index stringlengths 1 3 | question stringlengths 39 3.36k | answer stringlengths 9 650k | category stringclasses 19
values | eval_method stringclasses 3
values | image_path listlengths 0 50 | segments stringlengths 0 36.5k | qid stringlengths 21 43 | pair_qid stringlengths 7 28 | partition stringclasses 4
values | mode stringclasses 2
values | answer_type stringclasses 3
values | judge_prompt stringclasses 52
values | answer_note stringclasses 50
values | doc_id stringclasses 97
values | domain stringclasses 10
values | language stringclasses 2
values | source_index stringlengths 1 28 | images listlengths 0 50 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
300 | def conv1x1(in_feat: int, out_feat: int) -> None: ...
def res_transformer_block(feat: int, heads: int) -> None: ...
def down_conv2x2(in_feat: int, out_feat: int, stride: int) -> None: ...
def linear(in_feat: int, out_feat: int) -> None: ...
参考 **Figure 6** 及其图注。仅基于该图，通过按顺序列出每个操作（每行一个操作，并填入所有具体参数）来重构 QFlowNet 的完整顺序前向传播... | "conv1x1(in_feat, 64)\nres_transformer_block(64, 8)\nres_transformer_block(64, 8)\nres_transformer_block(64, 8)\nres_transformer_block(64, 8)\ndown_conv2x2(64, 128, 2)\nres_transformer_block(128, 8)\nres_transformer_block(128, 8)\nres_transformer_block(128, 8)\nres_transformer_block(128, 8)\nconv1x1(128, 256)\nlinear(2... | E2 | judge | [
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"ef/efae... | qflownet_001__zh_all_first | qflownet_001 | zh_all_first | all_first | text | You are evaluating a model's reconstruction of the QFlowNet forward pass from Figure 6.
The correct sequential forward pass is (one call per line):
{answer}
Model prediction:
{prediction}
Scoring: 13 operations total, each worth 1/13. Award credit for each operation that is correct in both function name and all argu... | {"reasoning": "E2 flowchart reconstruction of QFlowNet forward pass from Figure 6 (Appendix A, page 7). Derivation: reading top-to-bottom from the figure: (1) conv1x1(in_feat, 64) — initial projection; (2-5) four res_transformer_block(64, 8) — first stage with 64 features, 8 attention heads; (6) down_conv2x2(64, 128, 2... | qflownet | cs | zh | qflownet_001 | [
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"images/SciDocBench/50/5091c... | |
301 | 你将看到四张截图，标记为 A、B、C 和 D。每张截图都包含同一篇科学论文正文中的一段文字。
你的任务是确定这四段文字在论文中出现的顺序，从最早到最晚。这些段落可能来自以下任一部分：Abstract、Introduction、Method、Experiments/Results 或 Conclusion。
仅根据每段文字的内容、功能和写作风格来推断顺序。
严格按以下格式输出一行：
A>B>C>D
这里，“>”表示“在论文中出现得早于”。
不要提供任何解释、推理、章节名称或额外文本。只输出最终顺序。 | "D>C>A>B" | A1 | judge | [
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] | np_a1_01_001__zh_all_first | np_a1_01_001 | zh_all_first | all_first | text | You are an expert evaluator for ranking tasks. I will provide you with the ground-truth and the model's predicted ranking. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
If the model's predicted ranking is entirely correct, it receiv... | {} | np_a1_01 | scientific | zh | np_a1_01_001 | [
"images/SciDocBench/ce/ce07ab28f0ab9448cd3c12fb2dff907840050841f49ef942bc4d92eecd8e73a1.jpg",
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"images/SciDocBench/68/682c8... | |
302 | 你将获得五张截图，标记为 A、B、C、D 和 E。每张截图都包含同一篇科学论文正文中的一段文字。
你的任务是确定这五段文字在论文中出现的顺序，从最早到最晚。这些段落可能来自以下任一部分：摘要、引言、相关工作、方法、实验/结果或结论。
仅根据每段文字的内容、功能和写作风格来推断顺序。
严格按以下格式输出一行：
A>B>C>D>E
这里，“>”表示“在论文中出现得早于”。
不要提供任何解释、推理、章节名称或额外文本。只输出最终顺序。 | "E>A>D>C>B" | A1 | judge | [
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"41/413d... | np_a1_02_001__zh_all_first | np_a1_02_001 | zh_all_first | all_first | text | You are an expert evaluator for ranking tasks. I will provide you with the ground-truth and the model's predicted ranking. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
If the model's predicted ranking is entirely correct, it receiv... | {} | np_a1_02 | scientific | zh | np_a1_02_001 | [
"images/SciDocBench/37/378b893956f9a9f2413f13566395e2885ec880d2a9308a032ea5c901931bf99e.jpg",
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"images/SciDocBench/07/074f6... | |
303 | 你将看到五张截图，标记为 A、B、C、D 和 E。每张截图都包含同一篇科学论文正文中的一段文字。
你的任务是确定这五段文字在论文中出现的顺序，从最早到最晚。这些段落可能来自以下任一部分：Abstract、Introduction、Related Work、Method、Experiments/Results 或 Conclusion。
仅根据每段文字的内容、功能和写作风格来推断顺序。
严格按以下格式输出一行：
A>B>C>D>E
这里，“>”表示“在论文中出现得更早”。
不要提供任何解释、推理、章节名称或额外文本。只输出最终顺序。 | "B>E>D>C>A" | A1 | judge | [
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"0f/0f43... | np_a1_03_001__zh_all_first | np_a1_03_001 | zh_all_first | all_first | text | You are an expert evaluator for ranking tasks. I will provide you with the ground-truth and the model's predicted ranking. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
If the model's predicted ranking is entirely correct, it receiv... | {} | np_a1_03 | scientific | zh | np_a1_03_001 | [
"images/SciDocBench/c8/c8e4cbdac9bd0077122c57b1754e0143f999bcf7c834a6890f402f61053b9ae3.jpg",
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"images/SciDocBench/9b/9b912... | |
304 | 给你五张截图，标记为 A、B、C、D 和 E。每张截图都包含同一篇科学论文正文中的一段文字。
你的任务是确定这五段文字在论文中出现的顺序，从最早到最晚。这些段落可能来自以下任一部分：Abstract、Introduction、Related Work、Method、Experiments/Results 或 Conclusion。
仅根据每段文字的内容、功能和写作风格来推断顺序。
输出严格为一行，格式如下：
A>B>C>D>E
这里，“>” 表示“在论文中出现得早于”。
不要提供任何解释、推理、章节名称或额外文本。只输出最终顺序。 | "A>E>C>B>D" | A1 | judge | [
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"81/816a... | np_a1_04_001__zh_all_first | np_a1_04_001 | zh_all_first | all_first | text | You are an expert evaluator for ranking tasks. I will provide you with the ground-truth and the model's predicted ranking. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
If the model's predicted ranking is entirely correct, it receiv... | {} | np_a1_04 | scientific | zh | np_a1_04_001 | [
"images/SciDocBench/6f/6f23f2d3696d0e0bfd0ae8fee266239f2b830ccbd58dc55cf8fa8679894e0871.jpg",
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"images/SciDocBench/1c/1c990... | |
305 | 你将看到五张截图，标记为 A、B、C、D 和 E。每张截图都包含同一篇科学论文正文中的一段文字。
你的任务是确定这五段文字在论文中出现的顺序，从最早到最晚。这些段落可能来自以下任一部分：Abstract、Introduction、Related Work、Method、Experiments/Results 或 Conclusion。
仅根据每段文字的内容、功能和写作风格来推断顺序。
严格按以下格式输出一行：
A>B>C>D>E
这里，“>”表示“在论文中出现得早于”。
不要提供任何解释、推理、章节名称或额外文本。只输出最终顺序。 | "D>B>C>E>A" | A1 | judge | [
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"93/93ad... | np_a1_05_001__zh_all_first | np_a1_05_001 | zh_all_first | all_first | text | You are an expert evaluator for ranking tasks. I will provide you with the ground-truth and the model's predicted ranking. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
If the model's predicted ranking is entirely correct, it receiv... | {} | np_a1_05 | scientific | zh | np_a1_05_001 | [
"images/SciDocBench/5d/5d559c4d0b23c6ec9885fb14f12ab7c6b67d7dd6871c47cb49949f6b6e1aec44.jpg",
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"images/SciDocBench/16/16b3a... | |
306 | 现在我向 Deepseek-R1 （DS-R1） 提出了一个物理问题。我的题库包含许多从 AMC12、AMC10、AIME24 和 OlympiadBench 中提取的数学定理和计算方法。通过使用文档中提到的方法，我发现我的题库也可以帮助 DS-R1 更正确地回答这个物理问题。文档中的哪张表最能支持上述现象？
请按以下格式输出：
{"Location": "<e.g. Table 10>", "Description": "解释你为什么得出这个答案。"} | {"Location": "Table 2", "Description": "The phenomenon in the question uses examples from one domain (math) to help with a different domain (physics), which is about cross-domain generalizability. And the example bank has a low similarity to the target problem (since physics ≠ math). The section 'Generalizability' best... | A2 | judge | [
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"62/6230... | np_a2_03_001__zh_all_first | np_a2_03_001 | zh_all_first | all_first | json | You are an expert evaluator the model's response to a question. I will provide you with the ground-truth and the model's prediction. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
1. If the model's predicted 'Location' is incorrect, ... | {} | np_a2_03 | scientific | zh | np_a2_03_001 | [
"images/SciDocBench/dd/dda461fd0cc7adeb7caeef75df30e20be929319ed8343b0160f9b198a3cdcf4a.jpg",
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"images/SciDocBench/a1/a106f... | |
307 | 我需要使用本文中提到的一些数据集来训练我自己的模型。请按照以下步骤帮助我选择数据集：
1. 根据 token 数量选择排名前 15 的数据集。
2. 在这 15 个数据集中，仅保留来自 "Embodied AI" 和 "Spatial Understanding" 类别的数据集。
3. 按字母顺序对它们排序，并为每个类别输出一个列表，然后将它们合并为一个字典。
请按以下格式提供最终输出：
{"Embodied AI": ["<e.g. MsCoco>", "<e.g. MegaDepth">, ...], "Spatial Understanding": ["<e.g. ChartQA>", "<e.g. DocVQA>", ...... | {"Embodied AI": ["Eb-Alfred", "Eb-Habitat", "Muep", "OWMM-VLM Data", "Robo2VLM-1", "RoboVQA"], "Spatial Understanding": ["Vg-Llm-Scannet-Det", "Vica-322K", "Vsi 590K"]} | A4 | json_match | [
"f7/f75599ff0b8032a868da55eff38bc281f29930c96239b78da68f6486a6418224.jpg"
] | np_a4_01_001__zh_all_first | np_a4_01_001 | zh_all_first | all_first | json | {} | np_a4_01 | scientific | zh | np_a4_01_001 | [
"images/SciDocBench/f7/f75599ff0b8032a868da55eff38bc281f29930c96239b78da68f6486a6418224.jpg"
] | ||
308 | 你是一名论文阅读助手。阅读这篇文章，告诉我鞋底区域 II 和 III 中箭头的方向（从上到下还是从下到上？）
你的答案应该是几个单句或一个词。 | "Both from up to bottom" | A5 | judge | [
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"images/SciDocBench/73/730d8... | ||
309 | 在 Fig. 1 的底部图中，AQC(exp) 和 QAOA 曲线的 $\epsilon$ 值范围最接近以下哪个选项？
A. 5e-3 to 5e-1
B. 5e-4 to 3e-1
C. 1e-3 to 3e-1
D. 5e-3 to 1e0
E. 5e-4 to 1e-1
F. 8e-4 to 1e0
G. 8e-4 to 5e-1
H. 1e-3 to 1e-1
仔细思考，并按以下字典格式输出：
{"AQC(exp) range": "<e.g. A>", "QAOA range": "<e.g. A>", “Description": "<Explain why you derive this answer>"} | {"AQC(exp) range": "G", "QAOA range": "C", "Description": "The bottom figure is a log-log plot with κ=10. The scale on the x-axis gradually becomes denser from left to right, then suddenly thins out after a certain mark, before becoming denser again, repeating this cycle. Based on the characteristics of logarithmic cur... | B2 | judge | [
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"d7/d767... | np_b2_01_001__zh_all_first | np_b2_01_001 | zh_all_first | all_first | json | You are an expert evaluator the model's response to a question. I will provide you with the question, the ground-truth and the model's prediction. You need to score the model's prediction.
Question: {prompt}
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
1. If the model cor... | {} | np_b2_01 | scientific | zh | np_b2_01_001 | [
"images/SciDocBench/c7/c76bc2c2682d7f8bc8b17bbd006ed43c2a8e3030d1fb83e2543e1082d8825804.jpg",
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"images/SciDocBench/7b/7b0ea... | |
310 | 你将获得一篇科学 PDF 文档。请仔细检查文档中的每个图和子图，并找出所有内容/数据与正文不匹配的地方。
对于你识别出的每个有问题的单元格，输出一个包含以下字段的 JSON 对象：
- Location: 该问题所属的具体表格，例如 "Figure 1", "Figure 2(b)"
- Description: 解释为什么这个（子）图存在错误。
输出一个 JSON 列表，其中每个有问题的单元格对应一个对象。如果未发现问题，则输出空列表。只输出 JSON，不要输出任何其他评论。 | [{"Location": "Figure 8(b)", "Description": "The x-axis displays adsorption dosages of 0.5, 1.0, 1.5, 2.0, and 2.5 g/L, which contradicts the experimental dosages described in the main text (2, 4, 6, 8, and 10 g)."}, {"Location": "Figure 8(c)", "Description": "The x-axis displays agitation speed of of 50, 100, 150, 20... | C1 | judge | [
"2a/2ac547eb7afe480dfec223af5399d5a0d4ac7e2bcebffcba081a816a74d43b06.jpg",
"7b/7bf7fdce58d1b97528e80355c7d196197ba180a448c43848192a414178ab88bd.jpg",
"7d/7d16f5eaecec8797cadff724fc1655efc6778eed52980e1da8260d92ddd4de32.jpg"
] | np_c1_01_001__zh_all_first | np_c1_01_001 | zh_all_first | all_first | json | You are an expert evaluator. You will be given a question, a reference answer, and a model prediction.
Score the prediction from 0.0 to 1.0 based on how well it matches the reference answer in content and accuracy.
Question:
{prompt}
Reference answer:
{answer}
Model prediction:
{prediction}
Scoring rules (Total=1.0... | {} | np_c1_01 | scientific | zh | np_c1_01_001 | [
"images/SciDocBench/2a/2ac547eb7afe480dfec223af5399d5a0d4ac7e2bcebffcba081a816a74d43b06.jpg",
"images/SciDocBench/7b/7bf7fdce58d1b97528e80355c7d196197ba180a448c43848192a414178ab88bd.jpg",
"images/SciDocBench/7d/7d16f5eaecec8797cadff724fc1655efc6778eed52980e1da8260d92ddd4de32.jpg"
] | |
311 | 通读整篇论文。只使用这篇论文。不要解释。准确输出一个 20 个字符的位串。当且仅当该断言被论文完全支持时使用 1；否则使用 0。
A. 该论文研究对称的双社区 SBM G(n,p,q)，其中有两个大小相等的簇，簇内边概率为 p，跨簇边概率为 q。
B. 论文中的精确恢复意味着恢复顶点标签，不允许对所有标签进行全局翻转。
C. 在主要阈值机制中，论文取 p = α log(n)/n 和 q = β log(n)/n，为简单起见关注 α > β。
D. Theorem 1 本身表明，当 (α+β)/2 − sqrt(αβ) < 1 时，ML 以趋于一的概率失败。
E. Theorem 2 表明，当 (α+β)/2 − sqrt(αβ)... | "10101110101111101100" | C1 | judge | [
"90/90a9427cde889071385c2ad90e12b6956b5e38f67853d15010e2892842b8414b.jpg",
"56/5616259ecd97054e9e36f10f3db8f5e315466c50ea6e1d59a2a6cdb078f376e6.jpg",
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"00/00f7c91b079cf7002df9d48e243dac54bcce45ab0e7aa78e065026e5c0898bad.jpg",
"4e/4ef7... | np_c1_07_001__zh_all_first | np_c1_07_001 | zh_all_first | all_first | text | You are an expert evaluator the model's response to a question. I will provide you with the ground-truth and the model's prediction. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
1. The ground-truth is a bit-string with 20 bits. If t... | {} | np_c1_07 | scientific | zh | np_c1_07_001 | [
"images/SciDocBench/90/90a9427cde889071385c2ad90e12b6956b5e38f67853d15010e2892842b8414b.jpg",
"images/SciDocBench/56/5616259ecd97054e9e36f10f3db8f5e315466c50ea6e1d59a2a6cdb078f376e6.jpg",
"images/SciDocBench/16/16c18fb3215b030349c63d7e8af06b0fc18947e0baa3cf3ef1a5a7143fa6ad7e.jpg",
"images/SciDocBench/00/00f7c... | |
312 | 通读整篇论文。仅使用这篇论文。不要解释。准确输出一个 20 字符的比特串。当且仅当该断言被论文完全支持时使用 1；否则使用 0。
A. 权衡函数 T(P,Q)(α) 被定义为在 I 类错误至多为 α 的拒绝规则中，II 类错误的下确界。
B. Proposition 2.2 说权衡函数是凹的、连续的、非递减的，并且满足 f(x) ≥ 1−x。
C. 如果对所有相邻数据集 S,S′ 都有 T(M(S),M(S′)) ≥ f，则机制是 f-DP。
D. Proposition 2.4 通过 f^S=max{f,f^{-1}} 对 f 进行对称化，并且这是一个更宽松的隐私界，因为 f^S≤f。
E. Proposition 2.5 说明... | "10101101011101011101" | C1 | judge | [
"cc/cc94f37fdbe629de5ba8bc7c75fd126a769b3ca5df279bc3baadd920440bd62d.jpg",
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"66/66d5... | np_c1_09_001__zh_all_first | np_c1_09_001 | zh_all_first | all_first | text | You are an expert evaluator the model's response to a question. I will provide you with the ground-truth and the model's prediction. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
1. The ground-truth is a bit-string with 20 bits. If t... | {} | np_c1_09 | scientific | zh | np_c1_09_001 | [
"images/SciDocBench/cc/cc94f37fdbe629de5ba8bc7c75fd126a769b3ca5df279bc3baadd920440bd62d.jpg",
"images/SciDocBench/fb/fbe5c40808a0615f09f940f37ae00f56e977ae7e3f84c1b19c5f47d1e16d9d81.jpg",
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"images/SciDocBench/23/23104... | |
313 | 在我提供的文档中，右侧有一个图表。你的任务是找出图表中的一个错误。
图表描述：该图表使用 Python 的 matplotlib package 生成，所有实验数据随后都在图表上进行了标注。已知所有数据注释都是正确的；然而，在 matplotlib 绘图过程中，有一个数值被错误设置，导致这个特定数据集的图表中出现了绘图错误。
请按照以下步骤操作：
1. 识别图表中的绘图错误。
2. 识别与该错误对应的数据集。
3. 按照以下精确格式将你的答案作为 JSON dictionary 返回：
{"Dataset": "<The name of the corresponding dataset>, "Description": "<An ... | {"Dataset": "DenseFusion", "Description": "The bar height of DenseFusion is approximately at the middle of 1000 and 1500. However, the label is 1453, which should be near 1500. So the bar of DenseFusion is incorrectly plotted.\nFurthermore, the slope of the red line is nearly the same from LLaVa-23K to ShareGPT4V and ... | C2 | judge | [
"5e/5ecdc5ad6c5e9e809c5f6a4239fce6a21f7403661e15e0eb37eccb658973937e.jpg"
] | np_c2_01_001__zh_all_first | np_c2_01_001 | zh_all_first | all_first | json | You are an expert evaluator the model's response to a question. I will provide you with the ground-truth and the model's prediction. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
1. If the model's predicted 'Dataset' is incorrect, t... | {} | np_c2_01 | scientific | zh | np_c2_01_001 | [
"images/SciDocBench/5e/5ecdc5ad6c5e9e809c5f6a4239fce6a21f7403661e15e0eb37eccb658973937e.jpg"
] | |
314 | 我提供了某文档的前五页，以及第 5 页介绍的 Figure 2 中的子图 Figure 2b。然而，在 Figure 2b 中，与两个神经元对应的神经活动图的标签被颠倒了，导致该图中的内容与文档中对 Figure 2b 的分析不一致。请识别神经活动图被颠倒的这两个神经元。
请按以下格式输出：
{"Neruon 1": "<e.g. SMBV>, "Neuron 2": "<e.g. SMBV>", "Description": "说明你为什么得出这个答案。"} | {"Neuron 1": "SMDV", "Neuron 2": "SAAV", "Description": "From Figure 2b we know that the reversal ends at x=2. According to the text on page 5, SAAV activity is described as being higher during reversals in ventral turns (blue line) than in dorsal turns (green line). It suggests that SAAV may predict the future turning... | C2 | judge | [
"1d/1d7d3de0842dc9ed9d1b9d2224287627b0492ef87b7d10e4ecbe2d0f2287f9cb.jpg",
"0b/0b1e141d3d269801504f0255dd8e6fec030fad5daadeddd6864355c313b1b378.jpg",
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"27/27a0... | np_c2_03_001__zh_all_first | np_c2_03_001 | zh_all_first | all_first | json | You are an expert evaluator the model's response to a question. I will provide you with the ground-truth and the model's prediction. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
1. Both fields of 'Neurons' in the prediction MUST be... | {} | np_c2_03 | scientific | zh | np_c2_03_001 | [
"images/SciDocBench/1d/1d7d3de0842dc9ed9d1b9d2224287627b0492ef87b7d10e4ecbe2d0f2287f9cb.jpg",
"images/SciDocBench/0b/0b1e141d3d269801504f0255dd8e6fec030fad5daadeddd6864355c313b1b378.jpg",
"images/SciDocBench/67/674d5d3047d56ce3bcc985ec2c33fdece50466230cf6b18c62248c5a3a57f477.jpg",
"images/SciDocBench/08/0885d... | |
315 | 从附加文件中识别 Fig (b) 和 (c) 中红线和黄线的相对高度。
对于每个子图，检查完整的时间过程，并根据 x 轴进展以及任何视觉上可区分的阶段边界，将其划分为连续的时间阶段。对于每个阶段，确定两条线的相对位置：1/ red > yellow；2/ red < yellow。
按时间顺序输出每个子图的结果。将每个阶段输出为一个字典，并将它们放入一个列表中：
[{"Subplot": "Fig. (b)", "Stage_order": "1", "Relative_position": "<red > yellow / red < yellow>"}, {"Subplot": "Fig. (b)", "Stage_order"... | "[\n {\"Subplot\": \"Fig. (b)\", \"Stage_order\": \"1\", \"Relative_position\": \"red < yellow\"},\n {\"Subplot\": \"Fig. (b)\", \"Stage_order\": \"2\", \"Relative_position\": \"red > yellow\"},\n {\"Subplot\": \"Fig. (c)\", \"Stage_order\": \"1\", \"Relative_position\": \"red > yellow\"},\n {\"Subplot\": \"Fig. (c... | C3 | judge | [
"2f/2f128a7ea3f0be2c82142ff9ca2e437c6271329f595f9580abcf4922295cb2a2.jpg"
] | np_c3_03_001__zh_all_first | np_c3_03_001 | zh_all_first | all_first | text | You are an expert evaluator the model's response to a question. I will provide you with the ground-truth and the model's prediction. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
1. The ground-truth is a list of dictionaries. The or... | {} | np_c3_03 | scientific | zh | np_c3_03_001 | [
"images/SciDocBench/2f/2f128a7ea3f0be2c82142ff9ca2e437c6271329f595f9580abcf4922295cb2a2.jpg"
] | |
316 | 你是一名理论物理助手。阅读所提供的三篇论文：
- Paper A: Solving the Quantum Many-Body Problem with Artificial Neural Networks
- Paper B: Quantum Entanglement in Neural Network States
- Paper C: Approximating quantum many-body wave functions using artificial neural networks
使用 Paper A 的记号系统（即以 $S$, $\Psi_M(S;\mathcal{W})$, $\mathcal{W}=\... | [{"paper": "B", "eq_label": "Eq.(1)", "rewritten_formula": "\\Psi_M(\\mathcal{S}; \\mathcal{W}) = \\sum_{\\{h_i\\}} \\exp\\left(\\sum_{j=1}^N a_j \\sigma_j^z + \\sum_{i=1}^M b_i h_i + \\sum_{i=1}^M \\sum_{j=1}^N W_{ij} h_i \\sigma_j^z\\right)"}, {"paper": "B", "eq_label": "Rényi entropy", "rewritten_formula": "S_{\\alp... | D1 | judge | [
