9b54231119/case-002/explicit_03_page_001.png` | p.2, Table 1 | table | +| `task3:9b54231119:case-002` | `CASE-002` | `assets/images/9b54231119/case-002/explicit_04_page_001.png` | p.2, Table 1 | table | + ## https://doi.org/10.2139/ssrn.4084713 - Ссылка на статью: https://doi.org/10.2139/ssrn.4084713 @@ -2575,6 +2841,20 @@ | `task3:5ad3fe3294:case-010` | `CASE-010` | `assets/images/5ad3fe3294/case-010/explicit_03_page_002.png` | Table 2 | table | | `task3:5ad3fe3294:case-010` | `CASE-010` | `assets/images/5ad3fe3294/case-010/explicit_04_page_002.png` | Table 2 | table | +## https://doi.org/10.25209/2079-3316-2018-9-4-417-442 + +- Ссылка на статью: https://doi.org/10.25209/2079-3316-2018-9-4-417-442 +- Название: Modern methods for intelligent processing of Earth remote sensing data +- Идентификаторы: 10.25209/2079-3316-2018-9-4-417-442 +- Использовано изображений/страниц: 4 + +| sample_id | case_id | image | page_hint | evidence_kind | +|---|---|---|---|---| +| `task3:9b54231119:case-001` | `CASE-001` | `assets/images/9b54231119/case-001/explicit_01_page_001.png` | p.1 | page | +| `task3:9b54231119:case-001` | `CASE-001` | `assets/images/9b54231119/case-001/explicit_02_page_001.png` | p.1 | page | +| `task3:9b54231119:case-001` | `CASE-001` | `assets/images/9b54231119/case-001/explicit_03_page_001.png` | p.1 | page | +| `task3:9b54231119:case-001` | `CASE-001` | `assets/images/9b54231119/case-001/explicit_04_page_001.png` | p.1 | page | + ## https://doi.org/10.3389/fmars.2021.736486 - Ссылка на статью: https://doi.org/10.3389/fmars.2021.736486 @@ -2719,6 +2999,20 @@ | `task3:cherbaev__iris_retina_modern_ab:case-003` | `CASE-003` | `assets/images/cherbaev__iris_retina_modern_ab/case-003/explicit_03_page_000.png` | Раздел Results, таблица сравнения IoU/Dice на CASIA-v4, ND-IRIS и Cross-Eyed; качественные примеры сегментации (figure) | figure_or_table | | `task3:cherbaev__iris_retina_modern_ab:case-003` | `CASE-003` | `assets/images/cherbaev__iris_retina_modern_ab/case-003/explicit_04_page_000.png` | Раздел Results, таблица сравнения IoU/Dice на CASIA-v4, ND-IRIS и Cross-Eyed; качественные примеры сегментации (figure) | figure_or_table | +## https://doi.org/10.34759/trd-2021-119-18 + +- Ссылка на статью: https://doi.org/10.34759/trd-2021-119-18 +- Название: Методика ранжирования космических аппаратов дистанционного зондирования Земли с целью оперативного мониторинга чрезвычайных ситуаций +- Идентификаторы: 10.34759/trd-2021-119-18 +- Использовано изображений/страниц: 4 + +| sample_id | case_id | image | page_hint | evidence_kind | +|---|---|---|---|---| +| `task3:9b54231119:case-004` | `CASE-004` | `assets/images/9b54231119/case-004/explicit_01_page_005.png` | p.5 | mixed | +| `task3:9b54231119:case-004` | `CASE-004` | `assets/images/9b54231119/case-004/explicit_02_page_005.png` | p.5 | mixed | +| `task3:9b54231119:case-004` | `CASE-004` | `assets/images/9b54231119/case-004/explicit_03_page_005.png` | p.5 | mixed | +| `task3:9b54231119:case-004` | `CASE-004` | `assets/images/9b54231119/case-004/explicit_04_page_005.png` | p.5 | mixed | + ## https://doi.org/10.36253/Substantia-2441 - Ссылка на статью: https://doi.org/10.36253/Substantia-2441 @@ -2761,6 +3055,20 @@ | `task3:168bdb55d7:case-019` | `CASE-019` | `assets/images/168bdb55d7/case-019/explicit_03_page_001.png` | Table 1 | table | | `task3:168bdb55d7:case-019` | `CASE-019` | `assets/images/168bdb55d7/case-019/explicit_04_page_001.png` | Table 1 | table | +## https://doi.org/10.47813/2782-2818-2023-3-3-0301-0310 + +- Ссылка на статью: https://doi.org/10.47813/2782-2818-2023-3-3-0301-0310 +- Название: Методы дистанционного зондирования земли в лесной промышленности +- Идентификаторы: 10.47813/2782-2818-2023-3-3-0301-0310 +- Использовано изображений/страниц: 4 + +| sample_id | case_id | image | page_hint | evidence_kind | +|---|---|---|---|---| +| `task3:9b54231119:case-005` | `CASE-005` | `assets/images/9b54231119/case-005/explicit_01_page_005.png` | p.7, Fig.5 | figure | +| `task3:9b54231119:case-005` | `CASE-005` | `assets/images/9b54231119/case-005/explicit_02_page_005.png` | p.7, Fig.5 | figure | +| `task3:9b54231119:case-005` | `CASE-005` | `assets/images/9b54231119/case-005/explicit_03_page_005.png` | p.7, Fig.5 | figure | +| `task3:9b54231119:case-005` | `CASE-005` | `assets/images/9b54231119/case-005/explicit_04_page_005.png` | p.7, Fig.5 | figure | + ## https://doi.org/10.7717/peerj.13152 - Ссылка на статью: https://doi.org/10.7717/peerj.13152 diff --git a/README.md b/README.md index 25f5e1d6193fd69ec6c28fec75a75e2477a26e11..e017a402c35cd09e14fcd014a1a36c4e5aaac338 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: `2026-05-28T17:36:47Z`. +Дата генерации README: `2026-05-28T19:45:17Z`. ## 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: **345**. Строк с изображениями: **345**. Всего ссылок на изображения: **1380**. +Строк в текущей сборке: **372**. Строк с изображениями: **372**. Всего ссылок на изображения: **1488**. ## 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: **386**. +Строк: **413**. ### `data/task3_cases_summary.csv` @@ -65,18 +65,18 @@ CSV-сводка по строкам генерационного датасет ## Распределение по слоям сложности -- `easy_control`: 55 -- `multimodal_hard`: 202 -- `temporal_hard`: 88 +- `easy_control`: 59 +- `multimodal_hard`: 220 +- `temporal_hard`: 93 ## Распределение по типам evidence -- `figure`: 139 -- `figure_or_table`: 92 +- `figure`: 147 +- `figure_or_table`: 99 - `formula`: 9 -- `mixed`: 22 -- `page`: 37 -- `table`: 40 +- `mixed`: 23 +- `page`: 43 +- `table`: 45 - `text_claim`: 6 ## Как читать датасет diff --git a/article_image_sources.jsonl b/article_image_sources.jsonl index defd70d2d3c27d5341f4a8266cae45a31bc05a30..b4b4068a1ed5b47272ab6ff262de545f43c36bb1 100644 --- a/article_image_sources.jsonl +++ b/article_image_sources.jsonl @@ -1,3 +1,3 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@@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9f907e21042d5da940eba464cc3a2a672c52087ae5aa87660f5b5836c0bacb89 +size 360884 diff --git a/data/task3_cases_flat.jsonl b/data/task3_cases_flat.jsonl index bb4ca48523e1918ee548fce7c124b4d9ad60645d..baa60dfd44a0f4e41c4b5ab819ac879ee2e8c38c 100644 --- a/data/task3_cases_flat.jsonl +++ b/data/task3_cases_flat.jsonl @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:7f678cf98bd745725c695656212a30b24c38eee35c23f7292385af1e2e41bad3 -size 426980 +oid sha256:b05fda8d6ca4d344bb0739e9d178022bc6811e69c2beae4fd43e130300bf30e8 +size 460119 diff --git a/data/task3_cases_summary.csv b/data/task3_cases_summary.csv index df5c3dd12d71715ecc5b8c3bbe6242d1b3d2232d..a67ddaf2fdba89389ef1b7b8eef3b04062189aa0 100644 --- a/data/task3_cases_summary.csv +++ b/data/task3_cases_summary.csv @@ -332,6 +332,33 @@ task3:b74df7ada6:case-009,Nucleotide modification for insreasing nuclease stabil task3:b74df7ada6:case-010,Nucleotide modification for insreasing nuclease stability,троилина-мария-владимировна__b74df7ada6,CASE-010,temporal_hard,True,Tissue pharmacokinetics of antisense oligonucleotides,10.1016/j.omtn.2024.102133,2024,table,Table 1,4,"Сравните, какой вариант точнее извлекает из таблицы оценочные фармакокинетические параметры (Cmax, Tmax, t1/2, AUC) и параметры knockdown Malat1 (Emax, TE50, длительность эффекта) для каждой комбинации: [формуляция × путь введения × ткань], корректно ориентируется в иерархической структуре таблицы (верхняя/средняя/нижняя секции) и сохраняет единицы измерения и доверительные интервалы." task3:b74df7ada6:case-011,Nucleotide modification for insreasing nuclease stability,троилина-мария-владимировна__b74df7ada6,CASE-011,easy_control,False,Phosphorothioate modified oligonucleotide–protein interactions,10.1093/nar/gkaa299,2020,page,p.2,4,"Сравните, какой вариант точнее извлекает из текста параметры связывания PS ASO с белками плазмы: значения констант диссоциации (Kd), концентрации белков в плазме и