"62/62158a5cca63e70ffc0e6a735dbabaaac87e3e72662a9501986d7fcf29d74241.jpg",
"5d/5dfc62607fa7b5148cf37f1a8cb9cbe62b00dee11da78741259acea86464a333.jpg",
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"c0/c0f0... | np_d1_01_001__zh_all_first | np_d1_01_001 | zh_all_first | all_first | json | You are an expert evaluator the model's response to a question. I will provide you with the ground-truth and the model's prediction. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
1. The ground-truth is a list of 10 dictionaries. (If... | {} | np_d1_01 | scientific | zh | np_d1_01_001 | [
"images/SciDocBench/62/62158a5cca63e70ffc0e6a735dbabaaac87e3e72662a9501986d7fcf29d74241.jpg",
"images/SciDocBench/5d/5dfc62607fa7b5148cf37f1a8cb9cbe62b00dee11da78741259acea86464a333.jpg",
"images/SciDocBench/65/6596f44bea18760e29c037e313d550dceb953d4a3629c9009864281d7e213464.jpg",
"images/SciDocBench/9c/9c5b3... | |
317 | 以 mermaid 格式复现 Figure (b)。保留所有节点以及表示 'Gap junction' 的锯齿线。忽略 'Chemical synapse' 的箭头。每个节点应严格遵循图中的名称，你不需要复现原图中使用的颜色。找出所有 'Gap junction' 连接，并用 mermaid 绘制它们。只使用普通无向线代替锯齿线。
最终答案按以下格式输出：
```mermaid
graph LR
1[AIB]
2[AVE]
3[AVA]
1 --- 2
2 --- 3
``` | "```mermaid\ngraph LR\n 1[AIB]\n 2[AVE]\n 3[AVA]\n 4[RIM]\n 5[SMB]\n 6[SAA]\n 7[RMD]\n 8[SMD]\n 9[RIV]\n \n 1 --- 2\n 1 --- 4\n 1 --- 9\n 2 --- 3\n 2 --- 4\n 2 --- 7\n 2 --- 9\n 3 --- 4\n 5 --- 6\n 6 --- 7\n 7 --- 8\n 8 --- 9\n```" | E1 | judge | [
"d2/d2cfa1ed242929c4ea1708bfdf2a9caea63d4e91722ae1b26351717544154b9f.jpg"
] | np_e1_02_001__zh_all_first | np_e1_02_001 | zh_all_first | all_first | text | You are an expert evaluator the model's response to a question. I will provide you with the ground-truth and the model's prediction. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
1. Extract the content between ```mermaid and ``` (to... | {} | np_e1_02 | scientific | zh | np_e1_02_001 | [
"images/SciDocBench/d2/d2cfa1ed242929c4ea1708bfdf2a9caea63d4e91722ae1b26351717544154b9f.jpg"
] | |
318 | 请使用 Mermaid 语法为 R-(BN)₂ 绘制一个纵向流程图，严格遵循以下格式：
1. 节点格式：
- 内容：化学式 + 相对能量（单位：eV，数值取自论文，相对于 S₀）
- 样式：统一填充颜色 `#e8f5e9`，边框颜色 `#1b5e20`，黑色文本
2. 边格式：标签严格为 `λ=xxx, τ=xxx`（波长单位为 nm，时间带单位）；如果某个过程缺少某个参数，则使用 `λ=-` 作为占位符
3. 流程：从 2-NaphNH₂ 开始，经过合成步骤到 R-(BN)₂，然后进入光物理循环（必须展示中间步骤）
4. 尽可能使用论文中的原始符号
5. 全文使用 Unicode 数学字符（例如希腊字母 λ、τ，下标... | "flowchart TD\n A[\"2-NaphNH₂<br/>0.00 eV\"] -->|\"λ=-, τ=-\"| B[\"(BN)₁<br/>0.00 eV\"]\n A -->|\"λ=-, τ=-\"| C[\"R-(BN)₂<br/>0.00 eV\"]\n\n C -->|\"λ=280, τ=-\"| D[\"R-(BN)₂ S₁(¹LE)<br/>3.32 eV\"]\n D -->|\"λ=365, τ=3.0ns\"| C\n D -->|\"λ=-, τ=-\"| E[\"R-(BN)₂ ¹HLCT<br/>2.88 eV\"]\n E -->|\"λ=430, τ=... | B2 | judge | [
"06/0670c21fab94fc043653e00fdca33d4272ac01ead7ff555b5a92b52e93a7533a.jpg",
"f2/f2b9a8d54be9f7038d88d9ba614fb4b188a39afdfcd7121d377bc26ab1003f35.jpg",
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"a3/a31a... | np_b2_03_001__zh_all_first | np_b2_03_001 | zh_all_first | all_first | text | You are an expert evaluator the model's response to a question. I will provide you with the ground-truth and the model's prediction. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
1. Determine the score based on how well the predicte... | {} | np_b2_03 | scientific | zh | np_b2_03_001 | [
"images/SciDocBench/06/0670c21fab94fc043653e00fdca33d4272ac01ead7ff555b5a92b52e93a7533a.jpg",
"images/SciDocBench/f2/f2b9a8d54be9f7038d88d9ba614fb4b188a39afdfcd7121d377bc26ab1003f35.jpg",
"images/SciDocBench/b3/b3947919d281bac8223e948414cb4aa849f23b584e51b035441161d764a93274.jpg",
"images/SciDocBench/95/95d8d... | |
319 | 找出 Theorem 7.1 中每一项的依赖链，包括 7.1(1a)、7.1(1b)、7.1(1c)、7.1(2) 和 7.1(3)。
使用箭头写出紧凑的链，例如：
Theorem 7.1(1c) <- Proposition 7.1(1a) + Lemma 3.9
Proposition 3.9 <- Lemma 3.9 + Assumption 3.6
Lemma 3.2 <- Definition 3.4 | "Theorem 7.1(1a) ← Proposition 6.3 + Lemma 3.10\nTheorem 7.1(1b) ← Theorem 7.1(1a)\nTheorem 7.1(1c) sharpness ← Proposition 3.5 + Lemma 2.1\nTheorem 7.1(2) ← Proposition 6.4 + Lemma 3.10\nTheorem 7.1(3) ← Proposition 6.5 + Lemma 3.10\nLemma 3.10 ← Lemma 3.9\nLemma 3.9 ← Definitions 3.6, 3.7 + Proposition 3.5\nPropositi... | B3 | judge | [
"dd/dde8f8885392e469d83602d0f1d082e12700bc0fed6710fba1de0d117c4a5f77.jpg",
"2b/2b6a97a8f4f8fdd72565e98522e54889ba4f3b75c2ddfa1335eb0c7aef823783.jpg",
"eb/eb1fd19916d7042b44a0e2820027863264dd61c7e73cfa06205c2883aff7977f.jpg",
"5b/5b8d45889f33080770ea7bc2893debc66f332fe139c496bfa333d5f0324c359c.jpg",
"c3/c384... | np_b3_01_001__zh_all_first | np_b3_01_001 | zh_all_first | all_first | text | You are an expert evaluator the model's response to a question. I will provide you with the ground-truth and the model's prediction. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
1. The initial score is set as 0.0.
2. Full score is ... | {} | np_b3_01 | scientific | zh | np_b3_01_001 | [
"images/SciDocBench/dd/dde8f8885392e469d83602d0f1d082e12700bc0fed6710fba1de0d117c4a5f77.jpg",
"images/SciDocBench/2b/2b6a97a8f4f8fdd72565e98522e54889ba4f3b75c2ddfa1335eb0c7aef823783.jpg",
"images/SciDocBench/eb/eb1fd19916d7042b44a0e2820027863264dd61c7e73cfa06205c2883aff7977f.jpg",
"images/SciDocBench/5b/5b8d4... | |
320 | 在我提供的文档中，表 1 中 'Spatial Models' 类别有几处错误。你的任务是找出所有这些错误。
请遵循以下步骤：
1. 识别表中的错误。
2. 识别与这些错误相对应的数值。
3. 按以下确切格式将你的答案作为三个 JSON 字典的列表返回：
[{"value": "<The corresponding numerical value>, "description": "<A description of why this part of the table is incorrect.>"}, {"value": "<The corresponding numerical value>, "description": "... | [{"value": "48.4", "description": "The average accuracy for Spatial-MLLM-4B is incorrectly calculated. The sum of the 8 category scores (65.3 + 34.8 + 63.1 + 45.1 + 41.3 + 46.2 + 33.5 + 46.3) equals 375.6, which should result in an average of 46.95 (rounded to 47.0), not 48.4."}, {"value": "47.8", "description": "The a... | C1 | judge | [
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"e8/e8c7... | np_c1_03_001__zh_all_first | np_c1_03_001 | zh_all_first | all_first | json | {} | np_c1_03 | scientific | zh | np_c1_03_001 | [
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"images/SciDocBench/6b/6b1e4... | ||
321 | 通读整篇论文。只使用这篇论文。不要解释。只输出一个恰好为 20 个字符的比特串。当且仅当该说法得到论文的完全支持时使用 1；否则使用 0。
A. 该论文研究一种简化的 Transformer 模型，该模型仅包含 attention blocks，省略了 MLPs 和 LayerNorm。
B. 引言称，添加 LayerNorm 会导致非常不同的动力学，因为 tokens 被约束为在一个球面上演化。
C. 紧支撑适定性分析通过 conditional optimal transport 框架明确包括了 masked self-attention。
D. 紧支撑定理仅针对 unmasked single-head Softmax se... | "11101010111011110011" | C1 | judge | [
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"images/SciDocBench/bb/bb542... | ||
322 | 通读整篇论文。仅使用这篇论文。不要解释。输出恰好一个 20 个字符的比特串。当且仅当该声明得到论文的完全支持时使用 1；否则使用 0。
A. 摘要陈述，常数数量的 self-attention 层可以高效地模拟常数数量的 MPC 轮，并且也可以被其模拟。
B. 摘要还声称，对若干其他神经序列模型以及次二次 transformer 近似不能高效解决的任务，对数深度是足够的。
C. 引言将循环架构描述为比 transformers 更强，因为循环状态可以在所有位置上并行交互。
D. Theorem 3.1 假设一个 randomized R-round (γ,δ)-MPC protocol，并得到一个深度恰好为 R 的 transfo... | "11001110101011010110" | C1 | judge | [
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"images/SciDocBench/ed/ed340... | ||
323 | 通读整篇论文。仅使用这篇论文。不要解释。准确输出一个 20 字符的比特串。当且仅当该断言得到论文完全支持时使用 1；否则使用 0。
A. 该论文研究可分 Hilbert 空间中的线性回归，使用最小范数插值估计量。
B. 协变量假设允许 x = VΛ^{1/2}z，其中 z 的各坐标是独立的次高斯变量；它们不必是 Gaussian。
C. 在 Assumption 5 下，数据可以有许多解满足 Xθ = y，而最小范数插值解是 X^T(XX^T)^{-1}y。
D. 当 Hilbert 空间具有有限维度 p < n 且 X 的秩为 p 时，论文说存在许多满足 Xθ = y 的插值解。
E. Definition 3 将 r_k(Σ)... | "11100111011010101011" | C1 | judge | [
"3c/3ced1d04bc0fe76549252cb8ad4780767839e5adeb51220b547357833f832cba.jpg",
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"f0/f0fa... | np_c1_08_001__zh_all_first | np_c1_08_001 | zh_all_first | all_first | text | {} | np_c1_08 | scientific | zh | np_c1_08_001 | [
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"images/SciDocBench/9b/9bacb... | ||
324 | 从所附文件中识别子图 Fig. 5i 的内容，该子图标题为“疼痛敏化与触觉或触觉感觉的总结性回路图”。子图 Fig. 5i 底部的节点是 'SC'。右下角节点的名称是 'RVM-OPRM1+'。将该子图重新组织为结构化流程图描述。
对于图中显示的每一种关系，确定：
1. 源节点
2. 目标节点
3. 相互作用类型：excitatory、inhibitory，或如果未明确指出则为 unknown
4. 与该连接相关的任何标签、注释、分子、受体或通路
你可以忽略虚线所示的连接。对于每个源节点和目标节点的名称，必须使用上面给出的缩写或图中标注的缩写。
然后将整个子图转换为清晰的流程图式结构化输出。
以字典列表的形式输出，其中每个条目表示一... | "[\n{\"Source\": \"DCN\", \"Target\": \"VPL\", \"Interaction\": \"excitatory\", \"Annotation\": \"Touch or tactile\"},\n{\"Source\": \"SC\", \"Target\": \"VPL\", \"Interaction\": \"excitatory\", \"Annotation\": \"Pain sensitization\"},\n{\"Source\": \"SC\", \"Target\": \"Po\", \"Interaction\": \"excitatory\", \"Annotat... | C3 | judge | [
"49/49e27e6cd4b00680c10fd8f62792f83623c8e905aad4a385be531b990c4e0ac3.jpg"
] | np_c3_02_001__zh_all_first | np_c3_02_001 | zh_all_first | all_first | text | {} | np_c3_02 | scientific | zh | np_c3_02_001 | [
"images/SciDocBench/49/49e27e6cd4b00680c10fd8f62792f83623c8e905aad4a385be531b990c4e0ac3.jpg"
] | ||
325 | 你是一名物理学论文阅读助手。请阅读所提供的论文，并针对以下每一条在正文或 Methods 中提出的论断，识别所有直接支持该论断的具体 Figures 或 Extended Data Figures，并报告每个图出现的页码。
你的回答必须格式化为一个 JSON 对象，其中每个键是论断索引（字符串形式），每个值是一个恰好包含两个键的对象：
- `supporting_figures`: 一个由字符串组成的 JSON 数组（例如，`["Fig. 1c", "Extended Data Fig. 4"]`）。
- `page_numbers`: 一个由整数构成的 JSON 数组，给出每个对应图出现的页码。请报告 PDF 内部页码。
要... | {"1": {"supporting_figures": ["Extended Data Fig. 4"], "page_numbers": [11]}, "2": {"supporting_figures": ["Extended Data Fig. 3"], "page_numbers": [10]}, "3": {"supporting_figures": ["Fig. 1c", "Fig. 1d"], "page_numbers": [2, 2]}, "4": {"supporting_figures": ["Fig. 3b", "Fig. 3e", "Extended Data Fig. 5"], "page_number... | A2 | judge | [
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"images/SciDocBench/db/dbb5d... | ||
326 | 分析提供的量子计算综述论文，并识别论文的分类体系中明确分类的所有被引用研究工作。从以下维度分配分类标签：
维度 1: QKD_Type
- Discrete_Continuous
- COW
- DPS
- Six_State
- Decoy_State
维度 2: PQC_Type
- Lattice_based
- Code_based
- Multivariate
- Isogeny_based
- Hybrid
维度 3: Hardware_Platform
- Trapped_Ion
- Superconducting
- Silicon
- Photonic
- Topological
输出格式：
仅返回一个 J... | {"Ghalaii et al.(2020)": ["QKD_Type:Discrete_Continuous"], "Valivarthi et al.(2020)": ["QKD_Type:Discrete_Continuous"], "Leverrier and Grangier(2009)": ["QKD_Type:Discrete_Continuous"], "Li et al.(2018)": ["QKD_Type:Discrete_Continuous"], "Lin et al.(2019)": ["QKD_Type:Discrete_Continuous"], "Ruan et al.(2019)": ["QKD_... | A3 | json_match | [
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"images/SciDocBench/6a/6af97... | ||
327 | 你是一名化学助手。请根据以下要求，写出研究论文中 scheme 2 的有机化合物制备路线/步骤中的完整化学反应方程式：
1. 化学反应方程式必须包括反应产物和反应条件
2. 分子式必须以元素形式完全展开，例如，$\mathrm{C_6H_{12}O_6}$
3. 文本中提到的所有中间过程都必须详细包含
4. 反应方程式不需要遵循顺序/时间顺序；你可以自由安排
格式要求：
1. 所有化学方程式严格使用 LaTeX 格式
2. 使用 `-` 表示无序列表项（项目符号），每行只包含一个方程式
3. 每行应只包含一个方程式
示例回答：
- $\mathrm{H_2O(l)} \longrightarrow \mathrm{H_2(... | "- $\\mathrm{C_3H_7NO_2\\,(7)} + \\mathrm{C_{10}H_{18}O_5\\,(Boc_2O)} \\xrightarrow[\\text{THF/H}_2\\text{O (1:2), 4°C to rt, 16\\,h}]{\\mathrm{Na_2CO_3}} \\mathrm{C_8H_{15}NO_4\\,(8)} + \\mathrm{C_4H_{10}O\\,(t\\text{-}BuOH)} + \\mathrm{CO_2} \\quad 75\\%$\n\n- $\\mathrm{C_8H_{15}NO_4\\,(8)} + \\mathrm{C_4H_{10}ClNO_3... | A5 | judge | [
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"images/SciDocBench/d9/d90c6... | ||
328 | 根据文档，回答以下问题。
经过 20 个 epochs 后，在第 5 层的表示空间中，以下四个类别：airplane, automobile, ship, and truck 中，哪个类别的边界仍然相对模糊？
按以下格式回答：
{"Category": "<e.g. Airplane, automobile>"} | {"Category": "airplane"} | A5 | json_match | [
"25/25977f25d76158f527b825d69c07d2c6862e2a059c44758c934fca9b353dc6b9.jpg",
"1a/1aa17eff9a86c5ba481f2c3566dc87f8ce38ff587eeda06a6d0a329c98559eb6.jpg",
"0e/0e6c69a37b5ac056acfe4b900570666365e2968f736bafd2cdf15d016e1098df.jpg"
] | np_a5_05_001__zh_all_first | np_a5_05_001 | zh_all_first | all_first | json | {} | np_a5_05 | scientific | zh | np_a5_05_001 | [
"images/SciDocBench/25/25977f25d76158f527b825d69c07d2c6862e2a059c44758c934fca9b353dc6b9.jpg",
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"images/SciDocBench/0e/0e6c69a37b5ac056acfe4b900570666365e2968f736bafd2cdf15d016e1098df.jpg"
] | ||
329 | 关于这篇文章，以下陈述是正确的：
A. 从 Fig. 3 可以观察到，为皮带设计了一个可调滑轮以调节其张力。
B. Fig. 4 中的物理模型假设电缆力 F(cable) 与杆位于同一平面内，且该力平行于地面。
C. Fig. 6 表明电缆锚定模块的旋转可以被主动控制。
D. 在 Fig. 7 中，紫色表示控制信号线的连接顺序，红色表示电源连接，绿色表示传感器信号线连接。
E. Fig. 10 显示，在 100 N 的负载下，Nylon cable 的误差可以保持在约 5% 以内。
F. Fig. 12 显示机器人运动过程中没有负载发生。
请按以下格式作答：
{"Choice":"<e.g. A,B,C,D,E>"(use comm... | {"Choice": "B,E,F"} | A5 | json_match | [
"b6/b689f41d99e353a32fe76dba5eeaf8870a2c5adca88c85d4802ab9551c4aec3d.jpg",
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"images/SciDocBench/46/46ed4... | ||
330 | 你是一名专业科学编辑和 LaTeX 排版师。
你的目标是为所提供的研究论文生成一个“符号索引”（Index of Notations）。仅涵盖正文中引入的主要数学符号——不要包括 Appendix A 或任何其他附录中的符号。该表是示例性的而非穷尽性的：包括核心的架构和训练过程符号（数据、层索引、层输出、激活、核/特征图维度、批大小、掩码、集成大小、预测，以及 Packed-Ensembles 参数 $\alpha$, $\gamma$）。可以省略仅在单个证明或旁述中使用的临时符号。
输出要求：
请生成一个完整的 LaTeX 文件，其中包含一个 LaTeX 表格代码块。你可以使用 booktabs, amsmath, amsfont... | "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{amsmath, amssymb, amsfonts}\n\\usepackage{mathtools}\n\\begin{document}\n\n\\begin{table*}[!ht]\n\\renewcommand{\\figurename}{Table}\n\n\\caption{Index of Notations}\n\\label{table:notations}\n\\centering\n\\resizebox{\\textwidth}{!}\n{\n\\begin{tabular}{@... | B1 | judge | [
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"cf/cfcc... | np_b1_01_001__zh_all_first | np_b1_01_001 | zh_all_first | all_first | text | {} | np_b1_01 | scientific | zh | np_b1_01_001 | [
"images/SciDocBench/84/84a2993a0abd71e78d023d7f4e521b79cd54b587994accb33716654f538dd1c1.jpg",
"images/SciDocBench/11/11e3ef0cc4e07233ead245a1ce7910d03ee40663834df5ac96ba124067663d40.jpg",
"images/SciDocBench/5f/5fb5295428bf2fbffaf6c191cb35beb1f397f7a51019bc90fcdb62769f88ce83.jpg",
"images/SciDocBench/39/39173... | ||
331 | 根据 Figure 2(b)，回答以下问题：Figure 2(b) 中显示了多少个键中心？请按以下格式回答：
{"Number of bond centers": "<e.g. 3>", "Description": "<Explain why you derive this answer>"} | {"Number of bond centers": "6", "Description": "From the caption of this figure, we know that the orange dots are bond centers. In figure 2(b), there are 4 orange dots on the green lines in total, 1 in bottom-left and 1 in top-right. Therefore, the number of bond centers is 6."} | B2 | judge | [
"9e/9e6b2c02af648f4041b35abc89ae2a13c86d8be999472bf515240804e31d67d1.jpg",
"b2/b2497ab2325c6b6e024cdde62a11cef83615ae58088d738ef8fa853343ce8dfa.jpg",
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"11/11baae7d537bc9a726c9a2c4bd8313c05525fcb9274821c50ac011448bc9c87a.jpg",
"80/8085... | np_b2_02_001__zh_all_first | np_b2_02_001 | zh_all_first | all_first | json | {} | np_b2_02 | scientific | zh | np_b2_02_001 | [
"images/SciDocBench/9e/9e6b2c02af648f4041b35abc89ae2a13c86d8be999472bf515240804e31d67d1.jpg",
"images/SciDocBench/b2/b2497ab2325c6b6e024cdde62a11cef83615ae58088d738ef8fa853343ce8dfa.jpg",
"images/SciDocBench/1f/1fff7731d7e399dd3fc3f163cafc51d80a7afa51535a9cd92d55a7cf5e205b7c.jpg",
"images/SciDocBench/11/11baa... | ||
332 | 你是一名物理数据验证助手。请阅读所提供的论文，并对正文、图注以及任何嵌入的公式进行系统的数值和逻辑一致性检查。
你的任务是识别论文中所有数值、物理参数、实验条件或基于公式的预测在内部看起来不一致或相互矛盾的实例。不要将自己限制在预定义的主题列表内；请广泛扫描整个文档。
对于你发现的每一处不一致，请报告相互冲突的值及其来源位置，须与论文中出现的形式完全一致。
你的回答必须是具有以下结构的 JSON 数组的数组：
[
[
{
"value": "$<LaTeX formula or number>$",
"source": "<verbatim text from the paper or Fi... | [[{"value": "$n_s \\approx 3.2 \\times 10^{12}~\\mathrm{cm}^{-2}$", "source": "Figure 2a"}, {"value": "$-n_s/2 = -1.38 \\times 10^{12}~\\mathrm{cm}^{-2}$", "source": "Four-probe resistance $R_{xx}$ measured at densities corresponding to the region bounded by pink dashed lines in (a), versus temperature. Two superconduc... | C1 | judge | [
"b7/b78c343ac14fd269b65f5d6bbbe4c419a1e4eff60df0d03201f2e835e8741413.jpg",
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"1b/1be4... | np_c1_02_001__zh_all_first | np_c1_02_001 | zh_all_first | all_first | json | {} | np_c1_02 | scientific | zh | np_c1_02_001 | [
"images/SciDocBench/b7/b78c343ac14fd269b65f5d6bbbe4c419a1e4eff60df0d03201f2e835e8741413.jpg",
"images/SciDocBench/eb/eb64456f0186fba6c22a7e6d861303b8d739a8bffce52e8b12ed2ddfa0139db1.jpg",
"images/SciDocBench/ee/eeb1c63069f78e2652157df5b8dcdd82470423426196c7e6d237d5ec775d372c.jpg",
"images/SciDocBench/80/80fe7... | ||
333 | 通读整篇论文。仅使用这篇论文。不要解释。输出恰好一个 18 个字符的位串。当且仅当该断言得到论文完全支持时使用 1；否则使用 0。
A. 论文明确询问 Transformers 是否可以通过梯度下降学习 TC^0 中的简单逻辑函数。
B. 引言称，先前的复杂性理论结果已经解决了梯度下降下的训练问题。
C. 论文研究了一个简化的 Transformer，以及一个由二进制字符串及其多数标签配对组成的有限训练集。
D. 论文的主要贡献是一个严格的理论框架，为任何可微参数模型（包括 Transformers）在多项式样本或指数样本设置下给出泛化误差下界。
E. Theorem 4.1 是多项式样本困难性定理，而 Theorem 4.2 是... | "101111101011111010" | C1 | judge | [
"a2/a20d1a4d7a7a3e4f3bef194de8b854ded4f0474a38270345c2c04e81b2d3052c.jpg",
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"0b/0b87... | np_c1_05_001__zh_all_first | np_c1_05_001 | zh_all_first | all_first | text | {} | np_c1_05 | scientific | zh | np_c1_05_001 | [
"images/SciDocBench/a2/a20d1a4d7a7a3e4f3bef194de8b854ded4f0474a38270345c2c04e81b2d3052c.jpg",
"images/SciDocBench/71/71d8d7571614a39b0314e560bffad6133369367610b54d0fb0a247fd3a70d679.jpg",
"images/SciDocBench/b8/b8e931f3e5ffd3ff27c6ab4786d825f7e2cbf2a1df6b0a544d3454f03aa63d5c.jpg",
"images/SciDocBench/84/845cf... | ||
334 | 根据 Fig.3，回答以下问题。
对于 \chi_M'-频率曲线，描述斜率绝对值和拐点频率的变化趋势。 | As temperature increases from 30 K to 51 K, the absolute value of the slope decreases (the curve becomes more gradual), while the inflection point shifts to higher frequencies. | C3 | judge | [
"f4/f44efee4be07513f225b233d1857381a255367badc2b552b7834c9387023fcd0.jpg",
"1d/1d9f5694c346b83e5a8b68478aaacb59b63fdb0b5470b1d83d3f7262f63266d1.jpg",
"61/61f8fe026cfc6ea5062c587ab91531fb3879f34453dbad8ca7daecd0b7505557.jpg",
"c3/c3f89f2a14dce744140b0b7d1191d06283a0a2b196ca6e0d44ca64c3403e02cd.jpg",
"0c/0c84... | np_c3_04_001__zh_all_first | np_c3_04_001 | zh_all_first | all_first | text | {} | np_c3_04 | scientific | zh | np_c3_04_001 | [
"images/SciDocBench/f4/f44efee4be07513f225b233d1857381a255367badc2b552b7834c9387023fcd0.jpg",
"images/SciDocBench/1d/1d9f5694c346b83e5a8b68478aaacb59b63fdb0b5470b1d83d3f7262f63266d1.jpg",
"images/SciDocBench/61/61f8fe026cfc6ea5062c587ab91531fb3879f34453dbad8ca7daecd0b7505557.jpg",
"images/SciDocBench/c3/c3f89... | ||
335 | 根据论文中的图 1(b)，列出作为单独子图显示的三类社交互动。
请按以下 JSON 格式回答（三个字符串组成的数组，按照它们在图中出现的顺序）：
{"Categories": ["<category 1>", "<category 2>", "<category 3>"]} | {"Categories": ["Advice requests", "Work-related orders", "Social invitations"]} | A2 | json_match | [
"b6/b6a5fe9dee4707b62de68db118ece04a96320c1d28c7d2cfbda0d5d1a36a06a3.jpg",
"08/08bd7fe2c499b45289fa6bcf92d672b59519a6bb28048570ade18f74887b45ab.jpg",
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"17/17ff... | np_a2_02_001__zh_all_first | np_a2_02_001 | zh_all_first | all_first | json | {} | np_a2_02 | scientific | zh | np_a2_02_001 | [
"images/SciDocBench/b6/b6a5fe9dee4707b62de68db118ece04a96320c1d28c7d2cfbda0d5d1a36a06a3.jpg",
"images/SciDocBench/08/08bd7fe2c499b45289fa6bcf92d672b59519a6bb28048570ade18f74887b45ab.jpg",
"images/SciDocBench/38/383b4abb4c6a9e2b6160c67a539ddf9d116bdc09beedae5473f5ce146a33fbb4.jpg",
"images/SciDocBench/41/41358... | ||
336 | 你是一个用于生物学文章分析的 AI。基于所提供的页面，仅列出这些工作中明确被命名为使用的顶层数据库。使用规范的短名称（例如 "UniProt", "ClinVar", "ProteinGym", "gnomAD"）。不要列出子数据集，不要杜撰数据库，也不要包含仅仅顺带引用的资源。
输出一个按字母顺序排序的字符串 JSON 列表：
["<DB_1>", "<DB_2>", ...]
将该列表放在键 "answer_list" 下，因此最终输出为：
{"answer_list": [...]} | {"answer_list": ["ClinVar", "ProteinGym", "UniProt", "gnomAD"]} | A4 | json_match | [
"ba/ba93306680b9d682019e0a14b016f75ab371a742c28a3f8693eee67b6369709e.jpg",
"a3/a31e907f55ced28e90770031674cf71233332eb2812ac551c4e3bfa87d510ee6.jpg",
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"2a/2a3a... | np_a4_02_001__zh_all_first | np_a4_02_001 | zh_all_first | all_first | json | {} | np_a4_02 | scientific | zh | np_a4_02_001 | [
"images/SciDocBench/ba/ba93306680b9d682019e0a14b016f75ab371a742c28a3f8693eee67b6369709e.jpg",
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"images/SciDocBench/26/268b15c95fa8fb0d9a6e0ec47e76dd95d85de5b9644bcb1bb851db1c43a862a6.jpg",
"images/SciDocBench/28/28164... | ||
337 | 查看 Figure 2 中将定量测量值相对于变量 "Days on CLZ" 作图的面板（例如，按天数分组的箱线图 / 条形图）。对于 x 轴上出现的每个 "Days on CLZ" 值，计算该组显著性注释上方显示的星号（asterisks/stars）数量（如果有）。
对于每个 x 轴组：
- 确定 CLZ 上的天数。
- 计算该组上方的星号符号数量（如果没有则为 0）。
按 "Days on CLZ" 递增顺序，每组输出一行 JSON：
{"Days on CLZ": "<e.g. 1>", "Star_count": "<e.g. 0, 1, 2, 3>"}
{"Days on CLZ": "<e.g. 2>", "Sta... | "{\"Days on CLZ\": \"1\", \"Star_count\": \"0\"}\n{\"Days on CLZ\": \"2\", \"Star_count\": \"3\"}\n{\"Days on CLZ\": \"3\", \"Star_count\": \"3\"}\n{\"Days on CLZ\": \"5\", \"Star_count\": \"3\"}\n{\"Days on CLZ\": \"7\", \"Star_count\": \"3\"}" | A5 | judge | [
"1e/1e9b75a61e0078f8e1aacfe82ff78110fd57f2a57a4f9bc891de5b990f0dbf01.jpg"
] | np_a5_03_001__zh_all_first | np_a5_03_001 | zh_all_first | all_first | text | {} | np_a5_03 | scientific | zh | np_a5_03_001 | [
"images/SciDocBench/1e/1e9b75a61e0078f8e1aacfe82ff78110fd57f2a57a4f9bc891de5b990f0dbf01.jpg"
] | ||
338 | 根据图 1，在所示能量范围内，是否存在任何能量区间，其中 prompt signals 的计数少于 delay signals 的计数？回答 "Yes" 或 "No"。
Output format:
{"Has_Interval_Where_Prompt_Less_Than_Delay": "<Yes/No>"} | {"Has_Interval_Where_Prompt_Less_Than_Delay": "No"} | A5 | json_match | [
"63/6343627fa261d841c4bb09371f0af6c1eaff19401d992aff450fdd4f36952560.jpg",
"7e/7edd31db926e69c3a9328481500b1e1ff5a4ede8f19423659b7c559251348d3b.jpg",
"ba/ba4e2d8d94bdf9ee89d56f938e7792af0c43b7100c2035b6841c0273f3929433.jpg",
"23/2319c10b940f0d49aeaf0bca0bcb3a8a8a499bf323e238c57197a291bb87dbd5.jpg",
"10/1083... | np_a5_04_001__zh_all_first | np_a5_04_001 | zh_all_first | all_first | json | {} | np_a5_04 | scientific | zh | np_a5_04_001 | [
"images/SciDocBench/63/6343627fa261d841c4bb09371f0af6c1eaff19401d992aff450fdd4f36952560.jpg",
"images/SciDocBench/7e/7edd31db926e69c3a9328481500b1e1ff5a4ede8f19423659b7c559251348d3b.jpg",
"images/SciDocBench/ba/ba4e2d8d94bdf9ee89d56f938e7792af0c43b7100c2035b6841c0273f3929433.jpg",
"images/SciDocBench/23/2319c... | ||
339 | 关于这篇文章，以下陈述是正确的：
A. 图 1 仅用于说明张拉整体艺术与折纸艺术之间的关系。
B. 图 2 中的红色标签表示节点的索引。
C. 在图 3 中，对于 Miura 折纸单胞，Order 2 和 Order 3 的特征值相差超过一百万倍，这是由于变形模式发生了变化。
D. 图 4 表明，除少数特殊情况外，在 Miura 折纸单胞中，杆件相对于铰链的刚度贡献比约为 1:2。
E. 图 5 表明，在 Miura 折纸单胞的大变形分析过程中，x、y 和 z 方向的刚度呈现相同的变化趋势。
F. 图 10 表明，对于缆索驱动的 Kresling 结构，X 和 Z 方向的刚度同时达到最大值。在该构型下，对于相同大小的长度变化（无论是... | {"Choice": "C"} | A5 | json_match | [
"a8/a8fef1b99279556303e79a058ef899f2611c1df0a3dcb8133c4b0aeabf73efb4.jpg",
"c2/c22b3c3f1b1b7280e5a091bda44d97d5f978a43c1dd965e93a7c2df644a282eb.jpg",
"2e/2e1a21e813cd214722e0d447626690c694dd97f948d332c64824c52ff9b86630.jpg",
"e2/e2cedc653c69a66810dc3ea4bb953ec5d02ad4768241d54677fb95b1d2cea960.jpg",
"98/9881... | np_a5_06_001__zh_all_first | np_a5_06_001 | zh_all_first | all_first | json | {} | np_a5_06 | scientific | zh | np_a5_06_001 | [
"images/SciDocBench/a8/a8fef1b99279556303e79a058ef899f2611c1df0a3dcb8133c4b0aeabf73efb4.jpg",
"images/SciDocBench/c2/c22b3c3f1b1b7280e5a091bda44d97d5f978a43c1dd965e93a7c2df644a282eb.jpg",
"images/SciDocBench/2e/2e1a21e813cd214722e0d447626690c694dd97f948d332c64824c52ff9b86630.jpg",
"images/SciDocBench/e2/e2ced... | ||
340 | 我是一名张拉整体机器人的初学者，最近读到一段讨论具有双重运行模式的张拉整体机构的文字。我特别感兴趣的是文章中提到的所谓 “FCC” 算法。请你说明论文中是如何介绍 “FCC” 及其结果的？
A. 从头到尾进行了全面介绍，包括其基本原理。
B. 结合参考文献作了简要解释，并且只有少量描述性陈述。
C. 文章提供了明确的公式，可在无需额外推导的情况下直接评估姿态有效性。
D. 没有提供详细的方程或不等式；相反，该方法是以定性方式描述的。
E. 所有结果都呈现在三维坐标系中。
F. 根据图 4，在模拟 FCC 结果时，蓝色所示的结构固定在地面上。
请按以下格式回答：
{"Choice":"<e.g. A,B,C,D,E>"(使用逗号分隔选... | {"Choice": "B"} | B2 | json_match | [
"e4/e4cefea666faaf908907299f51aa2b584a2101312dbe460a1bfb70a38ed10767.jpg",
"7b/7bbad53e5e076b04176c878240149ed778048bd2cbeb86f7139b24a5e05ecad3.jpg",
"93/93d2ab91c1873071190cc043829d91cd892a45cf65cde74de808b0291adf563c.jpg",
"95/95a4343921bf90d389107a8542a5f09d92c5f8a0c9cd7dcd557ea07bbaf03ac6.jpg",
"14/14ff... | np_b2_04_001__zh_all_first | np_b2_04_001 | zh_all_first | all_first | json | {} | np_b2_04 | scientific | zh | np_b2_04_001 | [
"images/SciDocBench/e4/e4cefea666faaf908907299f51aa2b584a2101312dbe460a1bfb70a38ed10767.jpg",
"images/SciDocBench/7b/7bbad53e5e076b04176c878240149ed778048bd2cbeb86f7139b24a5e05ecad3.jpg",
"images/SciDocBench/93/93d2ab91c1873071190cc043829d91cd892a45cf65cde74de808b0291adf563c.jpg",
"images/SciDocBench/95/95a43... | ||
341 | 关于这篇文章，以下陈述是正确的：
A. 图 3 显示，该机器的顺时针旋转速度超过逆时针旋转速度的 1.5 倍以上。
B. 从图 4 (D) 可以清楚地观察到，该机器从水平杆下方通过。
C. 文章没有对抗冲击机制进行详细的理论分析；相反，它仅通过实验展示了机器的抗冲击能力。
D. 在运动实验和抗冲击测试中使用的端盖在材料和结构尺寸上均不同。这表明该研究可能无法同时实现“surviving harsh impacts from drops (at least 5.7 m)”和“achieving high locomotion speeds (18 bar lengths per minute)”。
E. 论文对该机制的运动进行了详细分... | {"Choice": "A,B,D,F"} | B2 | json_match | [
"29/29021e9fa17d38eecb43b4de5d83c26f901d286f7b4a657cf5cda9001a0ccef5.jpg",
"0d/0dc585d604f35ea9fdd157b842bbc0e38b1ac57533938167a6ca8e075992bd19.jpg",
"d5/d54b434ffc8d272b85a9e99777fc8731bc4d2bacf44b31ed134609860105cce1.jpg",
"46/466caaf647d0376d9c60361a6767f5660d9aa41789e6f3c859ceb90a88b7a3ad.jpg",
"7a/7abc... | np_b2_05_001__zh_all_first | np_b2_05_001 | zh_all_first | all_first | json | {} | np_b2_05 | scientific | zh | np_b2_05_001 | [
"images/SciDocBench/29/29021e9fa17d38eecb43b4de5d83c26f901d286f7b4a657cf5cda9001a0ccef5.jpg",
"images/SciDocBench/0d/0dc585d604f35ea9fdd157b842bbc0e38b1ac57533938167a6ca8e075992bd19.jpg",
"images/SciDocBench/d5/d54b434ffc8d272b85a9e99777fc8731bc4d2bacf44b31ed134609860105cce1.jpg",
"images/SciDocBench/46/466ca... | ||
342 | 在我提供的文档中，Figure 1 右侧有一个雷达图。你的任务是在该图中找出一个错误。
图表描述：该图是使用 Python 的 matplotlib 包生成的，所有实验数据随后都标注在图上。已知所有数据标注都是正确的；然而，在 matplotlib 绘图过程中，有一个数值被错误设置，导致某条曲线在某个特定 benchmark 上出现在了错误的位置。
请按照以下步骤：
1. 识别图表中的绘图错误。
2. 识别与该错误对应的已标注数值。
3. 识别与该错误对应的 benchmark 和 model。
4. 按照以下精确格式将你的答案作为 JSON 字典返回：
{"value": "<The corresponding numerica... | {"value": "63.3", "model": "Qwen-VL-Chat", "benchmark": "GQA"} | C2 | json_match | [
"e2/e2da6c79a6f543c0f49b40da603be045826cfe378bb16c10c7364f4be85dc9fe.jpg",
"d7/d74776e82727d11d80b6541aee9e466f43ccdc47ccaeb9c53eed5c0ee7d15325.jpg",
"d4/d411690c77a42d773f3c3ad121d1cf878b0d5a57f7b9ffa08b70a17db480c230.jpg",
"ec/ec9b760890cb7c42e849537075388546f620da431c7e764cce45c1090e9c0085.jpg",
"f6/f66f... | np_c2_02_001__zh_all_first | np_c2_02_001 | zh_all_first | all_first | json | {} | np_c2_02 | scientific | zh | np_c2_02_001 | [
"images/SciDocBench/e2/e2da6c79a6f543c0f49b40da603be045826cfe378bb16c10c7364f4be85dc9fe.jpg",
"images/SciDocBench/d7/d74776e82727d11d80b6541aee9e466f43ccdc47ccaeb9c53eed5c0ee7d15325.jpg",
"images/SciDocBench/d4/d411690c77a42d773f3c3ad121d1cf878b0d5a57f7b9ffa08b70a17db480c230.jpg",
"images/SciDocBench/ec/ec9b7... | ||
343 | 关于这篇文章，以下陈述是正确的：
A. 图 1 仅用于说明概念起源，与论文后续提出的理论推导无关。
B. 根据图 3 和图 4，半圆形梁表现出最差的拉伸–压缩对称性。
C. 在图 7 中，蓝色结构提供稳定的正刚度，而红色结构在一定范围内表现出负刚度。
D. 在图 B2 中，Force > 0 表示该结构受到拉伸载荷。
E. 图 2(e) 所示的结构在论文中得到了实验验证。
F. 附录 C 表明，结构常数 Q 的增加会导致 NS 区域的跨度和力幅值均增长。
按以下格式作答：
{"Choice":"<e.g. A,B,C,D,E>"(use comma to seperate the options), "Reason":"<Expla... | {"Choice": "B,D"} | C3 | json_match | [
"21/216e146a8eb0798a4882cc569b15a11d7c53e9f7cb38af950b049502dd923b3b.jpg",
"76/762300d9b0a9006a727720e9ec5588833ffc31584f45e024cda9ab969953837f.jpg",
"8d/8d9e5d593a5631b95714159d3297fc9626bf8a84273d245270d7a82ca7f52ca5.jpg",
"c0/c01a6e3e052384e3ca291ec0160e5d592edc633a3f91a6463ed68eeb12cb9849.jpg",
"9d/9d36... | np_c3_05_001__zh_all_first | np_c3_05_001 | zh_all_first | all_first | json | {} | np_c3_05 | scientific | zh | np_c3_05_001 | [
"images/SciDocBench/21/216e146a8eb0798a4882cc569b15a11d7c53e9f7cb38af950b049502dd923b3b.jpg",
"images/SciDocBench/76/762300d9b0a9006a727720e9ec5588833ffc31584f45e024cda9ab969953837f.jpg",
"images/SciDocBench/8d/8d9e5d593a5631b95714159d3297fc9626bf8a84273d245270d7a82ca7f52ca5.jpg",
"images/SciDocBench/c0/c01a6... | ||
344 | 你是一个有帮助的 AI Article Reader。第一个 PDF 是 "Strategies for Pre-Training Graph Neural Networks"（基线论文）。仅基于该基线论文，列出作为 pretrain GNN 变体进行评估的 GNN 模型架构。输出一个按字母顺序排序的模型名称 JSON 列表。
示例输出：
["GAT", "GCN", "GIN", "GraphSAGE"]
将该列表包装在键 "answer_list" 下，因此最终输出为：
{"answer_list": [...]} | {"answer_list": ["GAT", "GCN", "GIN", "GraphSAGE"]} | D2 | json_match | [
"f9/f9ae61614c8705153c27f727a6247a3b438e5896ec3454f68bb7fcd46051cbd1.jpg",
"c1/c19c11f814fa446e92a9b83e5053b6138782b08b59aa0b3f05ebcd9dd20e2d7b.jpg",
"b9/b94ae4b0f8c8a757490657080992934b8e757e8bb63198b71621d4770575b3f6.jpg",
"44/444eb67fcd821a6695acc9dcae96802ef44b9dd67fa15aa51f30745dcdcdfccd.jpg",
"d9/d991... | np_d2_01_001__zh_all_first | np_d2_01_001 | zh_all_first | all_first | json | {} | np_d2_01 | scientific | zh | np_d2_01_001 | [
"images/SciDocBench/f9/f9ae61614c8705153c27f727a6247a3b438e5896ec3454f68bb7fcd46051cbd1.jpg",
"images/SciDocBench/c1/c19c11f814fa446e92a9b83e5053b6138782b08b59aa0b3f05ebcd9dd20e2d7b.jpg",
"images/SciDocBench/b9/b94ae4b0f8c8a757490657080992934b8e757e8bb63198b71621d4770575b3f6.jpg",
"images/SciDocBench/44/444eb... | ||
345 | 你是一个用于生物学文章分析的 AI。基于所提供的页面，列出这些工作中明确被命名为使用过的顶层数据库。使用规范简称。不要列出子数据集或编造数据库。包括任何被明确描述为这些工作中所使用的数据来源的数据库（涵盖蛋白质序列/结构数据库、变异数据库、PTM 数据库、抗菌肽数据库、生物样本库等）。
输出一个按字母顺序排序（不区分大小写）的字符串 JSON 列表：
["<DB_1>", "<DB_2>", ...]