процент связывания (>95%), корректно сопоставляет каждый параметр с соответствующим белком (альбумин, α2-макроглобулин) и сохраняет единицы измерения (μM, %)." task3:b74df7ada6:case-012,Nucleotide modification for insreasing nuclease stability,троилина-мария-владимировна__b74df7ada6,CASE-012,easy_control,False,Phosphorothioate modified oligonucleotide–protein interactions,10.1093/nar/gkaa299,2020,page,,4,"Сравните, какой вариант точнее извлекает из текста информацию о внутриклеточных белках, взаимодействующих с PS ASO: корректно определяет роль каждого белка (связывание, транспорт, модуляция активности), классифицирует эффект на активность (усиливает / ингибирует / нет эффекта) и сохраняет контекст механизма (эндоцитоз, выход из эндосом, ядерная локализация, конкуренция с RNase H1)." +task3:6efae140d8:case-001,"World models for robotic manipulation and control: latent, generative video, world-action, and simulator-based approaches",медведев-александр-игоревич__6efae140d8,CASE-001,multimodal_hard,True,World Action Models are Zero-shot Policies (DreamZero),arXiv:2602.15922,2026,figure,"Fig.1 (system overview), Fig.3 (joint future-frame + action trajectories)",4,"Оцените, сохраняет ли вариант связь между каждым предсказанным action chunk и соответствующим future frame в совместной 14B image-to-video диффузионной модели, не сводя её к чистому video predictor." +task3:6efae140d8:case-002,"World models for robotic manipulation and control: latent, generative video, world-action, and simulator-based approaches",медведев-александр-игоревич__6efae140d8,CASE-002,multimodal_hard,True,Video Prediction Policy (VPP): Generalist Robot Policies with Predictive Visual Representations,arXiv:2412.14803,2024,figure_or_table,"Fig.2 (predictive representation pipeline), Table 2 (manipulation benchmarks)",4,"Оцените, точно ли вариант идентифицирует, что conditioning для policy — это predictive visual representations (intermediate latents из video diffusion), а не сгенерированные RGB кадры." +task3:6efae140d8:case-003,"World models for robotic manipulation and control: latent, generative video, world-action, and simulator-based approaches",медведев-александр-игоревич__6efae140d8,CASE-003,multimodal_hard,True,GR-1: Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation,arXiv:2312.13139,2023,figure_or_table,"Fig.1 (GPT-style future image + action prediction), Table 3 (CALVIN/manipulation results)",4,"Оцените, корректно ли вариант приписывает прирост на CALVIN-бенчмарке именно ablation по video pretraining (как видно из таблицы), а не общей GPT-style архитектуре GR-1." +task3:6efae140d8:case-004,"World models for robotic manipulation and control: latent, generative video, world-action, and simulator-based approaches",медведев-александр-игоревич__6efae140d8,CASE-004,multimodal_hard,True,RoboDreamer: Learning Compositional World Models for Robot Imagination,arXiv:2404.12377,2024,figure,Fig.4 (compositional rollouts по новым комбинациям объекта/действия),4,"Оцените, идентифицирует ли вариант compositional generalization на новых object-action парах по figure rollouts, а не общее improvement в video prediction quality." +task3:6efae140d8:case-005,"World models for robotic manipulation and control: latent, generative video, world-action, and simulator-based approaches",медведев-александр-игоревич__6efae140d8,CASE-005,multimodal_hard,True,MoWM: Mixture-of-World-Models for Embodied Planning,arXiv:2509.21797,2025,figure_or_table,"Fig.2 (pixel-space diffusion WM + latent WM fusion), Table 1 (planning benchmarks)",4,"Оцените, разделяет ли вариант вклад двух потоков MoWM (pixel-space diffusion WM и latent WM) через ablation в таблице, не сводя все улучшения к одному из потоков." +task3:6efae140d8:case-006,"World models for robotic manipulation and control: latent, generative video, world-action, and simulator-based approaches",медведев-александр-игоревич__6efae140d8,CASE-006,multimodal_hard,True,GWM: Gaussian World Models for Robotic Manipulation,arXiv:2508.17600,2025,figure,Fig.3 (3D Gaussian Splatting reconstruction + predicted future state),4,"Оцените, фиксирует ли вариант, что future state предсказывается в 3D Gaussian Splatting представлении на основе figure с 3D scene-level rendering, а не в 2D RGB пикселях." +task3:6efae140d8:case-007,"World models for robotic manipulation and control: latent, generative video, world-action, and simulator-based approaches",медведев-александр-игоревич__6efae140d8,CASE-007,multimodal_hard,True,LeWorldModel (LeWM): JEPA-style Latent World Model from Pixels,arXiv:2603.19312,2026,figure_or_table,"Fig.1 (two-term JEPA objective), Table 2 (planning speed vs. foundation-model WMs)",4,"Оцените, объясняет ли вариант, что LeWM обучается из пикселей через two-term JEPA objective без decoder и pixel-reconstruction loss, в отличие от Dreamer-style WMs." +task3:6efae140d8:case-008,"World models for robotic manipulation and control: latent, generative video, world-action, and simulator-based approaches",медведев-александр-игоревич__6efae140d8,CASE-008,temporal_hard,True,DreamerV3: Mastering Diverse Domains through World Models,arXiv:2301.04104,2023,figure_or_table,"Fig.1 (single hyperparam set across 150+ tasks), Fig.6 (long-horizon imagined rollouts)",4,"Оцените, точно ли вариант передаёт, что DreamerV3 решает 150+ задач одним фиксированным набором гиперпараметров одновременно, а не последовательным per-domain tuning-ом." +task3:6efae140d8:case-009,"World models for robotic manipulation and control: latent, generative video, world-action, and simulator-based approaches",медведев-александр-игоревич__6efae140d8,CASE-009,temporal_hard,True,DayDreamer: World Models for Physical Robot Learning,arXiv:2206.14176,2022,table,Table 1 (часы реального wall-clock training до целевого поведения на quadruped/arm/wheeled),4,"Оцените, передаёт ли вариант конкретные wall-clock часы physical training на реальном роботе из таблицы, а не сводит всё к общему 'works on real robots'." +task3:6efae140d8:case-010,"World models for robotic manipulation and control: latent, generative video, world-action, and simulator-based approaches",медведев-александр-игоревич__6efae140d8,CASE-010,temporal_hard,True,"TD-MPC2: Scalable, Robust World Models for Continuous Control",arXiv:2310.16828,2023,figure_or_table,"Fig.4 (scaling curves: return vs. model size/steps), Table 3 (multi-task settings)",4,"Оцените, интерпретирует ли вариант scaling curves TD-MPC2 именно как функцию training-time (шагов и размера модели), а не как общее 'better performance with scale'." +task3:6efae140d8:case-011,"World models for robotic manipulation and control: latent, generative video, world-action, and simulator-based approaches",медведев-александр-игоревич__6efae140d8,CASE-011,easy_control,False,Robotic World Model with Uncertainty-aware Model-Based Reinforcement Learning (RWM-U),arXiv:2504.16680,2025,page,Введение и метод (text-dominant изложение),4,"Кратко оцените, корректно ли вариант передаёт основную идею: learned neural simulator + uncertainty-aware offline MBRL, без специфической привязки к figures." +task3:6efae140d8:case-012,"World models for robotic manipulation and control: latent, generative video, world-action, and simulator-based approaches",медведев-александр-игоревич__6efae140d8,CASE-012,easy_control,False,Humanoid World Models (HWM): Egocentric Humanoid Video Forecasting,arXiv:2506.01182,2025,page,Abstract + section с описанием датасета egocentric humanoid video,4,"Кратко сравните, как варианты формулируют scope HWM: egocentric humanoid video forecasting как отдельная подзадача world modeling, без углубления в конкретные figure-level metrics." +task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-001,"Compiler register allocation: graph coloring, optimistic coloring, rematerialization",vinarskii-aleksandr-anatolievich__7a574f0525,CASE-001,multimodal_hard,True,Improvements to Graph Coloring