将该列表包装在键 "answer_list" 下，因此最终输出为：
{"answer_list": [...]} | {"answer_list": ["ActiveDriverDB", "ClinVar", "Genebass", "PhosphoSitePlus", "PTMint", "ProteinGym", "UniProt", "dbPTM", "gnomAD"]} | D2 | json_match | [
"a9/a9681eb57ca7dd3e746316e96ee2dee21574dfc51b840f6c73e2636162a03db4.jpg",
"cf/cf73e3a7398fe594feafeb9d84d85a07cb072739110c6237d1f74970640d5f65.jpg",
"bc/bcaf1f284a8d83056a2ba951679a9d86a1f35dc93c237b0f74a278c01a44ac6d.jpg",
"b0/b05f786288b34c51a93472ac2db18e38135d46fb3f60b837fd9bdc57cecd455b.jpg",
"81/8117... | np_d2_02_001__zh_all_first | np_d2_02_001 | zh_all_first | all_first | json | {} | np_d2_02 | scientific | zh | np_d2_02_001 | [
"images/SciDocBench/a9/a9681eb57ca7dd3e746316e96ee2dee21574dfc51b840f6c73e2636162a03db4.jpg",
"images/SciDocBench/cf/cf73e3a7398fe594feafeb9d84d85a07cb072739110c6237d1f74970640d5f65.jpg",
"images/SciDocBench/bc/bcaf1f284a8d83056a2ba951679a9d86a1f35dc93c237b0f74a278c01a44ac6d.jpg",
"images/SciDocBench/b0/b05f7... | ||
346 | 给定两篇关于在 caption 数据集 ScaleCap-450k 和 CapRL-1M 上训练 Qwen2.5-VL-7B 的科学 PDF 文档。列出同时出现在这两个模型的两篇文档主评估表中的 benchmark 名称。按字母顺序排序（不区分大小写）。使用 ScaleCap 文档中显示的大小写形式。
输出一个 benchmark 名称的 JSON 列表：
["<Benchmark_1>", "<Benchmark_2>", ...]
将该列表包装在键 "answer_list" 下，因此最终输出为：
{"answer_list": [...]} | {"answer_list": ["ChartQA", "InfoVQA", "MMStar", "MMVet"]} | D3 | json_match | [
"d5/d5f6862e6baf1735fe956d4d908e94f4abacaeba8c781dfbc170ecb95d8c2473.jpg",
"2c/2c97680f92f3814a81b7b48f929c77cfa93887b353cea8743c1f7f554b8ece69.jpg"
] | np_d3_01_001__zh_all_first | np_d3_01_001 | zh_all_first | all_first | json | {} | np_d3_01 | scientific | zh | np_d3_01_001 | [
"images/SciDocBench/d5/d5f6862e6baf1735fe956d4d908e94f4abacaeba8c781dfbc170ecb95d8c2473.jpg",
"images/SciDocBench/2c/2c97680f92f3814a81b7b48f929c77cfa93887b353cea8743c1f7f554b8ece69.jpg"
] | ||
347 | 你是一名化学论文阅读助手。提供的 PDFs 是关于 NCM-811 正极材料及其循环稳定性的研究文章。在所有提供的文章中，找出在恰好 500 次循环时报告的 NCM-811（或其改性形式）的最高容量保持率百分比。报告该数值以及该测试的电压窗口。
按以下精确格式输出 JSON：
{
"highest_retention_at_500_cycles_pct": <number>,
"voltage_window": {"lower_V": <number>, "upper_V": <number>}
}
规则：
- 仅使用所提供页面中可见的内容。
- 如果没有测试报告 NCM-811 在恰好 500 次循环时的数据，则输出 ... | {"highest_retention_at_500_cycles_pct": null, "voltage_window": null} | D3 | json_match | [
"b1/b1f25586b73974c730953b4864cc161fb686f5ff12a3817e69f645e59946e897.jpg",
"9f/9f302c12dba7af268f2a823212e0dab04ea6006308c43764c853adcb4179bb06.jpg",
"a2/a2a1114410327bd05c35fa5900ae98d6eedfc99db1cc242bff716d9311c0275f.jpg",
"93/93fe75ca4980ed2e8bcc61e7e92165abc6f2ec68f4d226794b7c18cf1c8d5b31.jpg",
"81/8162... | np_d3_02_001__zh_all_first | np_d3_02_001 | zh_all_first | all_first | json | {} | np_d3_02 | scientific | zh | np_d3_02_001 | [
"images/SciDocBench/b1/b1f25586b73974c730953b4864cc161fb686f5ff12a3817e69f645e59946e897.jpg",
"images/SciDocBench/9f/9f302c12dba7af268f2a823212e0dab04ea6006308c43764c853adcb4179bb06.jpg",
"images/SciDocBench/a2/a2a1114410327bd05c35fa5900ae98d6eedfc99db1cc242bff716d9311c0275f.jpg",
"images/SciDocBench/93/93fe7... | ||
348 | 在论文的 Fig. 5b 中，展示了三层垂直排列的节点。列出中间层中各块的标签（从上到下）以及右侧层中各节点的标签（从上到下）。
按以下精确格式输出 JSON：
{
"middle_layer_top_to_bottom": ["<label_1>", "<label_2>", ...],
"right_layer_top_to_bottom": ["<label_1>", "<label_2>", ...]
} | {"middle_layer_top_to_bottom": ["Progenitor maintenance", "Haemoglobin synthesis", "Autophagy", "Cell cycle"], "right_layer_top_to_bottom": ["MCH", "RDW", "IRF"]} | E1 | json_match | [
"5d/5db90440f7514bc7320636a2cc788bd414674359f20f768b1bcf641aa4a75238.jpg"
] | np_e1_01_001__zh_all_first | np_e1_01_001 | zh_all_first | all_first | json | {} | np_e1_01 | scientific | zh | np_e1_01_001 | [
"images/SciDocBench/5d/5db90440f7514bc7320636a2cc788bd414674359f20f768b1bcf641aa4a75238.jpg"
] | ||
349 | 你是一名 AI 生物信息学研究助手。以下是文章的文件结构树：
```
├── 01_identify_mutations
│ ├── 140gene_fasta_new_8species.R
│ ├── TBXT_8species_03.csv
│ ├── TBXT_new_8species.fasta
│ ├── forloop_python.sh
│ ├── gene140_location.csv
│ └── mutation.py
├── 02_define_mutations
│ ├── inputs
│ ├── mutation_classifier.R
│ └── outputs
... | {
"question_1": {
"script_name": "mutation.py",
"folder_path": "01_identify_mutations"
},
"question_2": {
"dna_analysis_chain": [
"01_identify_mutations/mutation.py",
"02_define_mutations/mutation_classifier.R",
"04_filter_vep_results/filter_vep_visualization.R"
]
}
} | F2 | json_match | [
"e0/e01276491fb6ebfa941e7808899b4dae686eb47101a8e147f2272fe24064279b.jpg",
"95/95f30d4e0c789dc89a4ad36778c2074adda957e2a9996f43c2d1c024d0648bf3.jpg",
"40/406417ab825e05d7aab43d03ee67569a0d236781f274894a5ff94c5005b2277c.jpg",
"2b/2b7afc2c375431bd256505f9ab07e662f593f0a5886ed9a7df2e995e71cd0561.jpg",
"19/190a... | np_f2_01_001__zh_all_first | np_f2_01_001 | zh_all_first | all_first | json | {} | np_f2_01 | scientific | zh | np_f2_01_001 | [
"images/SciDocBench/e0/e01276491fb6ebfa941e7808899b4dae686eb47101a8e147f2272fe24064279b.jpg",
"images/SciDocBench/95/95f30d4e0c789dc89a4ad36778c2074adda957e2a9996f43c2d1c024d0648bf3.jpg",
"images/SciDocBench/40/406417ab825e05d7aab43d03ee67569a0d236781f274894a5ff94c5005b2277c.jpg",
"images/SciDocBench/2b/2b7af... | ||
350 | 分析所提供的量子计算综述论文，并识别论文的分类体系中明确分类的所有被引用研究工作。根据以下维度分配分类标签：
Dimension 1: Basic_Characteristics
- QI
- CQCT
- PM
Dimension 2: Algorithmic_Characteristics
- P
- ACQA
- T
- TLG
- QO
Dimension 3: Time_and_Gate_Characteristics
- DT
- MT
Dimension 4: Other_Characteristics
- S
- TCGLQ
输出格式：
仅返回一个 JSON 对象，其中：
- 键：格式为 "Author ... | {
"Weitenberg et al.(2011)": [
"Basic_Characteristics:QI",
"Basic_Characteristics:CQCT",
"Basic_Characteristics:PM",
"Algorithmic_Characteristics:P",
"Algorithmic_Characteristics:ACQA",
"Algorithmic_Characteristics:TLG",
"Time_and_Gate_Characteristics:DT",
"Other_Characteristics:TCGLQ"... | A3 | judge | [
"a6/a66a8a481f38581d2d63fc5a464508a84969e96f81b4088f0e23029a71c63839.jpg",
"12/12b93964112067f4e7f84a3df3688b0caec170734cbb36da9adc1c62f475b6e0.jpg",
"60/604d315e0af033f871559dccdc6fe08e4810bbb35fa06e9718aa2726ee9ae479.jpg",
"6a/6af97092ffea30b1058acfb8d7541fd2ae56be6f9aef1b24fc8845caf7e51870.jpg",
"0d/0d00... | np_a3_01_002__zh_all_first | np_a3_01_002 | zh_all_first | all_first | json | You are an expert evaluator the model's response to a question. I will provide you with the ground-truth and the model's prediction. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
1. The initial score is set as 0.0.
2. Format score (... | {} | np_a3_01 | scientific | zh | np_a3_01_002 | [
"images/SciDocBench/a6/a66a8a481f38581d2d63fc5a464508a84969e96f81b4088f0e23029a71c63839.jpg",
"images/SciDocBench/12/12b93964112067f4e7f84a3df3688b0caec170734cbb36da9adc1c62f475b6e0.jpg",
"images/SciDocBench/60/604d315e0af033f871559dccdc6fe08e4810bbb35fa06e9718aa2726ee9ae479.jpg",
"images/SciDocBench/6a/6af97... | |
351 | 根据文档，回答以下问题。
在 Figure 1 中，虚线和实线分别表示什么？ | The solid line represents the activations passed to the next layer, and the dashed line represents the activations passed to the reverse network. | A5 | judge | [
"25/25977f25d76158f527b825d69c07d2c6862e2a059c44758c934fca9b353dc6b9.jpg",
"1a/1aa17eff9a86c5ba481f2c3566dc87f8ce38ff587eeda06a6d0a329c98559eb6.jpg",
"0e/0e6c69a37b5ac056acfe4b900570666365e2968f736bafd2cdf15d016e1098df.jpg"
] | np_a5_05_002__zh_all_first | np_a5_05_002 | zh_all_first | all_first | text | {} | np_a5_05 | scientific | zh | np_a5_05_002 | [
"images/SciDocBench/25/25977f25d76158f527b825d69c07d2c6862e2a059c44758c934fca9b353dc6b9.jpg",
"images/SciDocBench/1a/1aa17eff9a86c5ba481f2c3566dc87f8ce38ff587eeda06a6d0a329c98559eb6.jpg",
"images/SciDocBench/0e/0e6c69a37b5ac056acfe4b900570666365e2968f736bafd2cdf15d016e1098df.jpg"
] | ||
352 | 根据 Fig.3，回答以下问题。
1. 对于 \chi_M''-频率曲线，描述峰值坐标的变化趋势。（在 'Figure 3(b) curve' 字段中回答）
2. 确定 51K 时 \chi_M''-频率曲线峰值的频率。（给出精确数字）
按以下格式作答：
{"Figure 3(b) curve": "...", "Peak at 51K": "<e.g. 0.001 Hz>"} | {
"Figure 3(b) curve": "As temperature increases from 30 K to 51 K, the peak shifts to higher frequencies (moves rightward on the frequency axis) and the peak intensity decreases.",
"Peak at 51K": "2000 Hz"
} | C3 | judge | [
"f4/f44efee4be07513f225b233d1857381a255367badc2b552b7834c9387023fcd0.jpg",
"1d/1d9f5694c346b83e5a8b68478aaacb59b63fdb0b5470b1d83d3f7262f63266d1.jpg",
"61/61f8fe026cfc6ea5062c587ab91531fb3879f34453dbad8ca7daecd0b7505557.jpg",
"c3/c3f89f2a14dce744140b0b7d1191d06283a0a2b196ca6e0d44ca64c3403e02cd.jpg",
"0c/0c84... | np_c3_04_002__zh_all_first | np_c3_04_002 | zh_all_first | all_first | json | You are an expert evaluator the model's response to a question. I will provide you with the ground-truth and the model's prediction. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
1. If the model correctly answers '2000Hz' for 'Peak a... | {} | np_c3_04 | scientific | zh | np_c3_04_002 | [
"images/SciDocBench/f4/f44efee4be07513f225b233d1857381a255367badc2b552b7834c9387023fcd0.jpg",
"images/SciDocBench/1d/1d9f5694c346b83e5a8b68478aaacb59b63fdb0b5470b1d83d3f7262f63266d1.jpg",
"images/SciDocBench/61/61f8fe026cfc6ea5062c587ab91531fb3879f34453dbad8ca7daecd0b7505557.jpg",
"images/SciDocBench/c3/c3f89... | |
353 | 以下是关于一组人之间互动的描述。基于 figure 1(b)，哪个子图最能描述他们的互动类型？该子图中人物与图标之间可能对应的关系是什么？
你需要首先定位哪个子图表示这组人之间的互动类型。然后你需要从四个选项中选择所有可能正确的对应关系。（例如 Andy-Pink，表示 Andy 可以是子图中的粉色人物）
Group:
Peter always initiate interaction with Emma, while Emma more often rejects his requests. Sofia tends to start interaction with Emma, but Emma rejects it ofte... | {"Subfigure": "Advice requests", "Choice": "C,D"} | A2 | judge | [
"b6/b6a5fe9dee4707b62de68db118ece04a96320c1d28c7d2cfbda0d5d1a36a06a3.jpg",
"08/08bd7fe2c499b45289fa6bcf92d672b59519a6bb28048570ade18f74887b45ab.jpg",
"38/383b4abb4c6a9e2b6160c67a539ddf9d116bdc09beedae5473f5ce146a33fbb4.jpg",
"41/41358b9c6b4f85e24d50d7bd5031d7a56a457caa50477ebcf4570f2bed0db59e.jpg",
"17/17ff... | np_a2_02_002__zh_all_first | np_a2_02_002 | zh_all_first | all_first | json | You are an expert evaluator the model's response to a question. I will provide you with the ground-truth and the model's prediction. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
1. If the model's predicted 'Subfigure' is correct, 0... | {} | np_a2_02 | scientific | zh | np_a2_02_002 | [
"images/SciDocBench/b6/b6a5fe9dee4707b62de68db118ece04a96320c1d28c7d2cfbda0d5d1a36a06a3.jpg",
"images/SciDocBench/08/08bd7fe2c499b45289fa6bcf92d672b59519a6bb28048570ade18f74887b45ab.jpg",
"images/SciDocBench/38/383b4abb4c6a9e2b6160c67a539ddf9d116bdc09beedae5473f5ce146a33fbb4.jpg",
"images/SciDocBench/41/41358... | |
354 | 你是一个生物学文章分析 AI。请告诉我这些工作中使用的所有数据库。
你的回答格式是：
{
<dataset_name> : [
<subdataset_name1>,
<subdataset_name2>,
...
],
...
}
请只给我 json | {
"UniProt": [
"Swiss-Prot (February 2022 release)",
"UniProtKB reviewed",
"UniRef",
"UniRef50",
"UniProt ID mapping server"
],
"ClinVar": [
"ProteinGym ClinVar benchmark",
"Balanced ClinVar (2,400 genes)",
"ClinVar 2025 release (March 2025)",
"ClinVar (142,951 variants subset ... | A4 | judge | [
"ba/ba93306680b9d682019e0a14b016f75ab371a742c28a3f8693eee67b6369709e.jpg",
"a3/a31e907f55ced28e90770031674cf71233332eb2812ac551c4e3bfa87d510ee6.jpg",
"26/268b15c95fa8fb0d9a6e0ec47e76dd95d85de5b9644bcb1bb851db1c43a862a6.jpg",
"28/281646ea0e2f60c035753e64607504b0e80940bb367969389b4c7a9039ecea9a.jpg",
"2a/2a3a... | np_a4_02_001_dup2__zh_all_first | np_a4_02_001_dup2 | zh_all_first | all_first | json | You are an expert evaluator the model's response to a question. I will provide you with the ground-truth and the model's prediction. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
1. The initial score is set as 0.0.
2. Format score (... | {} | np_a4_02 | scientific | zh | np_a4_02_001_dup2 | [
"images/SciDocBench/ba/ba93306680b9d682019e0a14b016f75ab371a742c28a3f8693eee67b6369709e.jpg",
"images/SciDocBench/a3/a31e907f55ced28e90770031674cf71233332eb2812ac551c4e3bfa87d510ee6.jpg",
"images/SciDocBench/26/268b15c95fa8fb0d9a6e0ec47e76dd95d85de5b9644bcb1bb851db1c43a862a6.jpg",
"images/SciDocBench/28/28164... | |
355 | 根据图 1，在哪些能量区间中，prompt signals 的计数少于 delay signals 的计数？ | 2 to 2.5 MeV and 9 to 10 Mev，approximately. | A5 | judge | [
"63/6343627fa261d841c4bb09371f0af6c1eaff19401d992aff450fdd4f36952560.jpg",
"7e/7edd31db926e69c3a9328481500b1e1ff5a4ede8f19423659b7c559251348d3b.jpg",
"ba/ba4e2d8d94bdf9ee89d56f938e7792af0c43b7100c2035b6841c0273f3929433.jpg",
"23/2319c10b940f0d49aeaf0bca0bcb3a8a8a499bf323e238c57197a291bb87dbd5.jpg",
"10/1083... | np_a5_04_002__zh_all_first | np_a5_04_002 | zh_all_first | all_first | text | {} | np_a5_04 | scientific | zh | np_a5_04_002 | [
"images/SciDocBench/63/6343627fa261d841c4bb09371f0af6c1eaff19401d992aff450fdd4f36952560.jpg",
"images/SciDocBench/7e/7edd31db926e69c3a9328481500b1e1ff5a4ede8f19423659b7c559251348d3b.jpg",
"images/SciDocBench/ba/ba4e2d8d94bdf9ee89d56f938e7792af0c43b7100c2035b6841c0273f3929433.jpg",
"images/SciDocBench/23/2319c... | ||
356 | 你是一个有帮助的 AI Artical Reader。请阅读以下论文，并总结不同 pretrain GNN 变体在分子任务数据集上的性能，使用所有策略取得的最佳分数，包括分数、准确率。
你需要阅读的论文是：
1. Strategies for Pre-Training Graph Neural Networks，以下简称 baseline
2. ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Prediction，以下简称 improve1
3. Molecular Contrastive Learning of Repres... | ```
| Model \ Dataset | BBBP | Tox21 | ToxCast | SIDER | ClinTox | MUV | HIV | BACE | ESOL | FreeSolv | Lipo | QM7 | QM8 | QM9 |
| --------------- | ------- | ------- | ---------------- | ------- | ------- | ------- | ------- | ------- | -------- | -------- | -------... | D2 | judge | [
"cf/cf79e42ce6022b26c2780234d5944a0d007c0aa20c250af1847f5075f72bdf70.jpg",
"2c/2cdea7a2c0e265a611a0eb1addcded657e9c439a9360151732a3a8cb2090e9d6.jpg",
"c6/c6e880807aa0b4ced9e305f90a897960868ed60dc6769a67197ca7c7a1403014.jpg",
"ca/ca066299c6e2f36c267152f21099a9c0b6cd005c895c9479d76e80e783eb2b40.jpg",
"0f/0f3a... | np_d2_01new_001__zh_all_first | np_d2_01new_001 | zh_all_first | all_first | text | You are an expert evaluator the model's response to a question. I will provide you with the question, the ground-truth and the model's prediction. You need to score the model's prediction.
Question: {prompt}
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
1. The initial scor... | {} | np_d2_01new | scientific | zh | np_d2_01new_001 | [
"images/SciDocBench/cf/cf79e42ce6022b26c2780234d5944a0d007c0aa20c250af1847f5075f72bdf70.jpg",
"images/SciDocBench/2c/2cdea7a2c0e265a611a0eb1addcded657e9c439a9360151732a3a8cb2090e9d6.jpg",
"images/SciDocBench/c6/c6e880807aa0b4ced9e305f90a897960868ed60dc6769a67197ca7c7a1403014.jpg",
"images/SciDocBench/ca/ca066... | |
357 | 你将获得两篇科学 PDF 文档。你的任务是总结在数据集 ScaleCap-450k 和 CapRL-1M 上训练 Qwen2.5-VL-7B 后得到的模型的性能。只保留两个文档中都出现的基准测试上的性能。
执行以下步骤：
- 在两个文档中找到对应的表格。
- 找出两个表格中出现的基准测试的交集。
- 提取基准测试名称以及两个模型的准确率值。
- 按字母顺序对基准测试进行排序。（排序时忽略大小写，例如 "a" 排在 "B" 前面。）
- 写基准测试名称时，不要包含任何空格或换行，并将所有字符保持在一起。大小写应与 ScaleCap 中的表格一致。
- 按以下列表格式输出：
[{"Name":<Fill in the benchmark... | [{"Name": "AI2D", "ScaleCap-450k": "74.0", "CapRL-1M": "75.3"},
{"Name": "ChartQA", "ScaleCap-450k": "77.8", "CapRL-1M": "80.6"},
{"Name": "DocVQA", "ScaleCap-450k": "85.7", "CapRL-1M": "89.5"},
{"Name": "InfoVQA", "ScaleCap-450k": "51.8", "CapRL-1M": "59.9"},
{"Name": "MathVista", "ScaleCap-450k": "49.7", "CapRL-1... | D3 | judge | [
"2f/2f30a219636853df9a09935599044a4b2bb00463c7fc5094dd6b2929b0544095.jpg",
"72/728d2d696314f50bc799e966fd6a2cef4c1eead6276f1bbde897b4da94c73129.jpg",
"5e/5e4cd48d844047a87819738a82fe3a7a2ecb5f76bc5e1eadb8e47327fc299430.jpg",
"d9/d90f6b85c6adc5745380730b5b74ec473f8a5a42cadd676b35d094636e953ccd.jpg",
"7c/7c5d... | np_d3_01new_001__zh_all_first | np_d3_01new_001 | zh_all_first | all_first | text | You are an expert evaluator the model's response to a question. I will provide you with the ground-truth and the model's prediction. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
1. Format score (0.04 points): The ground-truth is a ... | {} | np_d3_01new | scientific | zh | np_d3_01new_001 | [
"images/SciDocBench/2f/2f30a219636853df9a09935599044a4b2bb00463c7fc5094dd6b2929b0544095.jpg",
"images/SciDocBench/72/728d2d696314f50bc799e966fd6a2cef4c1eead6276f1bbde897b4da94c73129.jpg",
"images/SciDocBench/5e/5e4cd48d844047a87819738a82fe3a7a2ecb5f76bc5e1eadb8e47327fc299430.jpg",
"images/SciDocBench/d9/d90f6... | |
358 | 你是一名化学论文阅读助手。请帮助我核验在以下实验条件下，这三篇论文中 NCM-811 或其改性材料的容量保持率数据：
1. 循环次数：$500$ cycles
2. 电压窗口：约 $2.8 \sim 4.5 V$
3. 质量负载：$10 mg cm^{-2}$
4. 充电倍率：$1 C$
5. 放电倍率：$1 C$
6. 面容量：$2 mAh cm^{-2}$
7. N/P 比：约 $1.2$
你的回答格式应为：
{
"<First 5 words of article title>" : {
"result" : <digit placehoder using percentage>,
... | {
"High Voltage Cycling Stability of": {
"result": 88,
"experiment_conditions": {
"cycle_number": 500,
"voltage_window": {
"lower_limit": 2.8,
"supper_limit": 4.5
},
"mass_loading($mg cm^{-2}$)": "10-10.5",
"charge_rate($C$)": 1,
"discharge_rate($C$)": 1,
... | D3 | judge | [
"0f/0f506548ac699be45eb7fda7523b391781069f8fc8f9a82857eecbac2d83d971.jpg",
"05/058147ae863f0eb6fbf946bd4ae2fdba8587585273cc89a3ddc0abbd32d90215.jpg",
"25/254d167b475456b9739053b826677d1d7314dfd2011d76a20d0f5879f4efda48.jpg",
"f2/f24ae81c94a16097d39f9d880794ede311d4d811242ac7d6fe456f372f540a3a.jpg",
"84/84b5... | np_d3_02new_001__zh_all_first | np_d3_02new_001 | zh_all_first | all_first | json | You are an expert evaluator the model's response to a question. I will provide you with the ground-truth and the model's prediction. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
1. Some values inside the ground-truth answers have m... | {} | np_d3_02new | scientific | zh | np_d3_02new_001 | [
"images/SciDocBench/0f/0f506548ac699be45eb7fda7523b391781069f8fc8f9a82857eecbac2d83d971.jpg",
"images/SciDocBench/05/058147ae863f0eb6fbf946bd4ae2fdba8587585273cc89a3ddc0abbd32d90215.jpg",
"images/SciDocBench/25/254d167b475456b9739053b826677d1d7314dfd2011d76a20d0f5879f4efda48.jpg",
"images/SciDocBench/f2/f24ae... | |
359 | 以 mermaid 格式复现 Image 5b，使用红色（#cc0000）和蓝色箭头（#0066cc）进行区分。你只需要绘制带箭头的三列。左列以节点 'CAD' 开始。中间层以块 'Progenitor maintenance' 开始，右层以节点 'MCH' 开始。忽略 'CAD' 左侧以及 'MCH' 右侧的列、节点和线。每个节点都应严格遵循图中的名称。每一层中的顺序也应遵循图中从上到下的顺序。箭头颜色只能从 #cc0000 和 #0066cc 中选择。
请按以下格式输出最终答案：
```mermaid
graph LR
subgraph Left Layer
1[CAD]
2[CALR]
end
... | ```
graph LR
subgraph Left Layer
1[CAD]
2[CALR]
3[POLE]
4[SUPT5H]
5[MED17]
6[ATR]
7[GTF3C1]
8[RBBP6]
end
subgraph Middle Layer
9[Progenitor maintenance]
10[Haemoglobin synthesis]
11[Autophagy]
12[Cell cycle]
end
subgraph Right Layer
13[MCH]
14[... | E1 | judge | [
"05/05dc866aea47f1bc0a40afe2fa7a81b233659c346666381104305f1454da2426.jpg",
"ce/cea9d4a4ca1fb22cc3c1008f5016f9abb0432389612f4dc14d1276ecc542f140.jpg",
"95/95cc4252e6e1296a31361ace63b611829240e8f0c6d364efb71ca71362fc1d4f.jpg"
] | np_e1_01new_001__zh_all_first | np_e1_01new_001 | zh_all_first | all_first | text | You are an expert evaluator the model's response to a question. I will provide you with the ground-truth and the model's prediction. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
1. Block score (0.28 points): First check all the blo... | {} | np_e1_01new | scientific | zh | np_e1_01new_001 | [
"images/SciDocBench/05/05dc866aea47f1bc0a40afe2fa7a81b233659c346666381104305f1454da2426.jpg",
"images/SciDocBench/ce/cea9d4a4ca1fb22cc3c1008f5016f9abb0432389612f4dc14d1276ecc542f140.jpg",
"images/SciDocBench/95/95cc4252e6e1296a31361ace63b611829240e8f0c6d364efb71ca71362fc1d4f.jpg"
] | |
360 | 请仔细阅读所附论文，并以 JSON 格式提取以下信息：
1. Model：列出本文提出的所有模型。对于每个模型，提供其名称以及它所基于的基础模型。
2. Data：提供本文构建的训练数据集名称，并列出用于构建它的所有源数据集。
3. Training：按顺序列出本文训练流程中使用的所有训练阶段/方法。对于每个阶段，提供其名称以及论文中明确提到的所有超参数。
将结果输出为一个仅包含三个键的单一 JSON 对象："Model"、"Data" 和 "Training"。严格遵循以下模式：
{
"Model": [
{
"name": "<model name>",
"base_model": "<bas... | {
"Model": [
{
"name": "Spatial-SSRL-3B",
"base_model": "Qwen2.5-VL-3B"
},
{
"name": "Spatial-SSRL-7B",
"base_model": "Qwen2.5-VL-7B"
},
{
"name": "Spatial-SSRL-4B",
"base_model": "Qwen3-VL-4B"
}
],
"Data": {
"name": "Spatial-SSRL-81k",
"source":... | B2 | judge | [
"e8/e827d7ab11c5c5bb4a87f69e40b40198f24d3c5c1838d15196f70fcd3bed63e5.jpg",
"ae/ae7743b6e3ef6d2fe44097225b13fb8f73a079a52ea19565418d3cbc406aa3f5.jpg",
"95/95dc2f76bf3d2dda1577762cb132104ea3f785acf046ea74c8a29f90a25ea484.jpg",
"80/8012b2c1915396c72e32335b51f182619ff1ea21fa48f1b924e4ff9ff450b723.jpg",
"49/4982... | spatial-ssrl_002__zh_all_first | spatial-ssrl_002 | zh_all_first | all_first | json | You are an expert evaluator the model's response to a question. I will provide you with the question, the ground-truth and the model's prediction. You need to score the model's prediction.
Question: {prompt}
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
1. First check if t... | {} | spatial-ssrl | cs | zh | spatial-ssrl_002 | [
"images/SciDocBench/e8/e827d7ab11c5c5bb4a87f69e40b40198f24d3c5c1838d15196f70fcd3bed63e5.jpg",
"images/SciDocBench/ae/ae7743b6e3ef6d2fe44097225b13fb8f73a079a52ea19565418d3cbc406aa3f5.jpg",
"images/SciDocBench/95/95dc2f76bf3d2dda1577762cb132104ea3f785acf046ea74c8a29f90a25ea484.jpg",
"images/SciDocBench/80/8012b... | |
361 | 你是一名论文阅读助手。请仔细分类本文中的引用文本。主要类别为：
1. Background: 提供理论支持或研究背景（例如，AI 时代的到来）。
2. Method: 采用或比较已有方法。
3. Result: 将他人的结果作为比较基线。
4. Critical: 纯粹批评现有研究的局限性。
注意：
- 你应该只关注带有编号标签的引用
- 你应该只关注给定的引用（编号 24,33）
你必须按照以下格式回答。不要输出 "```"
{
"[1]": {
"{Digital physical page number}-{Chapter name of the smallest section without ... | {
"[24]" : {
"2-Introduction-1" : ["Background"],
"2-Introduction-2" : ["Background"],
"4-Semantically Meaningful Integration to pLM Latent Space-3" : ["Method"]
},
"[33]" : {
"2-Introduction-1" : ["Critical"],
"2-Introduction-1" : ["Background"]
}
} | A3 | judge | [
"41/414626c0f3ab8b80064afaf12c99bae6d146fd6fab2b68a2b4305b87b6af2042.jpg",
"81/81b9dad57a4c7e3c632d0835dd10cb4558b378a0d0d249b5df631df6518877e5.jpg",
"a9/a9681eb57ca7dd3e746316e96ee2dee21574dfc51b840f6c73e2636162a03db4.jpg",
"d2/d29092a04a2391e0e4e28e4b376de5e0fa8f27313343e862dec3e9588c1f4501.jpg",
"9f/9fc3... | ProtFlowArticle_002__zh_all_first | ProtFlowArticle_002 | zh_all_first | all_first | text | You are an expert evaluator the model's response to a question. I will provide you with the question, the ground-truth and the model's prediction. You need to score the model's prediction.
Question: {prompt}
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
1. First check if t... | {} | ProtFlowArticle | biology | zh | ProtFlowArticle_002 | [
"images/SciDocBench/41/414626c0f3ab8b80064afaf12c99bae6d146fd6fab2b68a2b4305b87b6af2042.jpg",
"images/SciDocBench/81/81b9dad57a4c7e3c632d0835dd10cb4558b378a0d0d249b5df631df6518877e5.jpg",
"images/SciDocBench/a9/a9681eb57ca7dd3e746316e96ee2dee21574dfc51b840f6c73e2636162a03db4.jpg",
"images/SciDocBench/d2/d2909... | |
362 | 给定一篇科学 PDF 文档。你的任务是提取并用 LaTex 复现第 6.2 节中标题为 "Resolution metrics" 的小节正文。
严格遵循以下规则：
包含该节正文中的所有连续叙述和段落文本，完全保留原始措辞。
使用 LaTeX 命令保留所有原始粗体和斜体格式：用 \textbf{} 表示粗体，用 \textit{} 表示斜体。
假设 amsthm、amsmath 和 amssymb 已预先定义。保留公式中所有原始字母变体，例如用于花体的 \mathcal{} 和用于黑板粗体的 \mathbb{}。
完全排除以下内容：图注、表注、脚注、脚注标记、方程，以及任何框注或嵌入面板（例如 "Key Insight" 框）内的文... | Let $\mathcal{G}$ denote the ground-truth point set and $\mathcal{R}$ the reconstructed point set under evaluation. We measure accuracy using $\text{dist}(\mathcal{R}\rightarrow \mathcal{G})$ and completeness using $\text{dist}(\mathcal{G}\rightarrow \mathcal{R})$ following [1]. The Chamfer Distance (CD) is then define... | A1 | judge | [
"3e/3ec5ea2d53fb888ecb5e7874da24b2b605da7a2a105f473f69f4770ea11829df.jpg",
"ee/eeb13fc579ea4c988928ea66b9d710592b23a1a81be2d3f7d12fa12b1cfb30ed.jpg",
"bf/bf08a31e4e9293d2dd9a20beed22f2cff5ce415800f06adc1647494fa6b82025.jpg",
"b9/b90a171f3b85c196fe0a10b5b0c041403be23045d210157511fb4a64df84c091.jpg",
"72/7234... | depth-anything3_003__zh_all_first | depth-anything3_003 | zh_all_first | all_first | text | {} | depth-anything3 | cs | zh | depth-anything3_003 | [
"images/SciDocBench/3e/3ec5ea2d53fb888ecb5e7874da24b2b605da7a2a105f473f69f4770ea11829df.jpg",
"images/SciDocBench/ee/eeb13fc579ea4c988928ea66b9d710592b23a1a81be2d3f7d12fa12b1cfb30ed.jpg",
"images/SciDocBench/bf/bf08a31e4e9293d2dd9a20beed22f2cff5ce415800f06adc1647494fa6b82025.jpg",
"images/SciDocBench/b9/b90a1... | ||
363 | 你是一名专业科学编辑和 LaTeX 排版师。
你的目标是为所提供的研究论文内容生成一份全面的 "Index of Notations"。
输出要求：
请使用 booktabs package 生成一个 LaTeX Table 代码块。
该表必须有 3 列：
Symbol: 该符号的 LaTeX 代码（例如，$\mathcal{D}_{KL}$）。
Type: （例如，Scalar, Vector, Function, Set）。
Definition / Meaning: 简洁的英文解释。
First Appearance Snippet: 从文本中逐字引用（最多 15 个词），显示其定义位置。如有必要，使用 "..." 表... | $L$
$\ell$
$m$
$K$
$L_{\mathrm{res}}$
$L_{\mathrm{tr}}$
$x$
$y$
$B$
$\mathcal{L}_B(\theta)$
$\theta$
$\theta^+$
$\eta$
$\eta^\star$
$z^{(\ell)}(x;\theta)$
$z^{(\ell)}_i(x;\theta)$
$\sigma(\cdot)$
$\sigma'(\cdot)$
$q$
$\fan_{\mathrm{in}}(W)$
$W^{(\ell)}$
$W_{\mathrm{res}}$
$\partial_a$
$\Delta\theta_a$
$\Delta z^{(\ell)... | B1 | judge | [
"68/68178d5eede343a785c4ff6e78b93bcd2694112db4899c25da55cd0fdca4801a.jpg",
"3a/3a8f2a6472d752a20b51c24f3349dcc24b21d225717eb9edd504553f8c744df2.jpg",
"81/815fe4cf75639a2a3498ed3739ca5d2aeeb7f6e57806e3a91b6fd9338068280b.jpg",
"fd/fdbd25e79973cc8d3fee4ca657983746eab64328b5d5f704868586e7b2d132b5.jpg",
"87/871a... | hptransfer_002__zh_all_first | hptransfer_002 | zh_all_first | all_first | text | You are an expert evaluator the model's response to a question. I will provide you with the question, the ground-truth and the model's prediction. You need to score the model's prediction.
Question: {prompt}
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
1. Format Score (0.... | {} | hptransfer | cs | zh | hptransfer_002 | [
"images/SciDocBench/68/68178d5eede343a785c4ff6e78b93bcd2694112db4899c25da55cd0fdca4801a.jpg",
"images/SciDocBench/3a/3a8f2a6472d752a20b51c24f3349dcc24b21d225717eb9edd504553f8c744df2.jpg",
"images/SciDocBench/81/815fe4cf75639a2a3498ed3739ca5d2aeeb7f6e57806e3a91b6fd9338068280b.jpg",
"images/SciDocBench/fd/fdbd2... | |
364 | 你将获得一篇科学 PDF 文档。你的任务是提取论文中描述的 MM-IFInstruct-23k 数据集的组成和数据来源信息，并将其复现为 LaTeX 中的 TikZ 径向图。
严格遵循以下规则：
提取规则：
- 识别所有为 MM-IFInstruct-23k 作出贡献的源数据集。
- 对于每个源数据集，如果论文中明确说明了确切的数据数量（例如样本数或实例数），则提取该数量。如果未说明数量，则省略标签但仍包含该节点。
- 不要推断或编造文档中未明确出现的任何信息。
图示规则：
- 将 MM-IFInstruct-23k 放置为单个中心节点。
- 将每个源数据集作为周围的叶节点，围绕中心以径向（辐条）布局均匀分布。
- 绘制从每个叶节点... | \documentclass[border=10pt]{standalone}
\usepackage{tikz}
\usetikzlibrary{arrows.meta, positioning}
\definecolor{centerblue}{HTML}{3A8FB7}
\definecolor{leafbg}{HTML}{B5DDE4}
\definecolor{leafborder}{HTML}{6FBCCC}
\definecolor{arrowcolor}{HTML}{5A9EAE}
\begin{document}
\begin{tikzpicture}[
leaf/.style={
dr... | A4 | judge | [
"56/562613ffd6084f4f774dc68fee46e7975bfdd57616e3776adbaea1f27c11f6d2.jpg",
"ba/babc967d50d7d14b8df056adf2dfd17be4992ad3b7eb27d21e47a96ac0931326.jpg",
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"9a/9a74a2bb9113b52e8a3af0588aadfd7ecf8eddea97c972daf564fd6c76a90789.jpg",
"44/449e... | mmifenginenew_001__zh_all_first | mmifenginenew_001 | zh_all_first | all_first | text | You are an expert evaluator the model's response to a question. I will provide you with the ground-truth and the model's prediction. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
1. Format Score (0.2 points): Check whether the predi... | {} | mmifenginenew | cs | zh | mmifenginenew_001 | [
"images/SciDocBench/56/562613ffd6084f4f774dc68fee46e7975bfdd57616e3776adbaea1f27c11f6d2.jpg",
"images/SciDocBench/ba/babc967d50d7d14b8df056adf2dfd17be4992ad3b7eb27d21e47a96ac0931326.jpg",
"images/SciDocBench/56/566080440507e9f95922cc9446648a09242b424b60be467bb661c0b1e5a8741b.jpg",
"images/SciDocBench/9a/9a74a... | |
365 | 你将获得一篇科学 PDF 文档。你的任务是提取论文中描述的 ChartSFT 数据集的组成和数据来源信息，并将其复现为 LaTeX 中的 TikZ 径向图。
请严格遵循以下规则：
提取规则：
- 识别所有为 ChartSFT 做出贡献的源数据集（即 ChartSFT 组成表中列出的列来源）。
- 对于每个源数据集，仅当论文中以单个数字明确陈述其确切总数据量（即所有任务的总和）时，才提取该总数据量。如果某个来源的总量没有作为独立数值明确给出，则省略其箭头上的数量标签，但仍包含该节点。
- ChartSFT 的总体总大小 (39M) 已明确陈述，必须显示在中心节点上。
- 一些源数据集未在组成表中直接命名，而是通过参考文献编号引用。对... | \documentclass[tikz,border=10pt]{standalone}
\usepackage{tikz}
\usetikzlibrary{shapes.geometric, arrows.meta}
\begin{document}
\begin{tikzpicture}[
>=stealth,
thick,
every node/.style={font=\sffamily},
outer/.style={
ellipse,
draw=black,
fill=orange!20,
minimum width=2cm... | B1 | judge | [
"80/802636f63db827590e364d8419441e73933045a973417436a40e9eb4250601df.jpg",
"76/7654cd9e48bb47d36d5c1a92568fa0630385bedcb4c659fa9849ac69eb4e47c9.jpg",
"f7/f7a52cd7c72330a6b487082dbc45cecb71cb547bfc6705d87531dfb577bfce18.jpg",
"39/394ceb825bff651eb08f2ca091404b994537e860cdf917741b593da900979611.jpg",
"9c/9cb4... | chartassistant_002__zh_all_first | chartassistant_002 | zh_all_first | all_first | text | You are an expert evaluator the model's response to a question. I will provide you with the ground-truth and the model's prediction. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
1. Format Score (0.2 points): Check whether the predi... | {} | chartassistant | cs | zh | chartassistant_002 | [
"images/SciDocBench/80/802636f63db827590e364d8419441e73933045a973417436a40e9eb4250601df.jpg",
"images/SciDocBench/76/7654cd9e48bb47d36d5c1a92568fa0630385bedcb4c659fa9849ac69eb4e47c9.jpg",
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"images/SciDocBench/39/394ce... | |
366 | # 角色
你是 **Scientometrics** 和 **LaTeX Visualization** 方面的专家。你的目标是分析给定科学论文 PDF 中的引用，并使用 LaTeX/TikZ 将它们转换为一个**可视化 Citation Dashboard**。
# 任务
生成一个**独立的、可编译的 LaTeX 文件**，用于可视化输入文本的引用网络。输出必须包括：
1. **一个 TikZ Mindmap**：可视化当前工作（中心）与其参考文献（节点）之间的关系。
# 可视化逻辑（颜色编码）
你必须在 TikZ 代码中应用以下配色方案，以表示引用的**意图**：
- **方法基础（Foundation）** -> `Fo... | \documentclass[border=10pt]{standalone}
\usepackage[dvipsnames]{xcolor}
\usepackage{tikz}
\usetikzlibrary{mindmap,shadows,positioning,calc}
\usepackage{booktabs}
\usepackage{colortbl}
\usepackage{array}
\usepackage{tabularx}
\usepackage{makecell}
\usepackage{fontenc}
\usepackage{geometry}
% ── Custom colours ─────────... | A3 | judge | [
"ba/ba9172249b94313687d08c27029d6e35ab2d5a4e99182b55d5606dd8ec48860f.jpg",
"99/99eba5fb823055b873d1788a01b0d9f6485ec0cf0b3ebaab74d133f16b113d85.jpg",
"52/52d8b829ba098653a206f96775a2e0828acdd6ff7cbb18cc0915dd1996885753.jpg",
"6b/6b1e4d3fa2ceb6520bc06ee8c830bcde1081fc5f14f758db38fcd64ae691d979.jpg",
"4d/4d3c... | spa3r_002__zh_all_first | spa3r_002 | zh_all_first | all_first | text | You are an expert evaluator the model's response to a question. I will provide you with the ground-truth and the model's prediction. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
1. Format Score (0.1 points): Check whether the predi... | {} | spa3r | cs | zh | spa3r_002 | [
"images/SciDocBench/ba/ba9172249b94313687d08c27029d6e35ab2d5a4e99182b55d5606dd8ec48860f.jpg",
"images/SciDocBench/99/99eba5fb823055b873d1788a01b0d9f6485ec0cf0b3ebaab74d133f16b113d85.jpg",
"images/SciDocBench/52/52d8b829ba098653a206f96775a2e0828acdd6ff7cbb18cc0915dd1996885753.jpg",
"images/SciDocBench/6b/6b1e4... | |
367 | 给定三篇科学 PDF 文档。第一篇介绍 Cambrian-1，第二篇介绍 LLaVA-OneVision，第三篇介绍 MAmmoTH-VL。
从第一篇论文中的 Cambrian-7M composition figure 和第二篇论文中的 Single-Image 3.2M composition figure 中，提取每个图中黄色/橙色颜色类别下的所有数据集，并仅使用精确的大小写不敏感名称匹配来计算它们的交集。
然后，从第三篇论文中，识别 MAmmoTH-VL 中定义的所有数据源组。对于每个组，仅使用精确的大小写不敏感名称匹配，计算其与上一步得到的数据集列表的交集。确定哪个组具有最大的交集。
输出一个单一 JSON 对象，包... | {"group":"Group C","count":4,"datasets":["Hateful Memes","RefCOCO","VizWiz","VQAv2"]} | D2 | json_match | [
"23/238f6d5d3156a5780dc2f463a50a8a9595242fce8d3ca1ac013091b0c5619353.jpg",
"9d/9d154dd20a146167625e33c22de5cf91b5886c0ec938ed60f630f7c83f09a1f5.jpg",
"7d/7dd0f2687cb0ccb9e628771f234b697a0139dee7102d8bc391f5c1d50ee142a3.jpg",
"0e/0ef7b0a0ed6a2dc730d0649800cd4919af1ebf1b531b028e78048959195a2b8c.jpg",
"ea/ead2... | cambrian-llavaov-mammoth_002__zh_all_first | cambrian-llavaov-mammoth_002 | zh_all_first | all_first | json | {} | ["cambrian1", "llavaov", "mammoth-vl"] | cs | zh | cambrian-llavaov-mammoth_002 | [
"images/SciDocBench/23/238f6d5d3156a5780dc2f463a50a8a9595242fce8d3ca1ac013091b0c5619353.jpg",
"images/SciDocBench/9d/9d154dd20a146167625e33c22de5cf91b5886c0ec938ed60f630f7c83f09a1f5.jpg",
"images/SciDocBench/7d/7dd0f2687cb0ccb9e628771f234b697a0139dee7102d8bc391f5c1d50ee142a3.jpg",
"images/SciDocBench/0e/0ef7b... | ||
368 | # 角色
你是 **Scientometrics** 和 **LaTeX Visualization** 方面的专家。你的目标是分析给定科学论文 PDF 中的引用，并使用 LaTeX/TikZ 将其转换为一个**可视化 Citation Dashboard**。
# 任务
生成一个**独立的、可编译的 LaTeX 文件**，用于可视化输入文本的引用网络。输出必须包括：
1. **一个 TikZ Mindmap**：可视化当前工作（中心）与其参考文献（节点）之间的关系。
# 可视化逻辑（颜色编码）
你必须在 TikZ 代码中应用以下配色方案，以表示引用的**意图**：
- **Method Basis (Foundation)*... | \documentclass[border=10pt]{standalone}
\usepackage[dvipsnames]{xcolor}
\usepackage{tikz}
\usetikzlibrary{mindmap,shadows,positioning,calc}
\usepackage{booktabs}
\usepackage{colortbl}
\usepackage{array}
\usepackage{tabularx}
\usepackage{makecell}
\usepackage{fontenc}
\usepackage{geometry}
% ── Custom colours ─────────... | A3 | judge | [
"4d/4dc402b42b16ddece764c41f8fb4a75d3e94bb5bccda4429d41ce9a208c79841.jpg",
"8a/8a42354577f0049391ff5c41a92650e3e1a86c65744f8366c56c6232ddd59b8b.jpg",
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"02/02d0... | ppo_003__zh_all_first | ppo_003 | zh_all_first | all_first | text | You are an expert evaluator the model's response to a question. I will provide you with the ground-truth and the model's prediction. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
1. Format Score (0.1 points): Check whether the predi... | {} | ppo | cs | zh | ppo_003 | [
"images/SciDocBench/4d/4dc402b42b16ddece764c41f8fb4a75d3e94bb5bccda4429d41ce9a208c79841.jpg",
"images/SciDocBench/8a/8a42354577f0049391ff5c41a92650e3e1a86c65744f8366c56c6232ddd59b8b.jpg",
"images/SciDocBench/ca/ca23f44fcc1e06d7228552919854d916196e60ba30be3765017ef31b17a93527.jpg",
"images/SciDocBench/b8/b8f65... | |
369 | 你将获得 5 个 PDF 文件。第一个 PDF 包含 6 个数据样本的截图，这些样本从上到下垂直排列，编号为 1 到 6。其余 4 个 PDF 文件是研究论文，每篇论文描述一个特定的数据集。你的任务：
1. 仔细检查第一个 PDF 中的 6 个数据样本（从上到下编号为 1–6）。
2. 阅读 4 篇研究论文，以理解每篇论文所描述的数据集模式、字段和结构。
3. 对于每个数据样本，根据列名、字段类型、数据内容和整体结构，判断它是否匹配 4 篇论文中某一篇所描述的数据集。
4. 仅输出你确信某个数据样本属于某篇论文的数据集的配对。并非每个数据样本都一定匹配某篇论文，也并非每篇论文都一定匹配某个数据样本。将你的答案输出为匹配配对的 JSO... | [{"sample": 1, "title": "TwiFF (Think With Future Frames): A Large-Scale Dataset for Dynamic Visual Reasoning"},{"sample": 2, "title": "AGENTVISTA: Evaluating Multimodal Agents in Ultra-Challenging Realistic Visual Scenarios"}, {"sample": 5, "title": "AGENTVISTA: Evaluating Multimodal Agents in Ultra-Challenging Realis... | F1 | judge | [
"82/82808cc18148ac314c866d0018482ed26b50d1084df556599c4e41aba071abac.jpg",
"08/088bc11fc517a3fe811d5f70194f93bf8aed805f13243313501b0f48bf3972a8.jpg",
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"46/4678... | twiffnew_001__zh_all_first | twiffnew_001 | zh_all_first | all_first | text | You are an expert evaluator the model's response to a question. I will provide you with the ground-truth and the model's prediction. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
1. First check the format of the prediction. It shoul... | {} | twiffnew | cs | zh | twiffnew_001 | [
"images/SciDocBench/82/82808cc18148ac314c866d0018482ed26b50d1084df556599c4e41aba071abac.jpg",
"images/SciDocBench/08/088bc11fc517a3fe811d5f70194f93bf8aed805f13243313501b0f48bf3972a8.jpg",
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"images/SciDocBench/69/69f0e... | |
370 | 我正在阅读两篇文档，它们可能有助于我未来关于 SLE 的研究。在文档 1 中，提到了一种特定的 IgG 糖基化与 SLE 有强相关性，我希望将其纳入我未来的研究。我也有兴趣研究 SLE 中的唾液酸化 IgG。我在文档 2 的图 1 中看到了几种物质。请帮助我定位所有相关物质，这些物质必须同时满足上述两个要求，并检索文档 2 的图 2 中哪些峰(s)包含每种物质。
请思考并按以下格式输出最终答案：
{"Substance 1": "<Substance name, e.g. FA2G1>", "Location": "<e.g. Row 1, Column 1>", "Peak": "<e.g. 1>"}
{"Substance 2"... | {"Substance 1": "FA2G1S1", "Location": "Row 2, Column 6", "Peak": "(16a), (16b)"}
{"Substance 2": "FA1G1S1", "Location": "Row 2, Column 7", "Peak": "15"}
{"Substance 3": "FA2BG1S1", "Location": "Row 2, Column 9", "Peak": "(16b), (17)"}
{"Substance 4": "FA2G2S1", "Location": "Row 3, Column 6", "Peak": "18a, 18b"}
{"Sub... | D3 | judge | [
"42/4215517222f768e42b65eafd650c6438dcf5f7cd82792fc48c13e9a8d5cdc250.jpg",
"81/8124317546141b1eea606855b75381e07c92a48fa8767332a897c0b76f6d2bef.jpg",
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"be/be9e... | sle-glyco-iggnew_001__zh_all_first | sle-glyco-iggnew_001 | zh_all_first | all_first | text | You are an expert evaluator the model's response to a question. I will provide you with the ground-truth and the model's prediction. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