Register Allocation,doi:10.1145/177492.177575,1994,figure,"p. 432, Fig. 2",4,"Сравните, какой вариант лучше объясняет Fig. 2 с diamond graph: эвристика Chaitin приводит к spill, хотя двухцветная раскраска существует, а более сильная идея раскраски помогает избежать лишнего spill." +task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-002,"Compiler register allocation: graph coloring, optimistic coloring, rematerialization",vinarskii-aleksandr-anatolievich__7a574f0525,CASE-002,multimodal_hard,True,Improvements to Graph Coloring Register Allocation,doi:10.1145/177492.177575,1994,figure,"p. 436, Fig. 4",4,"Сравните, какой вариант лучше использует Fig. 4 для объяснения optimistic allocator: кандидат на spill сначала кладётся в стек на этапе simplify, а реальное добавление spill code откладывается до этапа селект, если цвет найти не удалось." +task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-003,"Compiler register allocation: graph coloring, optimistic coloring, rematerialization",vinarskii-aleksandr-anatolievich__7a574f0525,CASE-003,multimodal_hard,True,Improvements to Graph Coloring Register Allocation,doi:10.1145/177492.177575,1994,figure,"p. 438, Fig. 5",4,"Сравните, какой вариант лучше объясняет Fig. 5: при обычном spilling значение p сохраняется и загружается из памяти, а при rematerialization его можно заново вычислить дешёвой инструкцией, например load-immediate." +task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-004,"Compiler register allocation: graph coloring, optimistic coloring, rematerialization",vinarskii-aleksandr-anatolievich__7a574f0525,CASE-004,multimodal_hard,True,Improvements to Graph Coloring Register Allocation,doi:10.1145/177492.177575,1994,figure,"p. 440, Fig. 6",4,"Сравните, какой вариант лучше объясняет Fig. 6: SSA-разбиение позволяет отделить разные значения внутри одного live range, проставить rematerialization tags и оставить только минимально нужные split/copy операции." +task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-005,"Compiler register allocation: graph coloring, optimistic coloring, rematerialization",vinarskii-aleksandr-anatolievich__7a574f0525,CASE-005,multimodal_hard,True,Improvements to Graph Coloring Register Allocation,doi:10.1145/177492.177575,1994,table,"p. 446, Table I",4,"Извлеките из Table I экспериментальный результат optimistic coloring: в каких случаях spill costs уменьшаются, какие типы инструкций дают вклад и почему это подтверждает пользу optimistic coloring.." +task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-006,"Compiler register allocation: graph coloring, optimistic coloring, rematerialization",vinarskii-aleksandr-anatolievich__7a574f0525,CASE-006,multimodal_hard,True,Improvements to Graph Coloring Register Allocation,doi:10.1145/177492.177575,1994,table,"p. 446, Table II",4,"Извлеките из Table II результат rematerialization: улучшенная rematerialization уменьшает spill costs, обычно снижает число loads/stores и заменяет часть memory reload на более дешёвые load-immediate операции.." +task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-008,"Compiler register allocation: graph coloring, optimistic coloring, rematerialization",vinarskii-aleksandr-anatolievich__7a574f0525,CASE-008,temporal_hard,True,Register Allocation and Spilling via Graph Coloring,doi:10.1145/800230.806984,1982,page,"target paper p. 429, related work / Chaitin 1982",4,"Сравните, какой вариант лучше фиксирует временную связь: статья Chaitin 1982 предшествует Briggs et al. 1994 и задаёт базовый Chaitin-style graph-coloring allocator, который затем улучшается через optimistic coloring и rematerialization." +task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-009,"Compiler register allocation: graph coloring, optimistic coloring, rematerialization",vinarskii-aleksandr-anatolievich__7a574f0525,CASE-009,multimodal_hard,True,Improvements to Graph Coloring Register Allocation,doi:10.1145/177492.177575,1994,table,"p. 448, Table III",4,"Извлеките из Table III данные о стоимости allocation time: optimistic coloring почти не увеличивает время компиляции, а поддержка rematerialization может добавлять небольшой compile-time overhead.." +task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-010,"Compiler register allocation: graph coloring, optimistic coloring, rematerialization",vinarskii-aleksandr-anatolievich__7a574f0525,CASE-010,temporal_hard,True,Smallest-Last Ordering and Clustering and Graph Coloring Algorithms,doi:10.1145/2402.322385,1983,page,"target paper pp. 434-435, discussion of Matula and Beck",4,"Сравните, какой вариант лучше объясняет временную и идейную связь: Matula–Beck 1983 предшествует Briggs et al. 1994 и даёт более сильную эвристику graph coloring, которая помогает понять идею optimistic coloring." +task3:vinarskii-aleksandr-anatolievich__7a574f0525:case-011,"Compiler register allocation: graph coloring, optimistic coloring, rematerialization",vinarskii-aleksandr-anatolievich__7a574f0525,CASE-011,easy_control,False,Improvements to Graph Coloring Register Allocation,doi:10.1145/177492.177575,1994,page,"p. 428, abstract",4,"Извлеките простой основной тезис статьи: Briggs, Cooper и Torczon предлагают два улучшения Chaitin-style graph-coloring register allocation — optimistic coloring и расширенную rematerialization.." +task3:9b54231119:case-001,Дистанционное зондирование Земли,тимофеев-кирилл-анатольевич__9b54231119,CASE-001,easy_control,True,Modern methods for intelligent processing of Earth remote sensing data,10.25209/2079-3316-2018-9-4-417-442,2018,page,p.1,4,"Нужно сравнить кто лучше извлекает ключевые идеи, задачи и методы ДЗЗ" +task3:9b54231119:case-002,Дистанционное зондирование Земли,тимофеев-кирилл-анатольевич__9b54231119,CASE-002,multimodal_hard,True,Дистанционное зондирование. Методы и модели и методы обработки изображений,10.21046/2070-7401-2022-19-1-107-122,2018,table,"p.2, Table 1",4,"Сравни, какой вариант четче отражает задачи и методологии ДЗЗ" +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: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 index 37f0aeddb4b301736f7906f818b976335715e420..e8e2266b7800497cc8e19f3273a2c4cf53309486 100644 --- a/data/task3_vlm_generation.jsonl +++ b/data/task3_vlm_generation.jsonl @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:92b421c88dc14c67563e3bca5e186e107e30ce5ea75a5b04a3855709538b9a24 -size 2365790 +oid sha256:ea110b5554e8d25aa17a276bdf06e04a841765716b9f591b7439dafe6db90e3c +size 2555313 diff --git a/metadata/build_summary.json b/metadata/build_summary.json index 834da116b6ccad22ffdf4d96ff86b3791a6ab058..67e5ac62365134c95c008fe22b3ec85d0559c143 100644 --- a/metadata/build_summary.json +++ b/metadata/build_summary.json @@ -1,5 +1,5 @@ { - "generated_at": "2026-05-28T17:36:44Z", + "generated_at": "2026-05-28T19:45:09Z", "dataset_repo_id": "top-papers/top-papers-graph-benchmark", "dataset_config_name": "task3_vlm_generation", "split_name": "test", @@ -39,6 +39,9 @@ "/content/task3_hf_work/selected_task3_inputs/0032_timofeeva_marina_pavlovna__task3_ab_case_manifest.filled (1) - Марина.json", "/content/task3_hf_work/selected_task3_inputs/0033_smaznova_khristina_timofeevna__task3_ab_case_manifest.filled (3) - Иван Васильевич.json", "/content/task3_hf_work/selected_task3_inputs/0034_troilina_mariia_vladimirovna__task3_ab_case_manifest.filled - Мария Троилина.json", + "/content/task3_hf_work/selected_task3_inputs/0035_medvedev_aleksandr_igorevich__task3_ab_case_manifest.filled - Alex.json", + "/content/task3_hf_work/selected_task3_inputs/0036_vinarskii_aleksandr_anatol_evich__3task3_ab_case_manifest.filled - Александр Винарский.json", + "/content/task3_hf_work/selected_task3_inputs/0037_timofeev_kirill_anatol_evich__task3_ab_case_manifest.filled - Kirill Timofeeff.