1. Set the initial score to 0 points.
2. Score for 'Su... | {} | sle-glyco-iggnew | biology | zh | sle-glyco-iggnew_001 | [
"images/SciDocBench/42/4215517222f768e42b65eafd650c6438dcf5f7cd82792fc48c13e9a8d5cdc250.jpg",
"images/SciDocBench/81/8124317546141b1eea606855b75381e07c92a48fa8767332a897c0b76f6d2bef.jpg",
"images/SciDocBench/c8/c81cc4cd1ec7a92b754ed4c45ecff17dbd2ea622ba1ab5f88fe6e7d1c55d323e.jpg",
"images/SciDocBench/79/797ed... | |
371 | 我附上了一篇物理论文。请阅读它，并将 Figure 1(b) 中所示的图结构——具体而言是该图中嵌入的 3-regular graph 插图——复现为一个 LaTeX/TikZ 图形。
要求：
- 该图有 N = 22 个节点，每个节点恰好连接到 3 条边（3-regular）
- 节点应渲染为填充圆，并带有可见的数字标签（1–22）
- 边应清晰地绘制为实线
- 节点布局应尽可能在视觉上与论文中的图相匹配
请输出一个完整的、可编译的 LaTeX 文档。 | \documentclass[border=12pt]{standalone}
\usepackage{tikz}
\begin{document}
\begin{tikzpicture}[
scale=1.0,
nd/.style={circle, draw=teal!60!black, fill=teal!70, minimum size=7mm,
inner sep=0pt, font=\small\bfseries, text=white, line width=0.8pt},
ed/.style={draw=black, line width=1.5pt, line cap=roun... | E1 | judge | [
"14/14eba3e86d9d540a20acab554b51b99edfcb48d8a45a7d708a08a02712900c3b.jpg",
"44/44fdf6e9b5b01a9605667de4c0e53f5f25582e09215863b7a22adef1b40c7b4c.jpg",
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"83/8320... | itwa-spin_002__zh_all_first | itwa-spin_002 | zh_all_first | all_first | text | You are an expert evaluator the model's response to a question. I will provide you with the ground-truth and the model's prediction. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
1. Format Score (0.2 points): Check whether the predi... | {} | itwa-spin | physics | zh | itwa-spin_002 | [
"images/SciDocBench/14/14eba3e86d9d540a20acab554b51b99edfcb48d8a45a7d708a08a02712900c3b.jpg",
"images/SciDocBench/44/44fdf6e9b5b01a9605667de4c0e53f5f25582e09215863b7a22adef1b40c7b4c.jpg",
"images/SciDocBench/e4/e43c67deab2615bc416921a39a9ad47688473b74ec30eb6f5515c756691dbd24.jpg",
"images/SciDocBench/49/49bd7... | |
372 | 分析所提供的量子计算综述论文，并识别论文的分类体系中明确分类的所有被引用研究工作。根据以下维度分配分类标签，并以 JSON 格式表示：
{
"Basic_Characteristics": ["QI", "CQCT", "PM"],
"Algorithmic_Characteristics": ["P", "ACQA", "T", "TLG", "QO"],
"Time_and_Gate_Characteristics": ["DT", "MT"],
"Other_Characteristics": ["S", "TCGLQ"],
"QKD_Type": ["Discrete_Continuous", "COW"... | {"Weitenberg et al.(2011)": ["Basic_Characteristics:QI", "Basic_Characteristics:CQCT", "Basic_Characteristics:PM", "Algorithmic_Characteristics:P", "Algorithmic_Characteristics:ACQA", "Algorithmic_Characteristics:TLG", "Time_and_Gate_Characteristics:DT", "Other_Characteristics:TCGLQ", "Other_Characteristics:S"], "Tomza... | A3 | judge | [
"f3/f36e522096e8e71cbc15abb8661e5da085b257779cdd0b9fcaae07139d47916c.jpg",
"b2/b2b61a889d242a8a9f6a52807ba3186df8b21149a9418fd01146de451d572f0c.jpg",
"b8/b895ee6d75b44cbc4e01a32f7f2770200a862aa9e1707c7aaa0712378b59f2d4.jpg",
"14/14e8769c510d0f7e74a43a2b9e6a7643830ec0fdc19697126af1b956e18f8824.jpg",
"66/66be... | [{"text":"# Quantum Computing: A Taxonomy, Systematic Review and Future Directions\n\nSukhpal Singh Gill $^{1}$ , Adarsh Kumar $^{2*}$ , Harvinder Singh $^{3}$ , Manmeet Singh $^{4,5}$ , Kamalpreet Kaur $^{6}$ , Muhammad Usman $^{7}$ and Rajkumar Buyya $^{8}$\n\n$^{2}$ Department of Systemics, School of Computer Scienc... | aug_A3_2_2__zh_interleave | aug_A3_2_2 | zh_interleave | interleave | zh | 3 | [
"images/SciDocBench/f3/f36e522096e8e71cbc15abb8661e5da085b257779cdd0b9fcaae07139d47916c.jpg",
"images/SciDocBench/b2/b2b61a889d242a8a9f6a52807ba3186df8b21149a9418fd01146de451d572f0c.jpg",
"images/SciDocBench/b8/b895ee6d75b44cbc4e01a32f7f2770200a862aa9e1707c7aaa0712378b59f2d4.jpg",
"images/SciDocBench/14/14e87... | |||||
373 | 你是一名专业科学编辑和 LaTeX 排版师。你的目标是为提供的研究论文内容生成一份全面的 "Index of Notations"。
输出要求：请生成一个完整的 LaTeX 文件，并包含一个 LaTeX Table 代码块。你可以使用 booktabs、amsmath、amsfonts package 以及你想使用的其他 packages。该表格必须严格遵循下面示例的格式：
\documentclass{article}
\usepackage{booktabs}
\usepackage{amsmath}
\usepackage{amsfonts}
\begin{document}
\begin{table}[htbp]
\... | \documentclass{article}
\usepackage{booktabs}
\usepackage{amsmath, amssymb, amsfonts}
\usepackage{mathtools}
\begin{document}
\begin{table*}[!ht]
\renewcommand{\figurename}{Table}
\caption{Index of Notations}
\label{table:notations}
\centering
\resizebox{\textwidth}{!}
{
\begin{tabular}{@{}ll@{}}
\toprule
\textbf{Not... | B1 | judge | [
"22/226a19ff448530da9f3f727fd5eb89b65eb2fccb085961090722652f81246448.jpg",
"b9/b9f6eae860b94c56112526d2b330844eabdbe314b81085463c6931c25e9b035c.jpg",
"e4/e46bb606865ef97bbff617d50227eb423c44a11385df99ea9e5ce9782651dfcf.jpg",
"6b/6be77e79019111f10d36f19921355f6e63fd028f10566be3445635e39fa25cb4.jpg",
"78/788f... | [{"text":"# PACKED-ENSEMBLES FOR EFFICIENT UNCERTAINTY ESTIMATION\n\n## ABSTRACT\n\nDeep Ensembles (DE) are a prominent approach for achieving excellent performance on key metrics such as accuracy, calibration, uncertainty estimation, and out-of-distribution detection. However, hardware limitations of real-world system... | aug_B1_1_2__zh_interleave | aug_B1_1_2 | zh_interleave | interleave | zh | 7 | [
"images/SciDocBench/22/226a19ff448530da9f3f727fd5eb89b65eb2fccb085961090722652f81246448.jpg",
"images/SciDocBench/b9/b9f6eae860b94c56112526d2b330844eabdbe314b81085463c6931c25e9b035c.jpg",
"images/SciDocBench/e4/e46bb606865ef97bbff617d50227eb423c44a11385df99ea9e5ce9782651dfcf.jpg",
"images/SciDocBench/6b/6be77... | |||||
374 | 找出 Theorem 7.1 中每一项的依赖链，包括 7.1(1a)、7.1(1b)、7.1(1c)、7.1(2) 和 7.1(3)。
Output Requirement: 请返回一个 JSON 对象，其中每个键表示一个条目或引理，对应的值是其直接先决条件的数组。格式必须严格遵循以下示例：
{
"Theorem 7.1(1c)": ["Proposition 7.1(1a)", "Lemma 3.9"],
"Proposition 3.9": ["Lemma 3.9", "Assumption 3.6"],
"Lemma 3.2": ["Definition 3.4"]
} | {"Theorem 7.1(1a)": ["Proposition 6.3", "Lemma 3.10"], "Theorem 7.1(1b)": ["Theorem 7.1(1a)"], "Theorem 7.1(1c) sharpness": ["Proposition 3.5", "Lemma 2.1"], "Theorem 7.1(2)": ["Proposition 6.4", "Lemma 3.10"], "Theorem 7.1(3)": ["Proposition 6.5", "Lemma 3.10"], "Lemma 3.10": ["Lemma 3.9"], "Lemma 3.9": ["Definitions ... | B3 | judge | [] | [{"text":"# WEIGHTED $L^2$ THEORY FOR THE EUCLIDEAN DIRAC OPERATOR IN HIGHER DIMENSIONS\n\nABSTRACT. We study weighted $L^{2}$ solvability for the Euclidean Dirac operator in dimensions $n \\geq 3$ . We prove that, on the exterior domain $\\mathbb{R}^{n} \\setminus \\overline{B(0,1)}$ with logarithmic weight $\\varphi ... | aug_B3_1_2__zh_interleave | aug_B3_1_2 | zh_interleave | interleave | zh | 10 | [] | |||||
375 | 你是一名理论物理助手。阅读提供的三篇论文：
- Paper A: Solving the Quantum Many-Body Problem with Artificial Neural Networks
- Paper B: Quantum Entanglement in Neural Network States
- Paper C: Approximating quantum many-body wave functions using artificial neural networks
请使用 Paper A 的记号体系（即以 $S$, $\Psi_M(S;\mathcal{W})$, $\mathcal{W}=\... | [{"paper": "B", "eq_label": "Eq.(1)", "rewritten_formula": "\\Psi_M(\\mathcal{S}; \\mathcal{W}) = \\sum_{\\{h_i\\}} \\exp\\left(\\sum_{j=1}^N a_j \\sigma_j^z + \\sum_{i=1}^M b_i h_i + \\sum_{i=1}^M \\sum_{j=1}^N W_{ij} h_i \\sigma_j^z\\right)"}, {"paper": "B", "eq_label": "Rényi entropy", "rewritten_formula": "S_{\\alp... | D1 | judge | [
"76/76d011dd91c620f677daa9e60403d25564cc12006a52ce7b1d776e83c0075774.jpg",
"f0/f0ae8d5aef751f63e109518eb760704db5b96287384ec6330b3e16f6e31afb29.jpg",
"0d/0d03978749c65ab1ef9135951cb7dfe85ad4593055ccc0b128adad78aa0473ed.jpg",
"73/732c6b2fa5d12142e5a9df659d443c50bcd0c218916dca1177b7abb6eed172a5.jpg",
"32/3216... | [{"text":"# Solving the Quantum Many-Body Problem with Artificial Neural Networks\n\nNumerical approaches directly relying on the wave function can either sample a finite number of physically relevant configurations or perform an efficient compression of the quantum state. Stochastic approaches, like quantum Monte Carl... | aug_D1_1_2__zh_interleave | aug_D1_1_2 | zh_interleave | interleave | zh | 14 | [
"images/SciDocBench/76/76d011dd91c620f677daa9e60403d25564cc12006a52ce7b1d776e83c0075774.jpg",
"images/SciDocBench/f0/f0ae8d5aef751f63e109518eb760704db5b96287384ec6330b3e16f6e31afb29.jpg",
"images/SciDocBench/0d/0d03978749c65ab1ef9135951cb7dfe85ad4593055ccc0b128adad78aa0473ed.jpg",
"images/SciDocBench/73/732c6... | |||||
376 | 你是一名 AI 生物信息学研究助理。以下是以 JSON 格式表示的文章文件结构树::
{
"01_identify_mutations": [
"140gene_fasta_new_8species.R",
"TBXT_8species_03.csv",
"TBXT_new_8species.fasta",
"forloop_python.sh",
"gene140_location.csv",
"mutation.py"
],
"02_define_mutations": [
"inputs",
"mutation_classifier.R",
"... | {
"question_1": {
"script_name": "mutation.py",
"folder_path": "01_identify_mutations"
},
"question_2": {
"dna_analysis_chain": [
"01_identify_mutations/mutation.py",
"02_define_mutations/mutation_classifier.R",
"04_filter_vep_results/filter_vep_visualization.R"
]
}
} | F2 | judge | [
"ba/ba950fab997e8e87674de61091874c71c4b8138059902ee44555faac5bfd3114.jpg",
"c3/c33704edf3457b0cdcefae2acef13e240be69aa25f60a6f31547fb0c21d819e0.jpg",
"b4/b42515659d5a4132c4eba00f709ba497d61a6c277af6864e1b44b79ad48ac8b2.jpg",
"90/90c69637f6f4f16a342ad8b53da5e39460bd97156623962dd30929ca91bc968c.jpg",
"0e/0e33... | [{"text":"# On the genetic basis of tail-loss evolution in humans and apes\n\nIt has long been speculated that tail loss in hominoids contributed to orthograde and bipedal locomotion, the evolutionary occurrence of which coincided with the loss of the tail $^{15-17}$ . Yet, the genetic mechanism that facilitated either... | aug_F2_1_1__zh_interleave | aug_F2_1_1 | zh_interleave | interleave | zh | 22 | [
"images/SciDocBench/ba/ba950fab997e8e87674de61091874c71c4b8138059902ee44555faac5bfd3114.jpg",
"images/SciDocBench/c3/c33704edf3457b0cdcefae2acef13e240be69aa25f60a6f31547fb0c21d819e0.jpg",
"images/SciDocBench/b4/b42515659d5a4132c4eba00f709ba497d61a6c277af6864e1b44b79ad48ac8b2.jpg",
"images/SciDocBench/90/90c69... | |||||
377 | 你将获得一篇科学 PDF 文档。你的任务是提取并复现文档第 3 页中标题为 "3. MM-IFEval Benchmark" 的章节正文。
请按以下精确格式将答案作为 JSON 字典返回：
{"extraction": "<the extracted LaTeX body text>"}
请严格遵循以下针对 extraction 字段的规则：
- 包含该章节正文中的所有连续正文和段落文本，完全保留原始措辞。
- 使用 LaTeX 命令保留所有原始粗体和斜体格式：\textbf{} 表示粗体，\textit{} 表示斜体。
- 完全排除以下内容：图注、表注、脚注、脚注标记、公式，以及任何 boxed callouts 或 inset ... | "Our MM-IFEval comprises \\textbf{400 human-annotated questions}: 300 \\textit{compose-level} open-ended questions and 100 \\textit{perception-level} questions with ground truth. With 32 distinct constraint categories and an average of 5.1 constraints per question, MM-IFEval is substantially more challenging than prior... | A1 | judge | [
"0c/0cd5e8adad75b92f9294d32075911185bedccb5406031bf52d843da571b0ad34.jpg",
"5a/5a15a7798b801c703df7f429e519480c38a30bf85195c4c42df0126c4e613ade.jpg",
"89/89d0d615fcf4a1b1145fc2e83011f6b468bac61fe3fb94acce0fb2af0e9dd00d.jpg",
"bd/bdb696f537b4e218f37538b68387de9a2c6cf7b6e024f1442a1f7bf6e1b3ce07.jpg",
"1e/1ec5... | [{"text":"# MM-IFEngine: Towards Multimodal Instruction Following\n\nShengyuan Ding $^{1,2*}$ , Shenxi Wu $^{1,2*}$ , Xiangyu Zhao $^{2,3}$ , Yuhang Zang $^{2\\boxtimes}$ , Haodong Duan $^{2}$ , Xiaoyi Dong $^{2}$ , Pan Zhang $^{2}$ , Yuhang Cao $^{2}$ , Dahua Lin $^{2,4,5}$ , Jiaqi Wang $^{2,6\\boxtimes}$ $^{1}$ Fudan... | mmifengine_001__zh_interleave | mmifengine_001 | zh_interleave | interleave | json | You are an expert evaluator for scientific document extraction tasks.
The task: extract the verbatim body text of section "3. MM-IFEval Benchmark" from the paper as LaTeX, excluding the section heading, figure/table captions, footnotes, and equations.
Reference answer:
{answer}
Model prediction:
{prediction}
## Sco... | {"reasoning": "Section 3 on page 3 contains a single paragraph of body prose before transitioning to subsection 3.1. The scored text is that paragraph verbatim with LaTeX formatting preserved. Excluded elements include the section heading '3. MM-IFEval Benchmark', the subsection heading '3.1 Hybrid Evaluation Strategy'... | mmifengine | cs | zh | mmifengine_001 | [
"images/SciDocBench/0c/0cd5e8adad75b92f9294d32075911185bedccb5406031bf52d843da571b0ad34.jpg",
"images/SciDocBench/5a/5a15a7798b801c703df7f429e519480c38a30bf85195c4c42df0126c4e613ade.jpg",
"images/SciDocBench/89/89d0d615fcf4a1b1145fc2e83011f6b468bac61fe3fb94acce0fb2af0e9dd00d.jpg",
"images/SciDocBench/bd/bdb69... |
378 | 你将获得一篇科学 PDF 文档。你的任务是在论文中定位支持摘要中以下声明的具体证据：
"LLaVA-CoT 不仅在广泛的多模态推理基准上比其基础模型高出 9.4%"
请按以下步骤操作：
1. 确定论文中的哪一个图、表或章节直接提供了支持该声明的数值数据或实验结果。
2. 按以下精确格式将你的答案作为 JSON 字典返回：
{"location": "<e.g. Table 1, Figure 3, Section 4.2>"}
附加规则：
- location 字段必须使用论文中出现的具体标签（例如 Table 2, Figure 1, Section 4.2），而不是像“结果章节”这样的模糊描述。
- 如果 9.4% 这个数字并未... | {"location": "Table 5"} | A2 | json_match | [
"6b/6bf75f6e320d847fa73d249f15a05f836fd2d2a38667fa8fc9d9c404e25ff936.jpg",
"fe/fe5f4e74f960728d794b0c94ffdfb33368e4f348e42271793c564ebc0568f7eb.jpg",
"c7/c79150158726751519cc721bbe2365ec6b090b279498186e386f6c64a587261e.jpg",
"b7/b72757d02439fb5d966ad73427272b7b9f1402695f460ed2b0e48de36be0a150.jpg",
"b7/b72a... | [{"text":"# LLaVA-CoT: Let Vision Language Models Reason Step-by-Step\n\nLarge language models have demonstrated substantial advancements in reasoning capabilities. However, current Vision-Language Models (VLMs) often struggle to perform systematic and structured reasoning, especially when handling complex visual quest... | llavacot_001__zh_interleave | llavacot_001 | zh_interleave | interleave | json | {"reasoning": "Table 5 presents experimental results comparing LLaVA-CoT and state-of-the-art models on reasoning benchmarks. The base model, Llama-3.2-Vision-Instruct (11B), achieves an average score of 56.9. LLaVA-CoT (w/ scaling), also 11B, achieves an average score of 66.3. The 9.4% figure is the direct arithmetic ... | llavacot | cs | zh | llavacot_001 | [
"images/SciDocBench/6b/6bf75f6e320d847fa73d249f15a05f836fd2d2a38667fa8fc9d9c404e25ff936.jpg",
"images/SciDocBench/fe/fe5f4e74f960728d794b0c94ffdfb33368e4f348e42271793c564ebc0568f7eb.jpg",
"images/SciDocBench/c7/c79150158726751519cc721bbe2365ec6b090b279498186e386f6c64a587261e.jpg",
"images/SciDocBench/b7/b7275... | |
379 | 你将获得一篇科学 PDF 文档。你的任务是对以下每个引用在论文文本中出现时所扮演的角色进行分类：
[Sch+15b], [Mni+16], [Wan+16], [Hee+17], [KL02]
# Citation Roles
- **Background**：仅提供一般背景；介绍研究领域、说明问题动机，或命名相关工作。本文在实验中并未直接基于该工作构建，也未与其进行比较。Background 与所有其他角色互斥：如果某个引用也符合 Method、Comparison 或 Extension，则不要添加 Background。
- **Method**：本文作为自身方法的一部分直接改编、扩展或基于其构建的技术、目标、框架或算法。
... | {"[Sch+15b]": "Comparison,Method", "[Mni+16]": "Comparison,Method", "[Wan+16]": "Comparison", "[Hee+17]": "Extension", "[KL02]": "Background"} | A3 | json_match | [
"5a/5ad2a24c1609a9ddeb84f25c73f030fa84fa3bc5a3aed8c1b96ead662219954f.jpg",
"e7/e7118e702d2cde001fff2c8fbc61d84cb80cba30c10591421ba365639bf84dbc.jpg",
"49/495ed4ea9c52b22c97802f474ab72457344ec4953ae72caec723b43128cafffa.jpg",
"f0/f06e8b053a5dc7195cfb5202b472c0be3ab5f0e11a53c1afeaddf34fbccd40c0.jpg",
"54/54b4... | [{"text":"# Proximal Policy Optimization Algorithms\n\nJohn Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg Klimov\n\nOpenAI\n\n{joschu, filip, prafulla, alec, oleg}@openai.com\n\n## Abstract\n\nWe propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling... | ppo_001__zh_interleave | ppo_001 | zh_interleave | interleave | json | {"reasoning": "[Sch+15b] TRPO is Comparison+Method: PPO's surrogate objective directly builds on TRPO's L_CPI framework (page 2: \"In TRPO [Sch+15b], an objective function (the 'surrogate' objective) is maximized subject to a constraint on the size of the policy update\"), and TRPO is simultaneously used as an experime... | ppo | cs | zh | ppo_001 | [
"images/SciDocBench/5a/5ad2a24c1609a9ddeb84f25c73f030fa84fa3bc5a3aed8c1b96ead662219954f.jpg",
"images/SciDocBench/e7/e7118e702d2cde001fff2c8fbc61d84cb80cba30c10591421ba365639bf84dbc.jpg",
"images/SciDocBench/49/495ed4ea9c52b22c97802f474ab72457344ec4953ae72caec723b43128cafffa.jpg",
"images/SciDocBench/f0/f06e8... | |
380 | 你将获得一篇介绍 GSPO (Group Sequence Policy Optimization) 算法的科学 PDF 文档。该论文在其编号方程中引入并使用了若干数学符号。
你的任务：对于下方列出的十个符号中的每一个，识别它在论文中**首次出现**的方程编号——无论是作为定义的主要对象（例如，"symbol = ..."），还是作为方程主体的一部分。
符号：
- w_t(\theta)
- \mathcal{J}_\text{PPO}(\theta)
- \mathcal{J}_\text{GRPO}(\theta)
- w_{i,t}(\theta)
- \widehat{A}_{i,t}
- \widehat{A}_i
-... | {"w_t(\\theta)": "Eq. (1)", "\\mathcal{J}_\\text{PPO}(\\theta)": "Eq. (1)", "\\mathcal{J}_\\text{GRPO}(\\theta)": "Eq. (2)", "w_{i,t}(\\theta)": "Eq. (3)", "\\widehat{A}_{i,t}": "Eq. (3)", "\\widehat{A}_i": "Eq. (3)", "\\mathcal{J}_\\text{GSPO}(\\theta)": "Eq. (5)", "s_i(\\theta)": "Eq. (7)", "\\mathcal{J}_\\text{GSPO-... | B1 | json_match | [
"d9/d902311adc6733c74a88da896cf655147fe1c030b62a2021516b17ca038eb3d1.jpg",
"05/05f42e65631a60881251cc61407600298a7ebe5c994a81e07e9fc6e3929a870d.jpg",
"89/89dd8ee64f5431ec969dda6ada5253dadf7fe77604ccf0b0e01b52d440834b80.jpg",
"c3/c3e28311b7757dc4460986387c89d429abbb67755589bb8651ab5759dda8ce05.jpg",
"82/82c3... | [{"text":"# Group Sequence Policy Optimization\n\nChujie Zheng\\* Shixuan Liu Mingze Li Xiong-Hui Chen Bowen Yu\\* Chang Gao Kai Dang Yuqiong Liu Rui Men An Yang Jingren Zhou Junyang Lin Qwen Team, Alibaba Inc.\n\n## Abstract\n\nThis paper introduces Group Sequence Policy Optimization (GSPO), our stable, efficient, and... | gspo_001__zh_interleave | gspo_001 | zh_interleave | interleave | json | {"reasoning": "w_t(θ): appears in the body of Eq.(1), the PPO objective, as the token-level importance ratio πθ(y_t|x,y_{<t}) / πθold(y_t|x,y_{<t}); its inline definition follows immediately after Eq.(1). \\mathcal{J}_PPO(θ): is the subject of Eq.(1), the PPO objective function. Both w_t and J_PPO first appear in Eq.(1... | gspo | cs | zh | gspo_001 | [
"images/SciDocBench/d9/d902311adc6733c74a88da896cf655147fe1c030b62a2021516b17ca038eb3d1.jpg",
"images/SciDocBench/05/05f42e65631a60881251cc61407600298a7ebe5c994a81e07e9fc6e3929a870d.jpg",
"images/SciDocBench/89/89dd8ee64f5431ec969dda6ada5253dadf7fe77604ccf0b0e01b52d440834b80.jpg",
"images/SciDocBench/c3/c3e28... | |
381 | 你将获得一篇科学 PDF 文档，其中描述了 Spatial-SSRL，一个用于视觉语言模型空间推理的自监督强化学习框架。
你的任务：从论文中提取以下十个配置值。所有值都必须逐字取自论文——不要推断或计算。
返回一个仅包含以下十个键的单个 JSON 对象：
{
"sft_lr": "<learning rate used in the SFT cold-start stage, in scientific notation, e.g. 1e-5>",
"grpo_lr": "<learning rate used in the GRPO reinforcement le... | {"sft_lr": "1e-5", "grpo_lr": "1e-6", "grpo_batch_size": "128", "grpo_steps": "360", "reward_acc_weight": "0.9", "dataset_name": "Spatial-SSRL-81k", "dataset_size": "81053", "depth_r_max": "0.15", "depth_d_min": "0.05", "pos_pixel_threshold": "150", "sft_num_samples": "3600", "flipped_task_size": "4005", "pos_depth_thr... | B2 | json_match | [
"f4/f4ab7bfd1961eec716fd1d8a933556858e4a2503689ac4f95000821f0d46a08b.jpg",
"b3/b338778f7d447ae15465957a7f5872c3e1c19a3c17f11c508b107d5a46f19928.jpg",
"a5/a55ee03b11c347d91300550a48f1bab80d923bdcc67cc484625e774ebdba5f52.jpg",
"c4/c425123f770801e3b4080213b46a23e5c5f0f3971d2633502e35139761fe5d85.jpg",
"4f/4f22... | [{"text":"# Spatial-SSRL: Enhancing Spatial Understanding via Self-Supervised Reinforcement Learning"},{"image":"f4/f4ab7bfd1961eec716fd1d8a933556858e4a2503689ac4f95000821f0d46a08b.jpg"},{"text":"(a) Comparison of Responses to Spatial Understanding Problems \nFigure 1. We present Spatial-SSRL, a self-supervised reinfo... | spatial-ssrl_001__zh_interleave | spatial-ssrl_001 | zh_interleave | interleave | json | {"reasoning": "sft_lr (1e-5), grpo_lr (1e-6), grpo_batch_size (128), grpo_steps (360): all from §4.1 'In the cold-start stage, we train for 5 epochs on the SFT data with a learning rate of 1×10^{-5}. ... The training uses a global batch size of 128 and a learning rate of 1×10^{-6} for 360 steps.' reward_acc_weight (0.9... | spatial-ssrl | cs | zh | spatial-ssrl_001 | [
"images/SciDocBench/f4/f4ab7bfd1961eec716fd1d8a933556858e4a2503689ac4f95000821f0d46a08b.jpg",
"images/SciDocBench/b3/b338778f7d447ae15465957a7f5872c3e1c19a3c17f11c508b107d5a46f19928.jpg",
"images/SciDocBench/a5/a55ee03b11c347d91300550a48f1bab80d923bdcc67cc484625e774ebdba5f52.jpg",
"images/SciDocBench/c4/c4251... | |
382 | 给定一篇科学 PDF 文档（PPO: Proximal Policy Optimization Algorithms, Schulman et al. 2017）。
该论文在第 2–5 页包含 12 个编号方程（Eq. 1 到 Eq. 12）。你的任务是构建一个 Directed Acyclic Graph (DAG)，表示这些方程之间的逻辑推导依赖关系。
定义：
- 有向边 A → B 表示“Eq. B 直接依赖于 Eq. A”（即 B 明确使用了 A 引入的结果、记号或结构）。
- 节点分类：
- Definition：论文中没有数学前驱的基础方程
- Intermediate：建立在先前方程之上的推导方程，但不是论... | {"axioms": ["Eq. (1)", "Eq. (2)", "Eq. (10)", "Eq. (12)"], "main_clip_objective": "Eq. (7)", "Eq. (6)_direct_deps": ["Eq. (3)"], "Eq. (7)_direct_deps": ["Eq. (6)"], "Eq. (9)_direct_deps": ["Eq. (7)"], "Eq. (5)_direct_deps": ["Eq. (3)", "Eq. (4)"], "Eq. (8)_direct_deps": ["Eq. (5)", "Eq. (6)"], "Eq. (11)_direct_deps": [... | B3 | judge | [
"5a/5ad2a24c1609a9ddeb84f25c73f030fa84fa3bc5a3aed8c1b96ead662219954f.jpg",
"e7/e7118e702d2cde001fff2c8fbc61d84cb80cba30c10591421ba365639bf84dbc.jpg",
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"f0/f06e8b053a5dc7195cfb5202b472c0be3ab5f0e11a53c1afeaddf34fbccd40c0.jpg",
"54/54b4... | [{"text":"# Proximal Policy Optimization Algorithms\n\nJohn Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg Klimov\n\nOpenAI\n\n{joschu, filip, prafulla, alec, oleg}@openai.com\n\n## Abstract\n\nWe propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling... | ppo_002__zh_interleave | ppo_002 | zh_interleave | interleave | text | You are an expert evaluator for equation dependency graph tasks.
The task: construct a DAG of logical derivation dependencies among Eq.(1)–Eq.(12) in the PPO paper, with node classifications (Definition / Intermediate / Theorem).
Reference key facts:
{answer}
Model prediction:
{prediction}
## Scoring instructions
... | {"reasoning": "The 12 equations span pages 2–5. Axioms/Definitions: Eq.(1) is the policy gradient estimator ĝ; Eq.(2) is L^PG(θ), the surrogate whose gradient equals Eq.(1); Eq.(10) is the truncated-return advantage estimator Â_t; Eq.(12) is the TD residual δ_t. Critical CLIP path: Eq.(3) is the TRPO objective (maximiz... | ppo | cs | zh | ppo_002 | [
"images/SciDocBench/5a/5ad2a24c1609a9ddeb84f25c73f030fa84fa3bc5a3aed8c1b96ead662219954f.jpg",
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"images/SciDocBench/f0/f06e8... |
383 | 请仔细查看我输入的论文中的图 2，并确定：在 E = 2 MeV 时，三条曲线中哪一条在 y 轴上的截面值最大？请将你的答案输出为一个 JSON 对象：
{"answer": "<curve name>"} | "Tentori and Belloni" | A1 | judge | [
"c6/c6e562104018ee591a4a8957e83ca006b96375415f5c4ce66d684c0737567b0b.jpg",
"e2/e2a3185bb8226bc8920ee05a94b255f71f030a6ac3c6b73cf972bf7402d17641.jpg",
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"e4/e4f56f0093cc5a6dd044fb1e0d109eccd1d7c73cdc016495446e6bfb1ca82545.jpg",
"1c/1c27... | [{"text":"# Revisiting p- $^{11}$ B Fusion: Updated Cross-sections, Reactivity, and Energy Balance\n\nHong-Yi Wang, $^{1}$ Yu-Qi Li, $^{1}$ Qian Wu, $^{1}$ and Zhu-Fang Cui $^{1,*}$\n\nRecent experimental progress has substantially improved the available cross-section data for the p- $^{11}$ B fusion reaction, particul... | p11b_001__zh_interleave | p11b_001 | zh_interleave | interleave | text | You are an expert evaluator.
The question asks: at E = 2 MeV in Figure 2, which of the three curves has the largest cross-section value?
Reference answer: {answer}
Model prediction: {prediction}
## Scoring instructions
- Score 1.0 if the prediction correctly identifies "Tentori and Belloni" (case-insensitive, abbr... | {"reasoning": "Figure 2 (page 4) shows the p-11B fusion reaction cross-section σ(E) with four curves: Experimental data, Nevins and Swain (black dashed), Tentori and Belloni (blue dashed), and This work (red solid). At E = 2 MeV, the Tentori and Belloni (blue dashed) curve sits clearly above both Nevins and Swain and T... | p11b | physics | zh | p11b_001 | [
"images/SciDocBench/c6/c6e562104018ee591a4a8957e83ca006b96375415f5c4ce66d684c0737567b0b.jpg",
"images/SciDocBench/e2/e2a3185bb8226bc8920ee05a94b255f71f030a6ac3c6b73cf972bf7402d17641.jpg",
"images/SciDocBench/db/db83fce424ad83fdd3b652568f2d8134c8df11695ec01cc4fd256179a8ddce58.jpg",
"images/SciDocBench/e4/e4f56... |
384 | 阅读这份文档。文档中有多幅图；由于曲线拟合，即使在相同实验条件下，拟合后的预测实验结果也可能产生差异。我使用了哪幅图的曲线来得出以下预测实验结果？
A. At contact time=20 min, Dose=0.15g, pH=6, Temperature=25, stirring rate=50rpm, Concentration=100μg·L-1，the predicted uptake capacity for As(III)=37μg·g-1
B. At contact time=20 min, Dose=0.25g, pH=6, Temperature=25, stirring rate=50rpm, Concent... | {"A": "Figure 7", "B": "Figure 8"} | A2 | json_match | [
"ba/ba41bc1e3a757fea35be3e2035f23c71c43cbb1f8e7a8ac34d9f91ec82ae51f3.jpg",
"3e/3e4cdd28f23ce808c9978fd245a9f1f9d55b2f55f56b1fa420d81b86cb0abbe1.jpg",
"52/52b49c9674a5becf6cec44669181ece1936cb7aa12904dfd95f2741da754c393.jpg",
"af/af85a6b9bb357ddd47f29bfd5961df46524bcba0d97b39b207b2c9ffbd987124.jpg",
"8f/8fba... | [{"text":"ORIGINAL ARTICLE\n\nAbstract Batch and column operations were performed utilizing thioglycolated sugarcane carbon (TSCC), a low-cost adsorbent, to remove As(III) and As(V) from aqueous systems. Under optimized batch conditions, the TSCC could remove up to 92.7 and 91.4 % for As(III) and As(V), respectively. A... | environment3_001__zh_interleave | environment3_001 | zh_interleave | interleave | json | {"reasoning": "A: Dose=0.15g + As(III) candidates are Figure 5 (x-axis=Concentration) and Figure 7 (x-axis=Dose). The predicted uptake capacity at Concentration=100μg/L in Figure 5 is ~30μg/g, while in Figure 7 at Dose=0.15g the blue curve (Uptake Capacity) reads ~37μg/g. Only Figure 7 matches 37μg/g. B: Dose=0.25g + A... | environment3 | environment | zh | environment3_001 | [
"images/SciDocBench/ba/ba41bc1e3a757fea35be3e2035f23c71c43cbb1f8e7a8ac34d9f91ec82ae51f3.jpg",
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"images/SciDocBench/af/af85a... | |
385 | 你是一名论文阅读助手。在文章 *ProtFlowArticle* 中，对于以下每个引用，分类其在论文中所起的角色。使用这些类别：
1. Background: 提供理论支持或研究背景。
2. Method: 采用或与现有方法进行比较。
3. Result: 使用他人的结果作为比较基线。
4. Critical: 纯粹批评现有研究的局限性。
目标引用：[46], [3]
单个引用可能在不同出现位置具有多个角色。输出一个 JSON 对象，将每个引用标签映射到其在论文中任何位置所起角色的 JSON 列表。仅输出 JSON。
格式：
{"[46]": ["<role_1>", ...], "[3]": ["<role_1>", ...... | {"[46]": ["Method", "Result"], "[3]": ["Method", "Result"]} | A3 | json_match | [
"ef/ef0a3ae63b7d97588397e1b672213d34b692bac42854540605e5fabfbefe9ac5.jpg",
"6a/6aed035c2512d7b3268aa61d97eb5e6805a8fc79b28fd875152f8dd5d6127eb3.jpg",
"0b/0ba10a0fa08e44dd297c966c1c5632950db3adf43782fbb25f8c49bf044048ea.jpg",
"d7/d7b191bf8574794f3d7e801624037f9dedd198b7d1ee91f476dcf0dc721c2c6d.jpg",
"06/0632... | [{"text":"# ProtFlow: Flow Matching-based Protein Sequence Design with Comprehensive Protein Semantic Distribution Learning and High-quality Generation\n\nZitai Kong $^{1}$ , Yiheng Zhu $^{1}$ , Yinlong Xu $^{1}$ , Mingze Yin $^{1}$ , Tingjun Hou $^{4,6*}$ , Jian Wu $^{3,5*}$ , Hongxia Xu $^{2*}$ , and Chang-Yu Hsieh $... | ProtFlowArticle_001__zh_interleave | ProtFlowArticle_001 | zh_interleave | interleave | json | {"reasoning": {"[24]": {"2-Introduction-1": "ESM2 cited as evidence that LLMs can be applied to proteins (Background)", "2-Introduction-2": "ESM2 again cited to explain why LLMs capture protein semantics (Background)", "4-Semantically Meaningful Integration to pLM Latent Space-3": "Paper directly uses ESM-2 35M encoder... | ProtFlowArticle | biology | zh | ProtFlowArticle_001 | [
"images/SciDocBench/ef/ef0a3ae63b7d97588397e1b672213d34b692bac42854540605e5fabfbefe9ac5.jpg",
"images/SciDocBench/6a/6aed035c2512d7b3268aa61d97eb5e6805a8fc79b28fd875152f8dd5d6127eb3.jpg",
"images/SciDocBench/0b/0ba10a0fa08e44dd297c966c1c5632950db3adf43782fbb25f8c49bf044048ea.jpg",
"images/SciDocBench/d7/d7b19... | |
386 | 请阅读这篇论文，并按首次出现的顺序整理其中提到的所有数据集。
输出格式：
返回一个有效的扁平 JSON 对象。按照首次出现的顺序从 1 开始为数据集编号，使用形式为 "{N}.location", "{N}.name", "{N}.models" 的键：
{
"1.location": "<page_number>-<section_title_with_number>",
"1.name": "<dataset name>",
"1.models": "<model names, alphabetically sorted, comma-separated>",
"2.location": "...",
..... | {"1.location": "3-2.2 STRUCTURE-BASED PRE-TRAINING", "1.name": "AlphaFoldDB", "1.models": "ESM-IF, Evoformer-inspired ESM, GearNet, MIF", "2.location": "3-2.2 STRUCTURE-BASED PRE-TRAINING", "2.name": "Protein Data Bank (PDB)", "2.models": "MIF, MIF-ST", "3.location": "6-3.3.2 OBJECTIVE FUNCTION", "3.name": "SaProt pre-... | A4 | json_match | [
"79/7903336ddfccdf6f220d281f467be797d34bc02c46c04197432bb200ba57c9f8.jpg",
"9e/9eb08d22e204453d0ee039a5d9187e0f727cb38a40c45479f00822a33955d780.jpg",
"54/5408b79f90eb223eb609d8871553534246988733f482a7ad5dad080765f30c85.jpg",
"1f/1fec2fc37e0b1b3d5c3a4f714af16c078d924d070dcb0af4969a49f29b3a19c4.jpg",
"f5/f52c... | [{"text":"# SAPROT: PROTEIN LANGUAGE MODELING WITH STRUCTURE-AWARE VOCABULARY\n\nLarge-scale protein language models (PLMs), such as the ESM family, have achieved remarkable performance in various downstream tasks related to protein structure and function by undergoing unsupervised training on residue sequences. They h... | saprot_001__zh_interleave | saprot_001 | zh_interleave | interleave | {"reasoning": "18 datasets in chronological order. Key traps: (1) AlphaFoldDB vs PDB — both appear in §2.2 but serve different roles (AF2=structure pre-training at scale, PDB=experimental structures for SaProt-PDB); (2) SaProt's own pre-training corpus is distinct from AlphaFoldDB (it is derived from AF2 but is a curat... | saprot | biology | zh | saprot_001 | [
"images/SciDocBench/79/7903336ddfccdf6f220d281f467be797d34bc02c46c04197432bb200ba57c9f8.jpg",
"images/SciDocBench/9e/9eb08d22e204453d0ee039a5d9187e0f727cb38a40c45479f00822a33955d780.jpg",
"images/SciDocBench/54/5408b79f90eb223eb609d8871553534246988733f482a7ad5dad080765f30c85.jpg",
"images/SciDocBench/1f/1fec2... | ||
387 | 提取以下变量的具体数值、单位和首次出现位置。输出为一个 JSON 数组，其中对象遵循以下确切结构：
[
{
"symbol": <symbol name in $$>,
"value": <value>,
"unit": <unit>,
"trap_type": <PT or AT>,
"location": "Page <page>-Section <section>"
"definition": <The strict definition from the paper is derived from that specific sentence in the origina... | [{"symbol": "$B_{0,\\text{PT}}$", "value": "1.945", "unit": "T", "trap_type": "PT", "location": "Page 2-Section Experimental set-up", "definition": "The heart of our experiment is a superconducting solenoid magnet with a horizontal bore, operated at a magnetic field of $B_{0,PT} = 1.945$ T."}, {"symbol": "$\\nu_{+,\\te... | B1 | judge | [
"78/78254a541bfd874e5639f436bd992dc66cf6e2baac79810d8133d3c5419b0de2.jpg",
"09/09a3c49f0715ab5fb907b25610ba14a5b8061626a59d5b191b199311071ceefa.jpg",
"9c/9cb0a34475663850784438e2831e022b9bf4068bf01e836ab71ba6f042082ec4.jpg",
"84/846f053ede6c5f428c72d02958040c4c7f6c4fb966034ce4bae502eaf033925d.jpg",
"e3/e398... | [{"text":"# Coherent spectroscopy with a single antiproton spin\n\nhttps://doi.org/10.1038/s41586-025-09323-1\n\nReceived: 10 March 2025\n\nOpen access\n\nCheck for updates\n\nB. M. Latacz $^{1,2}$ , S. R. Erlewein $^{1,3,4}$ , M. Fleck $^{2,5}$ , J. I. Jäger $^{1,4}$ , F. Abbass $^{3}$ , B. P. Arndt $^{4,6}$ , P. Geis... | antiproton-spin_001__zh_interleave | antiproton-spin_001 | zh_interleave | interleave | json | You are an expert evaluator for scientific parameter extraction tasks in physics.