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", @@ -112,14 +115,14 @@ "/content/top_papers_graph_benchmark_task3/_work/extracted_archives/0014_kolesnikov_ivan_vital_evich__-ex-xidad__894e4444d90a/aerospace-12-00655.pdf", "/content/top_papers_graph_benchmark_task3/_work/extracted_archives/0014_kolesnikov_ivan_vital_evich__-ex-xidad__894e4444d90a/electronics-11-02468 (1).pdf" ], - "other_files_discovered": 87, - "manifests_discovered": 36, - "cases_total": 386, - "cases_written": 345, + "other_files_discovered": 84, + "manifests_discovered": 39, + "cases_total": 413, + "cases_written": 372, "cases_skipped_disabled": 0, "cases_skipped_incomplete": 41, - "cases_with_images": 345, - "explicit_images_copied": 1380, + "cases_with_images": 372, + "explicit_images_copied": 1488, "rendered_pages": 0, "pdfs_discovered": 4, "pdfs_downloaded": 0, diff --git a/review_metadata/task3_case_rationales.jsonl b/review_metadata/task3_case_rationales.jsonl index 639c19169cf76e990aab1a3467e18f16fa27fe29..f28bc7c995d8d8bed93a81bd516ba25802696313 100644 --- a/review_metadata/task3_case_rationales.jsonl +++ b/review_metadata/task3_case_rationales.jsonl @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:6feee6bea780c704a4f5d85fe369992085cf8c44c8c7f8635b2da29673f15a29 -size 712262 +oid sha256:0fb6cadd53162a74425b5e7473380952b336213abc4fef78773c312cb7aa731f +size 768802 diff --git a/source_manifests/6efae140d8__0035_medvedev_aleksandr_igorevich__task3_ab_case_manifest.filled - Alex.json b/source_manifests/6efae140d8__0035_medvedev_aleksandr_igorevich__task3_ab_case_manifest.filled - Alex.json new file mode 100644 index 0000000000000000000000000000000000000000..ec77857330632dffc72c3eecc162b72934a31fa1 --- /dev/null +++ b/source_manifests/6efae140d8__0035_medvedev_aleksandr_igorevich__task3_ab_case_manifest.filled - Alex.json @@ -0,0 +1,412 @@ +{ + "schema_version": "task3_ab_case_manifest_v1", + "generated_at": "2026-05-24T14:52:53Z", + "experiment_meta": { + "topic": "World models for robotic manipulation and control: latent, generative video, world-action, and simulator-based approaches", + "submission_id": "медведев-александр-игоревич__6efae140d8", + "creator_id": "Медведев Александр Игоревич", + "cutoff_year": "2026", + "review_goal": "Surface differences between baseline VLM and SFT/DPO VLM on Task 3 hypothesis generation for world-models-for-robotics papers. Hard subset stresses figure/table evidence linkage (latent/generative/3D/world-action) and temporal grounding (long-horizon imagined rollouts, wall-clock physical training, training-step scaling)." + }, + "strata_targets": { + "multimodal_hard": 7, + "temporal_hard": 3, + "easy_control": 2 + }, + "error_taxonomy": [ + "missed_visual_fact", + "wrong_evidence_linkage", + "needs_time_fix", + "hallucinated_visual_inference" + ], + "cases": [ + { + "case_id": "CASE-001", + "enabled": true, + "primary_endpoint": true, + "stratum": "multimodal_hard", + "paper_title": "World Action Models are Zero-shot Policies (DreamZero)", + "paper_id": "arXiv:2602.15922", + "year": "2026", + "evidence_kind": "figure", + "page_hint": "Fig.1 (system overview), Fig.3 (joint future-frame + action trajectories)", + "creator_prompt": "Оцените, сохраняет ли вариант связь между каждым предсказанным action chunk и соответствующим future frame в совместной 14B image-to-video диффузионной модели, не сводя её к чистому video predictor.", + "creator_rationale": "Ключевой вклад WAM/DreamZero — единая 14B image-to-video diffusion модель, которая одновременно предсказывает actions и future frames. Baseline VLM часто описывает её только как video predictor и теряет связь между predicted frame и соответствующим action, а tuned VLM должен сохранить именно эту совместную evidence linkage.", + "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": "world action model", + "premise_contains": "image-to-video diffusion backbone predicts actions and future visual states", + "mechanism_contains": "", + "time_scope_contains": "", + "rank_hint": "" + }, + "notes": "Frontier-paper; arXiv ID на момент составления не зафиксирован, эксперт-участник проверяет по landing page." + }, + { + "case_id": "CASE-002", + "enabled": true, + "primary_endpoint": true, + "stratum": "multimodal_hard", + "paper_title": "Video Prediction Policy (VPP): Generalist Robot Policies with Predictive Visual Representations", + "paper_id": "arXiv:2412.14803", + "year": "2024", + "evidence_kind": "figure_or_table", + "page_hint": "Fig.2 (predictive representation pipeline), Table 2 (manipulation benchmarks)", + "creator_prompt": "Оцените, точно ли вариант идентифицирует, что conditioning для policy — это predictive visual representations (intermediate latents из video diffusion), а не сгенерированные RGB кадры.", + "creator_rationale": "Главная диагностическая трудность — отличить 'video as policy input' от 'video features as policy conditioning'. Baseline VLM часто описывает VPP как генератор видео, а не как extractor of predictive representations, и теряет figure-level evidence про то, какие именно слои/latents подаются в policy head.", + "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": "video prediction policy", + "premise_contains": "predictive visual representations from video diffusion as policy conditioning", + "mechanism_contains": "", + "time_scope_contains": "", + "rank_hint": "" + }, + "notes": "Ключевая разница с GR-1 и WAM — predictive features vs. generated frames as action signal." + }, + { + "case_id": "CASE-003", + "enabled": true, + "primary_endpoint": true, + "stratum": "multimodal_hard", + "paper_title": "GR-1: Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation", + "paper_id": "arXiv:2312.13139", + "year": "2023", + "evidence_kind": "figure_or_table", + "page_hint": "Fig.1 (GPT-style future image + action prediction), Table 3 (CALVIN/manipulation results)", + "creator_prompt": "Оцените, корректно ли вариант приписывает прирост на CALVIN-бенчмарке именно ablation по video pretraining (как видно из таблицы), а не общей GPT-style архитектуре GR-1.", + "creator_rationale": "GR-1 — каноничный кейс table-driven evidence: ключевой вклад (video pretraining → manipulation generalization) видно только из ablation таблиц. Baseline VLM часто пропускает столбцы с/без video pretraining и неверно атрибутирует gain трансформер-архитектуре, а не pretraining.", + "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": "manipulation generalization", + "hypothesis_title_contains": "video generative pre-training", + "premise_contains": "GPT-style future frame and action prediction", + "mechanism_contains": "", + "time_scope_contains": "", + "rank_hint": "" + }, + "notes": "Ablation table — главный источник правильной evidence linkage." + }, + { + "case_id": "CASE-004", + "enabled": true, + "primary_endpoint": true, + "stratum": "multimodal_hard", + "paper_title": "RoboDreamer: Learning Compositional World Models for Robot Imagination", + "paper_id": "arXiv:2404.12377", + "year": "2024", + "evidence_kind": "figure", + "page_hint": "Fig.4 (compositional rollouts по новым комбинациям объекта/действия)", + "creator_prompt": "Оцените, идентифицирует ли вариант compositional generalization на новых object-action парах по figure rollouts, а не общее improvement в video prediction quality.", + "creator_rationale": "Compositional claim сидит в figure: на конкретных object-action парах, которых не было в обучении, RoboDreamer сохраняет связную динамику. Baseline VLM часто галлюцинирует более общий 'video quality improvement' visual inference и теряет точную compositional привязку.", + "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": "compositional world model", + "premise_contains": "", + "mechanism_contains": "compositional decomposition into object and action factors", + "time_scope_contains": "", + "rank_hint": "" + }, + "notes": "Высокий риск hallucinated_visual_inference на красивых длинных rollouts." + }, + { + "case_id": "CASE-005", + "enabled": true, + "primary_endpoint": true, + "stratum": "multimodal_hard", + "paper_title": "MoWM: Mixture-of-World-Models for Embodied Planning", + "paper_id": "arXiv:2509.21797", + "year": "2025", + "evidence_kind": "figure_or_table", + "page_hint": "Fig.2 (pixel-space diffusion WM + latent WM fusion), Table 1 (planning benchmarks)", + "creator_prompt": "Оцените, разделяет ли вариант вклад двух потоков MoWM (pixel-space diffusion WM и latent WM) через ablation в таблице, не сводя все улучшения к одному из потоков.", + "creator_rationale": "Главный риск — wrong evidence linkage: baseline VLM часто сводит mixture к 'просто более сильному WM' и пропускает ablation, где выключение одного потока меняет конкретные метрики по-разному. Это именно multimodal-hard кейс: evidence распределён между figure pipeline и ablation table.", + "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": "mixture of world models", + "premise_contains": "fused pixel-space diffusion world model and latent world model for action planning", + "mechanism_contains": "", + "time_scope_contains": "", + "rank_hint": "" + }, + "notes": "Проверить, что эксперт не путает с MMWM; правильный термин — MoWM." + }, + { + "case_id": "CASE-006", + "enabled": true, + "primary_endpoint": true, + "stratum": "multimodal_hard", + "paper_title": "GWM: Gaussian World Models for Robotic Manipulation", + "paper_id": "arXiv:2508.17600", + "year": "2025", + "evidence_kind": "figure", + "page_hint": "Fig.3 (3D Gaussian Splatting reconstruction + predicted future state)", + "creator_prompt": "Оцените, фиксирует ли вариант, что future state предсказывается в 3D Gaussian Splatting представлении на основе figure с 3D scene-level rendering, а не в 2D RGB пикселях.", + "creator_rationale": "GWM — единственная в подборке статья с 3D scene-level WM. Baseline VLM часто описывает её как 'обычный pixel-space video WM', теряя ключевую визуальную evidence про 3DGS-представление. Это типичный missed_visual_fact: 3D-факт виден только из figure.", + "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": "gaussian world model", + "premise_contains": "", + "mechanism_contains": "3D Gaussian Splatting representation for future state prediction", + "time_scope_contains": "", + "rank_hint": "" + }, + "notes": "3D evidence невозможно подтвердить только из абстракта; обязательна фигура." + }, + { + "case_id": "CASE-007", + "enabled": true, + "primary_endpoint": true, + "stratum": "multimodal_hard", + "paper_title": "LeWorldModel (LeWM): JEPA-style Latent World Model from Pixels", + "paper_id": "arXiv:2603.19312", + "year": "2026", + "evidence_kind": "figure_or_table", + "page_hint": "Fig.1 (two-term JEPA objective), Table 2 (planning speed vs. foundation-model WMs)", + "creator_prompt": "Оцените, объясняет ли вариант, что LeWM обучается из пикселей через two-term JEPA objective без decoder и pixel-reconstruction loss, в отличие от Dreamer-style WMs.", + "creator_rationale": "Главное отличие LeWM от Dreamer-style WMs — отсутствие decoder и reconstruction loss; это сидит в figure с objective и в speed-таблице. Baseline VLM часто атрибутирует выигрыш 'просто JEPA-объективу' и пропускает планирование-скорость как отдельный visual fact.", + "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": "faster planning than foundation-model world models", + "hypothesis_title_contains": "latent world model", + "premise_contains": "JEPA-style two-term objective without pixel reconstruction", + "mechanism_contains": "", + "time_scope_contains": "", + "rank_hint": "" + }, + "notes": "Latent/JEPA-line; не путать с TD-MPC2 decoder-free latent MPC." + }, + { + "case_id": "CASE-008", + "enabled": true, + "primary_endpoint": true, + "stratum": "temporal_hard", + "paper_title": "DreamerV3: Mastering Diverse Domains through World Models", + "paper_id": "arXiv:2301.04104", + "year": "2023", + "evidence_kind": "figure_or_table", + "page_hint": "Fig.1 (single hyperparam set across 150+ tasks), Fig.6 (long-horizon imagined rollouts)", + "creator_prompt": "Оцените, точно ли вариант передаёт, что DreamerV3 решает 150+ задач одним фиксированным набором гиперпараметров одновременно, а не последовательным per-domain tuning-ом.", + "creator_rationale": "Temporal grounding здесь двойной: (1) длина imagined rollout-а; (2) cross-domain 'один и тот же набор гиперпараметров одновременно'. Baseline VLM часто теряет смысл 'одновременно' и описывает Dreamer как post-hoc оптимизированный per-domain, что меняет интерпретацию вклада.", + "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": "dreamerv3", + "premise_contains": "single fixed hyperparameter set", + "mechanism_contains": "", + "time_scope_contains": "long-horizon imagined rollouts in latent space", + "rank_hint": "" + }, + "notes": "Высокий риск needs_time_fix на формулировке 'одновременно vs. последовательно tuned'." + }, + { + "case_id": "CASE-009", + "enabled": true, + "primary_endpoint": true, + "stratum": "temporal_hard", + "paper_title": "DayDreamer: World Models for Physical Robot Learning", + "paper_id": "arXiv:2206.14176", + "year": "2022", + "evidence_kind": "table", + "page_hint": "Table 1 (часы реального wall-clock training до целевого поведения на quadruped/arm/wheeled)", + "creator_prompt": "Оцените, передаёт ли вариант конкретные wall-clock часы physical training на реальном роботе из таблицы, а не сводит всё к общему 'works on real robots'.", + "creator_rationale": "Главный диагностический сигнал — wall-clock временные рамки и сравнение с simulator-based RL по сходимости. Baseline VLM часто теряет числа часов и платформу, что превращает temporal claim в обтекаемое 'efficient on hardware'.", + "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": "daydreamer", + "premise_contains": "", + "mechanism_contains": "", + "time_scope_contains": "wall-clock hours of physical robot training without simulator", + "rank_hint": "" + }, + "notes": "Easy для unaware modeler — но именно потому temporal precision тут diagnostic." + }, + { + "case_id": "CASE-010", + "enabled": true, + "primary_endpoint": true, + "stratum": "temporal_hard", + "paper_title": "TD-MPC2: Scalable, Robust World Models for Continuous Control", + "paper_id": "arXiv:2310.16828", + "year": "2023", + "evidence_kind": "figure_or_table", + "page_hint": "Fig.4 (scaling curves: return vs. model size/steps), Table 3 (multi-task settings)", + "creator_prompt": "Оцените, интерпретирует ли вариант scaling curves TD-MPC2 именно как функцию training-time (шагов и размера модели), а не как общее 'better performance with scale'.", + "creator_rationale": "Здесь temporal — это training-time scaling и многозадачное обучение как функция шагов. Baseline VLM часто описывает scaling как просто 'улучшается с размером', теряя ось 'шаги/часы' и факт decoder-free latent MPC vs. Dreamer-style generative reconstruction.", + "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": "td-mpc2", + "premise_contains": "decoder-free latent planning with TD learning", + "mechanism_contains": "", + "time_scope_contains": "scaling with training steps across continuous control tasks", + "rank_hint": "" + }, + "notes": "Контраст-кейс для LeWM (тоже latent, но JEPA-line)." + }, + { + "case_id": "CASE-011", + "enabled": true, + "primary_endpoint": false, + "stratum": "easy_control", + "paper_title": "Robotic World Model with Uncertainty-aware Model-Based Reinforcement Learning (RWM-U)", + "paper_id": "arXiv:2504.16680", + "year": "2025", + "evidence_kind": "page", + "page_hint": "Введение и метод (text-dominant изложение)", + "creator_prompt": "Кратко оцените, корректно ли вариант передаёт основную идею: learned neural simulator + uncertainty-aware offline MBRL, без специфической привязки к figures.", + "creator_rationale": "Контрольный кейс: ключевые claims статьи изложены в тексте и не зависят от figure/table evidence. Ожидается, что baseline и tuned VLM здесь близки — это и есть смысл easy_control в наборе.", + "review_focus": [ + "overall" + ], + "expected_error_modes": [], + "match": { + "candidate_signature": "", + "candidate_source_contains": "", + "candidate_predicate_contains": "", + "candidate_target_contains": "", + "hypothesis_title_contains": "robotic world model", + "premise_contains": "uncertainty-aware offline model-based reinforcement learning", + "mechanism_contains": "", + "time_scope_contains": "", + "rank_hint": "" + }, + "notes": "Сбалансирует набор: показывает, что tuned VLM не выигрывает 'всегда по стилю'." + }, + { + "case_id": "CASE-012", + "enabled": true, + "primary_endpoint": false, + "stratum": "easy_control", + "paper_title": "Humanoid World Models (HWM): Egocentric Humanoid Video Forecasting", + "paper_id": "arXiv:2506.01182", + "year": "2025", + "evidence_kind": "page", + "page_hint": "Abstract + section с описанием датасета egocentric humanoid video", + "creator_prompt": "Кратко сравните, как варианты формулируют scope HWM: egocentric humanoid video forecasting как отдельная подзадача world modeling, без углубления в конкретные figure-level metrics.", + "creator_rationale": "Контрольный кейс рядом с GR00T-line: текстовая декларация scope. Здесь от baseline и tuned VLM ожидается похожий уровень, а не drastic difference — именно поэтому это easy_control в наборе.", + "review_focus": [ + "overall" + ], + "expected_error_modes": [], + "match": { + "candidate_signature": "", + "candidate_source_contains": "", + "candidate_predicate_contains": "", + "candidate_target_contains": "", + "hypothesis_title_contains": "humanoid world model", + "premise_contains": "egocentric humanoid video forecasting", + "mechanism_contains": "", + "time_scope_contains": "", + "rank_hint": "" + }, + "notes": "Если позже найдётся sharp figure-level claim, можно переквалифицировать в multimodal_hard." + } + ] +} \ No newline at end of file diff --git a/source_manifests/9b54231119__0037_timofeev_kirill_anatol_evich__task3_ab_case_manifest.filled - Kirill Timofeeff.json b/source_manifests/9b54231119__0037_timofeev_kirill_anatol_evich__task3_ab_case_manifest.filled - Kirill Timofeeff.json new file mode 100644 index 0000000000000000000000000000000000000000..d794c6b7ed6a5234c3fa1294c2d4bf46fe1f9ef3 --- /dev/null +++ b/source_manifests/9b54231119__0037_timofeev_kirill_anatol_evich__task3_ab_case_manifest.filled - Kirill Timofeeff.json @@ -0,0 +1,195 @@ +{ + "schema_version": "task3_ab_case_manifest_v1", + "generated_at": "2026-05-23T11:09:47Z", + "experiment_meta": { + "topic": "Дистанционное зондирование Земли", + "submission_id": "тимофеев-кирилл-анатольевич__9b54231119", + "creator_id": "Тимофеев Кирилл Анатольевич", + "cutoff_year": "2016", + "review_goal": "Surface VLM differences on multimodal-hard and temporal-hard Task 3 cases." + }, + "strata_targets": { + "multimodal_hard": 7, + "temporal_hard": 3, + "easy_control": 2 + }, + "error_taxonomy": [ + "missed_visual_fact", + "wrong_evidence_linkage", + "needs_time_fix", + "hallucinated_visual_inference" + ], + "cases": [ + { + "case_id": "CASE-001", + "enabled": true, + "primary_endpoint": true, + "stratum": "easy_control", + "paper_title": "Modern methods for intelligent processing of Earth remote sensing data", + "paper_id": "10.25209/2079-3316-2018-9-4-417-442", + "year": "2018", + "evidence_kind": "page", + "page_hint": "p.1", + "creator_prompt": "Нужно сравнить кто лучше извлекает ключевые идеи, задачи и методы ДЗЗ", + "creator_rationale": "Ищи в тексте главные идеи, не распространяйся лишним текстом", + "review_focus": [ + "overall", + "evidence" + ], + "expected_error_modes": [ + "wrong_evidence_linkage" + ], + "match": { + "candidate_signature": "target", + "candidate_source_contains": "", + "candidate_predicate_contains": "", + "candidate_target_contains": "", + "hypothesis_title_contains": "ДЗЗ", + "premise_contains": "", + "mechanism_contains": "", + "time_scope_contains": "", + "rank_hint": "" + }, + "notes": "" + }, + { + "case_id": "CASE-002", + "enabled": true, + "primary_endpoint": true, + "stratum": "multimodal_hard", + "paper_title": "Дистанционное зондирование. Методы и модели и методы обработки изображений", + "paper_id": "10.21046/2070-7401-2022-19-1-107-122", + "year": "2018", + "evidence_kind": "table", + "page_hint": "p.2, Table 1", + "creator_prompt": "Сравни, какой вариант четче отражает задачи и методологии ДЗЗ", + "creator_rationale": "Смотри в левой колонке класс задачи ДЗЗ, а в правой уточненный класс и его методологии", + "review_focus": [ + "evidence", + "visual_fact", + "overall" + ], + "expected_error_modes": [ + "missed_visual_fact", + "wrong_evidence_linkage" + ], + "match": { + "candidate_signature": "predicate", + "candidate_source_contains": "", + "candidate_predicate_contains": "", + "candidate_target_contains": "", + "hypothesis_title_contains": "", + "premise_contains": "", + "mechanism_contains": "", + "time_scope_contains": "", + "rank_hint": "" + }, + "notes": "" + }, + { + "case_id": "CASE-003", + "enabled": true, + "primary_endpoint": true, + "stratum": "multimodal_hard", + "paper_title": "Дистанционное зондирование. Модели и методы обработки изображений.", + "paper_id": "978-5-94836-244-1", + "year": "2020", + "evidence_kind": "figure_or_table", + "page_hint": "p.20, Fig.1.1", + "creator_prompt": "Оцени количество найденных моделей, количество и качество сопоставленных с моделями пар значений количества спектральных каналов и интервалов дискретизации", + "creator_rationale": "Обрати внимание на метки на диаграмме, на текстовые метки (Текстовая метка может быть выше, ниже, правее, левее или вынесена сноской относительно точки метки на полотне диаграммы), также обрати внимание на оси координат диаграммы, где по горизонтали (X) - интервалы дискретизации, а по вертикали - количество спектральных каналов.", + "review_focus": [ + "evidence", + "visual_fact", + "overall" + ], + "expected_error_modes": [ + "missed_visual_fact", + "wrong_evidence_linkage", + "hallucinated_visual_inference" + ], + "match": { + "candidate_signature": "source", + "candidate_source_contains": "", + "candidate_predicate_contains": "", + "candidate_target_contains": "", + "hypothesis_title_contains": "", + "premise_contains": "", + "mechanism_contains": "", + "time_scope_contains": "", + "rank_hint": "" + }, + "notes": "" + }, + { + "case_id": "CASE-004", + "enabled": true, + "primary_endpoint": true, + "stratum": "multimodal_hard", + "paper_title": "Методика ранжирования космических аппаратов дистанционного зондирования Земли с целью оперативного мониторинга чрезвычайных ситуаций ", + "paper_id": "10.34759/trd-2021-119-18", + "year": "2021", + "evidence_kind": "mixed", + "page_hint": "p.5", + "creator_prompt": "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", + "creator_rationale": "Нужно составить план математического анализа расчета вклада КА в исследование лесных массивов. Распознать формулы и понять как они связаны.", + "review_focus": [ + "evidence", + "visual_fact", + "overall" + ], + "expected_error_modes": [ + "missed_visual_fact", + "wrong_evidence_linkage", + "hallucinated_visual_inference" + ], + "match": { + "candidate_signature": "predicate", + "candidate_source_contains": "", + "candidate_predicate_contains": "", + "candidate_target_contains": "", + "hypothesis_title_contains": "", + "premise_contains": "", + "mechanism_contains": "", + "time_scope_contains": "", + "rank_hint": "" + }, + "notes": "" + }, + { + "case_id": "CASE-005", + "enabled": true, + "primary_endpoint": true, + "stratum": "multimodal_hard", + "paper_title": "Методы дистанционного зондирования земли в лесной промышленности", + "paper_id": "10.47813/2782-2818-2023-3-3-0301-0310", + "year": "2023", + "evidence_kind": "figure", + "page_hint": "p.7, Fig.5", + "creator_prompt": "Сравни понимание схемы работы системы \"Лесоохранитель\"", + "creator_rationale": "Обрати внимание на связи блок схемы работы системы, найди наиболее значимые элементы системы.", + "review_focus": [ + "evidence", + "visual_fact", + "overall" + ], + "expected_error_modes": [ + "missed_visual_fact", + "wrong_evidence_linkage", + "hallucinated_visual_inference" + ], + "match": { + "candidate_signature": "source", + "candidate_source_contains": "", + "candidate_predicate_contains": "", + "candidate_target_contains": "", + "hypothesis_title_contains": "", + "premise_contains": "", + "mechanism_contains": "", + "time_scope_contains": "", + "rank_hint": "" + }, + "notes": "" + } + ] +} \ No newline at end of file diff --git "a/source_manifests/vinarskii-aleksandr-anatolievich__7a574f0525__0036_vinarskii_aleksandr_anatol_evich__3task3_ab_case_manifest.filled - \320\220\320\273\320\265\320\272\321\201\320\260\320\275\320\264\321\200 \320\222\320\270\320\275\320\260\321\200\321\201\320\272\320\270\320\271.json" "b/source_manifests/vinarskii-aleksandr-anatolievich__7a574f0525__0036_vinarskii_aleksandr_anatol_evich__3task3_ab_case_manifest.filled - \320\220\320\273\320\265\320\272\321\201\320\260\320\275\320\264\321\200 \320\222\320\270\320\275\320\260\321\200\321\201\320\272\320\270\320\271.json" new file mode 100644 index 0000000000000000000000000000000000000000..f77fcc9866c7bbeeaefc8b13b3362f419a605ba6 --- /dev/null +++ "b/source_manifests/vinarskii-aleksandr-anatolievich__7a574f0525__0036_vinarskii_aleksandr_anatol_evich__3task3_ab_case_manifest.filled - \320\220\320\273\320\265\320\272\321\201\320\260\320\275\320\264\321\200 \320\222\320\270\320\275\320\260\321\200\321\201\320\272\320\270\320\271.json" @@ -0,0 +1,350 @@ +{ + "schema_version": "task3_ab_case_manifest_v1", + "generated_at": "2026-05-24T17:37:59Z", + "experiment_meta": { + "topic": "Compiler register allocation: graph coloring, optimistic coloring, rematerialization", + "submission_id": "vinarskii-aleksandr-anatolievich__7a574f0525", + "creator_id": "Vinarskii Aleksandr Anatolievich", + "cutoff_year": "1994", + "review_goal": "Surface VLM differences on multimodal evidence extraction, evidence linkage, and temporal grounding for compiler register-allocation papers." + }, + "strata_targets": { + "multimodal_hard": 7, + "temporal_hard": 3, + "easy_control": 1 + }, + "error_taxonomy": [ + "missed_visual_fact", + "wrong_evidence_linkage", + "needs_time_fix", + "hallucinated_visual_inference" + ], + "cases": [ + { + "case_id": "CASE-001", + "enabled": true, + "primary_endpoint": true, + "stratum": "multimodal_hard", + "paper_title": "Improvements to Graph Coloring Register Allocation", + "paper_id": "doi:10.1145/177492.177575", + "year": "1994", + "evidence_kind": "figure", + "page_hint": "p. 432, Fig. 2", + "creator_prompt": "Сравните, какой вариант лучше объясняет Fig. 2 с diamond graph: эвристика Chaitin приводит к spill, хотя двухцветная раскраска существует, а более сильная идея раскраски помогает избежать лишнего spill.", + "creator_rationale": "Это диагностический визуальный кейс: ключевое доказательство находится не только в тексте, но и в структуре графа на Fig. 2. Слабая VLM может не понять, почему diamond graph раскрашиваем, и не связать этот пример с мотивацией optimistic coloring.", + "review_focus": [ + "evidence", + "visual_fact" + ], + "expected_error_modes": [ + "missed_visual_fact", + "wrong_evidence_linkage" + ], + "match": { + "candidate_signature": "", + "candidate_source_contains": "optimistic coloring", + "candidate_predicate_contains": "reduces", + "candidate_target_contains": "spill", + "hypothesis_title_contains": "diamond graph", + "premise_contains": "", + "mechanism_contains": "", + "time_scope_contains": "", + "rank_hint": "" + }, + "notes": "" + }, + { + "case_id": "CASE-002", + "enabled": true, + "primary_endpoint": true, + "stratum": "multimodal_hard", + "paper_title": "Improvements to Graph Coloring Register Allocation", + "paper_id": "doi:10.1145/177492.177575", + "year": "1994", + "evidence_kind": "figure", + "page_hint": "p. 436, Fig. 4", + "creator_prompt": "Сравните, какой вариант лучше использует Fig. 4 для объяснения optimistic allocator: кандидат на spill сначала кладётся в стек на этапе simplify, а реальное добавление spill code откладывается до этапа селект, если цвет найти не удалось.", + "creator_rationale": "Это диагностический кейс по алгоритмической схеме: нужно прочитать pipeline на рисунке и связать его с главным механизмом статьи. Слабая VLM может написать про optimistic coloring слишком обще и пропустить, что ключевой перенос решения о spill происходит после select.", + "review_focus": [ + "evidence", + "visual_fact" + ], + "expected_error_modes": [ + "missed_visual_fact", + "wrong_evidence_linkage" + ], + "match": { + "candidate_signature": "", + "candidate_source_contains": "optimistic allocator", + "candidate_predicate_contains": "delays", + "candidate_target_contains": "spill code", + "hypothesis_title_contains": "", + "premise_contains": "", + "mechanism_contains": "simplify select", + "time_scope_contains": "", + "rank_hint": "" + }, + "notes": "" + }, + { + "case_id": "CASE-003", + "enabled": true, + "primary_endpoint": true, + "stratum": "multimodal_hard", + "paper_title": "Improvements to Graph Coloring Register Allocation", + "paper_id": "doi:10.1145/177492.177575", + "year": "1994", + "evidence_kind": "figure", + "page_hint": "p. 438, Fig. 5", + "creator_prompt": "Сравните, какой вариант лучше объясняет Fig. 5: при обычном spilling значение p сохраняется и загружается из памяти, а при rematerialization его можно заново вычислить дешёвой инструкцией, например load-immediate.", + "creator_rationale": "Это диагностический визуальный кейс: нужно сопоставить несколько колонок рисунка — Source, Ideal, Chaitin и Splitting — и понять, почему rematerialization лучше обычного store/reload для некоторых значений. Слабая VLM может описать spilling общо, но не увидеть различие между reload из памяти и recomputation.", + "review_focus": [ + "evidence", + "visual_fact" + ], + "expected_error_modes": [ + "missed_visual_fact", + "wrong_evidence_linkage" + ], + "match": { + "candidate_signature": "", + "candidate_source_contains": "rematerialization", + "candidate_predicate_contains": "reduces", + "candidate_target_contains": "spill", + "hypothesis_title_contains": "", + "premise_contains": "", + "mechanism_contains": "recompute", + "time_scope_contains": "", + "rank_hint": "" + }, + "notes": "" + }, + { + "case_id": "CASE-004", + "enabled": true, + "primary_endpoint": true, + "stratum": "multimodal_hard", + "paper_title": "Improvements to Graph Coloring Register Allocation", + "paper_id": "doi:10.1145/177492.177575", + "year": "1994", + "evidence_kind": "figure", + "page_hint": "p. 440, Fig. 6", + "creator_prompt": "Сравните, какой вариант лучше объясняет Fig. 6: SSA-разбиение позволяет отделить разные значения внутри одного live range, проставить rematerialization tags и оставить только минимально нужные split/copy операции.", + "creator_rationale": "Это диагностический визуальный кейс: нужно понять переход между колонками Source, SSA, Splits и Minimal. Слабая VLM может сказать только, что используется SSA, но не объяснить, зачем нужны split-операции и почему минимальный вариант лучше.", + "review_focus": [ + "evidence", + "visual_fact" + ], + "expected_error_modes": [ + "missed_visual_fact", + "wrong_evidence_linkage" + ], + "match": { + "candidate_signature": "", + "candidate_source_contains": "rematerialization", + "candidate_predicate_contains": "extends", + "candidate_target_contains": "multivalued live ranges", + "hypothesis_title_contains": "", + "premise_contains": "", + "mechanism_contains": "SSA splits", + "time_scope_contains": "", + "rank_hint": "" + }, + "notes": "" + }, + { + "case_id": "CASE-005", + "enabled": true, + "primary_endpoint": true, + "stratum": "multimodal_hard", + "paper_title": "Improvements to Graph Coloring Register Allocation", + "paper_id": "doi:10.1145/177492.177575", + "year": "1994", + "evidence_kind": "table", + "page_hint": "p. 446, Table I", + "creator_prompt": "Сравните, какой вариант лучше извлекает из Table I экспериментальный результат optimistic coloring: в каких случаях spill costs уменьшаются, какие типы инструкций дают вклад и почему это подтверждает пользу optimistic coloring.", + "creator_rationale": "Это диагностический табличный кейс: важное evidence находится в числах и колонках Table I, а не только в тексте. Слабая VLM может сказать, что optimistic coloring полезен, но не связать это с конкретными измерениями spill costs и вкладом load/store/copy инструкций.", + "review_focus": [ + "evidence", + "visual_fact" + ], + "expected_error_modes": [ + "missed_visual_fact", + "wrong_evidence_linkage" + ], + "match": { + "candidate_signature": "", + "candidate_source_contains": "optimistic coloring", + "candidate_predicate_contains": "reduces", + "candidate_target_contains": "spill costs", + "hypothesis_title_contains": "", + "premise_contains": "", + "mechanism_contains": "Table I", + "time_scope_contains": "", + "rank_hint": "" + }, + "notes": "" + }, + { + "case_id": "CASE-006", + "enabled": true, + "primary_endpoint": true, + "stratum": "multimodal_hard", + "paper_title": "Improvements to Graph Coloring Register Allocation", + "paper_id": "doi:10.1145/177492.177575", + "year": "1994", + "evidence_kind": "table", + "page_hint": "p. 446, Table II", + "creator_prompt": "Сравните, какой вариант лучше извлекает из Table II результат rematerialization: улучшенная rematerialization уменьшает spill costs, обычно снижает число loads/stores и заменяет часть memory reload на более дешёвые load-immediate операции.", + "creator_rationale": "Это диагностический табличный кейс: нужно не просто пересказать, что rematerialization полезна, а связать вывод с конкретной таблицей и типами инструкций. Слабая VLM может потерять числовое evidence или неправильно объяснить, за счёт чего уменьшается стоимость spilling.", + "review_focus": [ + "evidence", + "visual_fact" + ], + "expected_error_modes": [ + "missed_visual_fact", + "wrong_evidence_linkage" + ], + "match": { + "candidate_signature": "", + "candidate_source_contains": "rematerialization", + "candidate_predicate_contains": "reduces", + "candidate_target_contains": "spill costs", + "hypothesis_title_contains": "", + "premise_contains": "", + "mechanism_contains": "Table II", + "time_scope_contains": "", + "rank_hint": "" + }, + "notes": "" + }, + { + "case_id": "CASE-008", + "enabled": true, + "primary_endpoint": true, + "stratum": "temporal_hard", + "paper_title": "Register Allocation and Spilling via Graph Coloring", + "paper_id": "doi:10.1145/800230.806984", + "year": "1982", + "evidence_kind": "page", + "page_hint": "target paper p. 429, related work / Chaitin 1982", + "creator_prompt": "Сравните, какой вариант лучше фиксирует временную связь: статья Chaitin 1982 предшествует Briggs et al. 1994 и задаёт базовый Chaitin-style graph-coloring allocator, который затем улучшается через optimistic coloring и rematerialization.", + "creator_rationale": "Это temporal-hard кейс: важно не просто упомянуть graph coloring, а правильно восстановить направление влияния до cutoff 1994. Слабая VLM может перепутать базовую работу Chaitin с вкладом Briggs et al. или не показать, что Briggs et al. именно улучшают уже существующий allocator.", + "review_focus": [ + "temporal", + "evidence" + ], + "expected_error_modes": [ + "needs_time_fix", + "wrong_evidence_linkage" + ], + "match": { + "candidate_signature": "", + "candidate_source_contains": "Chaitin", + "candidate_predicate_contains": "precedes", + "candidate_target_contains": "optimistic coloring", + "hypothesis_title_contains": "", + "premise_contains": "", + "mechanism_contains": "", + "time_scope_contains": "1982 before 1994", + "rank_hint": "" + }, + "notes": "" + }, + { + "case_id": "CASE-009", + "enabled": true, + "primary_endpoint": true, + "stratum": "multimodal_hard", + "paper_title": "Improvements to Graph Coloring Register Allocation", + "paper_id": "doi:10.1145/177492.177575", + "year": "1994", + "evidence_kind": "table", + "page_hint": "p. 448, Table III", + "creator_prompt": "Сравните, какой вариант лучше извлекает из Table III данные о стоимости allocation time: optimistic coloring почти не увеличивает время компиляции, а поддержка rematerialization может добавлять небольшой compile-time overhead.", + "creator_rationale": "Это диагностический табличный кейс: нужно отличить улучшение качества allocation от стоимости самого алгоритма. Слабая VLM может написать только про уменьшение spill costs и пропустить, что Table III проверяет другую сторону — время работы allocator.", + "review_focus": [ + "evidence", + "visual_fact" + ], + "expected_error_modes": [ + "missed_visual_fact", + "wrong_evidence_linkage" + ], + "match": { + "candidate_signature": "", + "candidate_source_contains": "allocation time", + "candidate_predicate_contains": "cost", + "candidate_target_contains": "rematerialization", + "hypothesis_title_contains": "", + "premise_contains": "", + "mechanism_contains": "Table III", + "time_scope_contains": "", + "rank_hint": "" + }, + "notes": "" + }, + { + "case_id": "CASE-010", + "enabled": true, + "primary_endpoint": true, + "stratum": "temporal_hard", + "paper_title": "Smallest-Last Ordering and Clustering and Graph Coloring Algorithms", + "paper_id": "doi:10.1145/2402.322385", + "year": "1983", + "evidence_kind": "page", + "page_hint": "target paper pp. 434-435, discussion of Matula and Beck", + "creator_prompt": "Сравните, какой вариант лучше объясняет временную и идейную связь: Matula–Beck 1983 предшествует Briggs et al. 1994 и даёт более сильную эвристику graph coloring, которая помогает понять идею optimistic coloring.", + "creator_rationale": "Это temporal-hard кейс: важно правильно отделить внешний источник идеи от вклада Briggs et al. Слабая VLM может сказать, что Matula–Beck уже решили register allocation, хотя в статье Briggs et al. подчёркивается другое. Их эвристика вдохновляет optimistic coloring, но сама не даёт полного механизма spill-code generation.", + "review_focus": [ + "temporal", + "evidence" + ], + "expected_error_modes": [ + "needs_time_fix", + "wrong_evidence_linkage" + ], + "match": { + "candidate_signature": "", + "candidate_source_contains": "Matula", + "candidate_predicate_contains": "informs", + "candidate_target_contains": "optimistic coloring", + "hypothesis_title_contains": "", + "premise_contains": "", + "mechanism_contains": "smallest-last", + "time_scope_contains": "", + "rank_hint": "" + }, + "notes": "" + }, + { + "case_id": "CASE-011", + "enabled": true, + "primary_endpoint": false, + "stratum": "easy_control", + "paper_title": "Improvements to Graph Coloring Register Allocation", + "paper_id": "doi:10.1145/177492.177575", + "year": "1994", + "evidence_kind": "page", + "page_hint": "p. 428, abstract", + "creator_prompt": "Сравните, какой вариант лучше извлекает простой основной тезис статьи: Briggs, Cooper и Torczon предлагают два улучшения Chaitin-style graph-coloring register allocation — optimistic coloring и расширенную rematerialization.", + "creator_rationale": "Это контрольный простой кейс: нужная информация явно дана в abstract. Хорошая модель должна без сложного анализа выделить две основные идеи статьи и не заменить их более общими словами вроде “улучшение register allocation”.", + "review_focus": [ + "overall" + ], + "expected_error_modes": [], + "match": { + "candidate_signature": "", + "candidate_source_contains": "graph-coloring register allocator", + "candidate_predicate_contains": "uses", + "candidate_target_contains": "optimistic coloring", + "hypothesis_title_contains": "", + "premise_contains": "", + "mechanism_contains": "rematerialization", + "time_scope_contains": "", + "rank_hint": "" + }, + "notes": "" + } + ] +} \ No newline at end of file