The task: extract values, units, trap types, locations, and definitions for 10 physical variables from a paper on antiproton spin spectroscopy.
Reference answer (JSON array, 10 items):
{answer}
Model prediction:
{prediction}
## Scorin... | {"reasoning": "Items 5 (Δν_{z,SF,PT}) and 7 (τ_{s,AT}) are marked null in the reference answer but values do exist in the paper: Δν_{z,SF}=173 mHz (page 3, spin-flip detection threshold Δν_{z,sf}/2=0.173/2 Hz) and τ_{s,AT}=5.4(6) s (Extended Data Fig. 1 caption, page 8). B₀,AT has a paper-internal discrepancy: page 2 t... | antiproton-spin | physics | zh | antiproton-spin_001 | [
"images/SciDocBench/78/78254a541bfd874e5639f436bd992dc66cf6e2baac79810d8133d3c5419b0de2.jpg",
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"images/SciDocBench/84/846f0... |
388 | 你将获得一篇关于 d-dimensional micropolar Rayleigh-Bénard problem 的弱解大时间行为的科学 PDF 文档。
你的任务：准确复现论文中的 equation (A.1)，以 LaTeX code 形式给出。
将你的答案作为 JSON dictionary 返回：
{"latex": "<the complete LaTeX code for equation (A.1), including \begin{equation}, \tag{A.1}, and the cases environment>"}
规则：
- 按论文中出现的样子准确复现该方程，包括所有算子、投影、下标和上标。... | "\\begin{equation}\n\\tag{A.1}\n\\begin{cases}\n\\partial_t u^N + \\mathbb{P} J_N (\\mathbb{P} J_N u^N \\cdot \\nabla \\mathbb{P} J_N u^N) = (\\mu + \\chi) \\Delta \\mathbb{P} J_N u^N + 2\\chi \\nabla \\times J_N w^N + J_N \\theta^N e_d, \\\\[1ex]\n\\partial_t w^N + J_N (\\mathbb{P} J_N u^N \\cdot \\nabla J_N w^N) - \\... | B1 | judge | [
"37/375e23497d19c9e77d1de7458090e56431154037cf859d7539bfa64266d0e359.jpg"
] | [{"text":"Regular Articles\n\n# Large time behavior of weak solutions to d-dimensional micropolar Rayleigh-Bénard problem"},{"image":"37/375e23497d19c9e77d1de7458090e56431154037cf859d7539bfa64266d0e359.jpg"},{"text":"Keywords: \nMicropolar Rayleigh-Bénard problem \nLarge time behavior \nGlobal weak solutions\n\nThis... | micropolar-rb_001__zh_interleave | micropolar-rb_001 | zh_interleave | interleave | text | You are an expert evaluator for LaTeX equation reproduction tasks in mathematical analysis.
The task: reproduce equation (A.1) from a paper on micropolar Rayleigh-Bénard equations exactly in LaTeX.
Reference answer:
{answer}
Model prediction:
{prediction}
## Scoring instructions
Score based on the following key fa... | {"reasoning": "Equation (A.1) on page 20 is the Galerkin approximate system for the micropolar RB problem. Key traps: (1) Line 1 LHS convection has two \\mathbb{P} projections: \\mathbb{P} J_N (\\mathbb{P} J_N u^N · \\nabla \\mathbb{P} J_N u^N) — models often drop the inner one; (2) Line 2 has -\\eta \\nabla \\nabla · ... | micropolar-rb | math | zh | micropolar-rb_001 | [
"images/SciDocBench/37/375e23497d19c9e77d1de7458090e56431154037cf859d7539bfa64266d0e359.jpg"
] |
389 | 阅读所提供论文的第 6.2 节，其中定义了用于评估点云重建的指标。按这些指标在该节中被引入的顺序，列出它们的名称。输出一个由指标名称组成的 JSON 列表。
Format:
["<metric_1>", "<metric_2>", ...] | ["Chamfer Distance", "Precision", "Recall", "F1-score"] | A1 | json_match | [
"d7/d7ef77ceb057751425b6f46b1d7cf9368539796729018cd3436dcc5cb7b0690d.jpg",
"5a/5a22e4539681dd3f4edfb98720a1de3d878693417c6d27a127eff023ffb987b0.jpg",
"77/77ec4333863d500d9a3e62027712a7343ce9cd05f29e4266df42a0e22589be27.jpg",
"8f/8f9e551326ee46b54ecb8a49b33ca3889710df4c53095cb4db5f5d4caeab4149.jpg",
"cd/cd39... | [{"text":"# Depth Anything 3: Recovering the Visual Space from Any Views\n\nWe present Depth Anything 3 (DA3), a model that predicts spatially consistent geometry from an arbitrary number of visual inputs, with or without known camera poses. In pursuit of minimal modeling, DA3 yields two key insights: a single plain tr... | depth-anything3_001__zh_interleave | depth-anything3_001 | zh_interleave | interleave | json | {"reasoning": "The subsection 'Resolution metrics' does not exist in the paper. The correct subsection on page 13 is titled 'Reconstrution metrics.' (paper's own typo for Reconstruction), within section 6.2 Metrics. This is a misleading-title trap: models that search literally for 'Resolution metrics' will fail to loca... | depth-anything3 | cs | zh | depth-anything3_001 | [
"images/SciDocBench/d7/d7ef77ceb057751425b6f46b1d7cf9368539796729018cd3436dcc5cb7b0690d.jpg",
"images/SciDocBench/5a/5a22e4539681dd3f4edfb98720a1de3d878693417c6d27a127eff023ffb987b0.jpg",
"images/SciDocBench/77/77ec4333863d500d9a3e62027712a7343ce9cd05f29e4266df42a0e22589be27.jpg",
"images/SciDocBench/8f/8f9e5... | |
390 | 你将获得一篇科学 PDF 文档。你的任务是定位支持以下实验结果的证据：第一页左侧图表中洋红色条目对应的模型性能。
请遵循以下步骤：
1. 识别该实验结果。
2. 识别论文中哪个图、表或章节直接提供了证实该结果的数值数据。
3. 按以下精确格式将你的答案作为 JSON 字典返回：
{"result": "<The accuracy value of the corresponding experimental result>", "location": "<e.g. Table 1, Figure 3, Section 4.2>"}
附加规则：
- result 字段必须是一个数值。
- location 字段必须使用论文中出现的... | {"result": "94.6", "location": "Table 4"} | A2 | json_match | [
"d7/d7ef77ceb057751425b6f46b1d7cf9368539796729018cd3436dcc5cb7b0690d.jpg",
"5a/5a22e4539681dd3f4edfb98720a1de3d878693417c6d27a127eff023ffb987b0.jpg",
"77/77ec4333863d500d9a3e62027712a7343ce9cd05f29e4266df42a0e22589be27.jpg",
"8f/8f9e551326ee46b54ecb8a49b33ca3889710df4c53095cb4db5f5d4caeab4149.jpg",
"cd/cd39... | [{"text":"# Depth Anything 3: Recovering the Visual Space from Any Views\n\nWe present Depth Anything 3 (DA3), a model that predicts spatially consistent geometry from an arbitrary number of visual inputs, with or without known camera poses. In pursuit of minimal modeling, DA3 yields two key insights: a single plain tr... | depth-anything3_002__zh_interleave | depth-anything3_002 | zh_interleave | interleave | json | {"reasoning": "The left-most chart on page 1 is a bar chart titled 'Monocular Depth' showing three bars: DA2=90.3 (orange), DA3=92.4 (blue), DA3-Teacher=94.6 (magenta). The magenta bar is DA3-Teacher with value 94.6. Table 4 (page 15) provides monocular depth comparisons (δ1) across 5 benchmarks (KITTI, NYU, SINTEL, ET... | depth-anything3 | cs | zh | depth-anything3_002 | [
"images/SciDocBench/d7/d7ef77ceb057751425b6f46b1d7cf9368539796729018cd3436dcc5cb7b0690d.jpg",
"images/SciDocBench/5a/5a22e4539681dd3f4edfb98720a1de3d878693417c6d27a127eff023ffb987b0.jpg",
"images/SciDocBench/77/77ec4333863d500d9a3e62027712a7343ce9cd05f29e4266df42a0e22589be27.jpg",
"images/SciDocBench/8f/8f9e5... | |
391 | 你将获得一篇科学 PDF 文档。你的任务是对以下每个引用在论文文本中出现时所扮演的角色进行分类：
[34], [17], [22], [46], [35]
# Citation Roles
- **Background**：仅提供一般背景；介绍研究领域、阐明问题动机，或指出相关工作。该论文在其实验中并未直接基于该工作构建或与其进行比较。Background 与所有其他角色互斥：如果某个引用也符合 Method、Comparison 或 Extension，则不要添加 Background。
- **Method**：本文作为自身方法的一部分直接改编、扩展或基于其构建的技术、目标、框架或算法。
- **Comparison**：在本... | {"[34]": "Comparison,Method", "[17]": "Background", "[22]": "Method", "[46]": "Comparison", "[35]": "Background"} | A3 | json_match | [
"f6/f655dca68cfca8ca2c0c7b299667d834f627a87f18441c715150c28be7cf7309.jpg",
"5b/5b827bbe34f1a9b1e16632ad2aae08ec50b79ca947756c2a045650896e478570.jpg",
"da/da830b179aed8c38b3acf3359307ce8a27af06edf5ad5d6afcc2163acb547883.jpg"
] | [{"text":"# Spa3R: Predictive Spatial Field Modeling for 3D Visual Reasoning\n\n## Abstract\n\nWhile Vision-Language Models (VLMs) exhibit exceptional 2D visual understanding, their ability to comprehend and reason about 3D space—a cornerstone of spatial intelligence—remains superficial. Current methodologies attempt t... | spa3r_001__zh_interleave | spa3r_001 | zh_interleave | interleave | json | {"reasoning": "[34] VGGT is Comparison+Method: the paper directly adapts VGGT as the Asymmetric View Aggregator backbone (Sec 3.2: \"adapts the pre-trained VGGT [34] to extract spatially-aligned features\"; weights initialized from VGGT in Sec 4.2), and simultaneously uses VGGT as an experimental baseline in the ablati... | spa3r | cs | zh | spa3r_001 | [
"images/SciDocBench/f6/f655dca68cfca8ca2c0c7b299667d834f627a87f18441c715150c28be7cf7309.jpg",
"images/SciDocBench/5b/5b827bbe34f1a9b1e16632ad2aae08ec50b79ca947756c2a045650896e478570.jpg",
"images/SciDocBench/da/da830b179aed8c38b3acf3359307ce8a27af06edf5ad5d6afcc2163acb547883.jpg"
] | |
392 | 请阅读这篇论文，并按它们首次出现的顺序整理其中提到的所有数据集。
输出格式：
返回一个有效的扁平 JSON 对象。按首次出现的顺序从 1 开始为数据集编号，使用形式为 "{N}.location", "{N}.name", "{N}.models" 的键：
{
"1.location": "<page_number>-<section_title_with_number>",
"1.name": "<dataset name>",
"1.models": "<model names, alphabetically sorted, comma-separated>",
"2.location": "...",
.... | {"1.location": "1-Abstract", "1.name": "MM-IFInstruct-23k", "1.models": "LLaVA-Next-Llama3-8B, Qwen2-VL-7B-Instruct", "2.location": "1-Abstract", "2.name": "MM-IFDPO-23k", "2.models": "LLaVA-Next-Llama3-8B, Qwen2-VL-7B", "3.location": "1-Abstract", "3.name": "MM-IFEval", "3.models": "Claude-3.5V-Sonnet, GPT-4o, InternV... | A4 | json_match | [
"0c/0cd5e8adad75b92f9294d32075911185bedccb5406031bf52d843da571b0ad34.jpg",
"5a/5a15a7798b801c703df7f429e519480c38a30bf85195c4c42df0126c4e613ade.jpg",
"89/89d0d615fcf4a1b1145fc2e83011f6b468bac61fe3fb94acce0fb2af0e9dd00d.jpg",
"bd/bdb696f537b4e218f37538b68387de9a2c6cf7b6e024f1442a1f7bf6e1b3ce07.jpg",
"1e/1ec5... | [{"text":"# MM-IFEngine: Towards Multimodal Instruction Following\n\nShengyuan Ding $^{1,2*}$ , Shenxi Wu $^{1,2*}$ , Xiangyu Zhao $^{2,3}$ , Yuhang Zang $^{2\\boxtimes}$ , Haodong Duan $^{2}$ , Xiaoyi Dong $^{2}$ , Pan Zhang $^{2}$ , Yuhang Cao $^{2}$ , Dahua Lin $^{2,4,5}$ , Jiaqi Wang $^{2,6\\boxtimes}$ $^{1}$ Fudan... | mmifengine_002__zh_interleave | mmifengine_002 | zh_interleave | interleave | json | {"reasoning": "7 datasets/benchmarks in order. Key traps: (1) ALLaVA — only in footnote 3 on page 2 (Sec 2.1), easy to miss; (2) LLaVA-Instruct — only in footnote 2 on page 1 (Introduction), easy to miss; (3) IFEval — only mentioned as '+12.3%' in Abstract, never has a dedicated section; (4) MM-IFInstruct-23k trains Qw... | mmifengine | cs | zh | mmifengine_002 | [
"images/SciDocBench/0c/0cd5e8adad75b92f9294d32075911185bedccb5406031bf52d843da571b0ad34.jpg",
"images/SciDocBench/5a/5a15a7798b801c703df7f429e519480c38a30bf85195c4c42df0126c4e613ade.jpg",
"images/SciDocBench/89/89d0d615fcf4a1b1145fc2e83011f6b468bac61fe3fb94acce0fb2af0e9dd00d.jpg",
"images/SciDocBench/bd/bdb69... | |
393 | 请阅读这篇论文，并整理所有对 ChartSFT 有贡献的源数据集，这些源数据集列在表 1 的列标题中，按从左到右出现的顺序。
输出格式：
返回一个有效的扁平 JSON 对象。按从 1 开始、从左到右的顺序为来源编号，使用形式为 "{N}.location", "{N}.name", "{N}.tasks" 的键：
{
"1.location": "<page_number>-<section_title_with_number>",
"1.name": "<source name exactly as shown in the table header, without citation bracket numbers>"... | {"1.location": "4-3.1.1 Chart-to-Table Translation", "1.name": "ChartQA", "1.tasks": "Chart-to-Table Translation, Open-ended Question Answering", "2.location": "4-3.1.1 Chart-to-Table Translation", "2.name": "PlotQA", "2.tasks": "Chart Summarization, Chart-to-Table Translation, Numerical Question Answering, Open-ended ... | A4 | json_match | [
"4a/4af90b951af96cabc7d8ae3a447400fcfa6e7618b8db5b41f0a24b6b1353f78e.jpg",
"11/119d8a9bac844e575ec2e62f047487766a0810ff95dc767e222d1876d6421ed1.jpg",
"93/93fe3ab6a0a69aa81d1387d7f377d24ba679513379832b39d5512984a07651f1.jpg",
"74/7408ab4803cfef9f4891a3c3c56ce34d90209955460ac0c1e073a3cce21b50d0.jpg",
"e4/e434... | [{"text":"# ChartAssistant: A Universal Chart Multimodal Language Model via Chart-to-Table Pre-training and Multitask Instruction Tuning\n\n$^{1}$ OpenGVLab, Shanghai AI Laboratory $^{2}$ Shanghai Jiao Tong University $^{3}$ The University of Hong Kong $^{4}$ Nanjing University\n\n## Abstract"},{"image":"4a/4af90b951af... | chartassistant_001__zh_interleave | chartassistant_001 | zh_interleave | interleave | {"reasoning": "Table 1 on page 4 lists 10 column sources left to right: ChartQA, PlotQA, OpenCQA, ScigraphQA, Vistext, Chart-to-text, ChartSumm, arXiv, Data Aug., SpecializedTypes. Key traps: (1) arXiv is a data platform not a chart benchmark, but appears as an explicit column source — must be included; (2) Data Aug. i... | chartassistant | cs | zh | chartassistant_001 | [
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"images/SciDocBench/74/7408a... | ||
394 | 你获得了一篇科学 PDF 文档，该文档介绍了非循环多路径神经网络的超参数迁移定律。论文在其编号方程中定义并使用了若干数学符号，涵盖正文（第 1-10 页）和附录（第 11 页及以后）。
你的任务：对于下列二十个符号中的每一个，确定它在论文中**首次出现**的方程编号——无论是作为定义的主要对象（例如，"symbol := ..."），还是作为方程主体的一部分。只统计带编号的（展示）方程，即右侧页边有括号中编号的方程。
符号：
- W_{ij}
- z^{(\ell)}(x)
- \Delta z^{(\ell)}_i(x)
- S_\ell(\eta)
- \bar{S}(\eta)
- \mathcal{M}(S_1, \do... | {"W_{ij}": "Eq. (1)", "z^{(\\ell)}(x)": "Eq. (2)", "\\Delta z^{(\\ell)}_i(x)": "Eq. (7)", "S_\\ell(\\eta)": "Eq. (8)", "\\bar{S}(\\eta)": "Eq. (9)", "\\mathcal{M}(S_1, \\dots, S_L)": "Eq. (11)", "T_h(\\mu_1, \\mu_2)": "Eq. (13)", "T_{L+1}(\\mu_1, \\mu_2)": "Eq. (16)", "S_{L+1}(\\mu_1, \\mu_2)": "Eq. (17)", "\\sigma_y^2... | B1 | json_match | [
"50/50979f59f6a72f7bd97cd524904a359f9c56617bae7410790d69ed06cd7dc002.jpg",
"b5/b55dd0a8b031104968d81463bef7007ce73be94c91dda133753b018b640a498f.jpg",
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"8c/8cdd... | [{"text":"# Hyperparameter Transfer Laws for Non-Recurrent Multi-Path Neural Networks\n\nDeeper modern architectures are costly to train, making hyperparameter transfer preferable to expensive repeated tuning. Maximal Update Parametrization ( $\\mu P$ ) helps explain why many hyperparameters transfer across width. Yet ... | hptransfer_001__zh_interleave | hptransfer_001 | zh_interleave | interleave | json | {"reasoning": "W_{ij}: Eq.(1). TRAP: prose introduces W_{ij} before Eq.(1). z^(ell)(x): Eq.(2) MLP. TRAP: z^(0)=x in prose; CNN Eq.(3); ResNet Eq.(4); appendix Eq.(28),(40). Delta z: Eq.(7). S_ell(eta): Eq.(8). TRAP: appendix Eq.(10) redefines as S_ell without eta. bar_S(eta): Eq.(9). TRAP: appendix Eq.(10) redefines w... | hptransfer | cs | zh | hptransfer_001 | [
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"images/SciDocBench/77/779f7... | |
395 | 你将获得一篇科学 PDF 文档，内容是关于使用机器学习基于 anti-dsDNA IgG 糖基化谱来分类系统性红斑狼疮 (SLE) 中的器官受累情况。
我是一名医生，如果我想用我自己患者的数据复现这篇论文的机器学习结果，我必须为每位患者收集哪些数据？（只选择复现机器学习算法所必需的数据）
A. 血清中所有岩藻糖基化 anti-dsDNA IgG1 的浓度。
B. 血清中总体 anti-dsDNA IgG1 的浓度。
C. 血清中所有半乳糖基化 anti-dsDNA IgG2 的浓度。
D. 血清中所有唾液酸化 anti-dsDNA IgG（不指定亚类）的浓度。
E. 患者的年龄。
F. 患者的血液系统是否受 SLE 影响。
G.... | {"Choice": "A,C,E,F,G,J", "Reason": "The ML inputs are concentrations of 12 subclass-specific glycoforms (4 glycan types x 3 subclasses: IgG1/IgG2/IgG3-4). A (fucosylated IgG1) and C (galactosylated IgG2) are among the 12 glycoform predictors. B (overall IgG1 concentration) is not a glycoform predictor. D (sialylated I... | B2 | judge | [
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"62/62544462f1759f4c63a8286f820bd8e045ef4caa290df7d7148ba5ef2656b5d8.jpg",
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"a9/a9edc01ac0140e9ed56a7e86f51941057443a7c6d0389732acdac33a43d69ef7.jpg",
"7a/7a77... | [{"text":"# Glycosylation of anti-dsDNA IgG correlates with organ involvement in treatment-naïve patients with systemic lupus erythematosus\n\nTo cite: Zhou Z, Liu Y, Gu X, et al. Glycosylation of anti-dsDNA IgG correlates with organ involvement in treatment-naïve patients with systemic lupus erythematosus. Lupus Scien... | sle-glyco_001__zh_interleave | sle-glyco_001 | zh_interleave | interleave | json | You are an expert evaluator for scientific document understanding tasks.
The task: given a multiple-choice question about reproducing ML results from a medical paper, determine the score based on the following group-based rubric.
Reference answer (correct choices): {answer}
Model prediction:
{prediction}
## Scoring... | {"reasoning": "ML inputs: 12 subclass-specific glycoforms (4 glycan types x 3 IgG subclasses). A (IgG1Fuc) and C (IgG2Gal) are two of the 12 inputs; B (total IgG1) is not a predictor; D (sialylated IgG without subclass) does not match subclass-specific measurements. E: age exclusion criterion (under-18 excluded from st... | sle-glyco | biology | zh | sle-glyco_001 | [
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"images/SciDocBench/a9/a9edc... |
396 | 你将获得一篇关于临床医学中大型语言模型（LLMs）系统综述的科学 PDF 文档。
图 4 显示了 LLMs 在不同条件下与人类专家相比的表现。一些子图包含标记（*, **, ***），表示特定组之间存在统计学显著差异。
找出图 4 中所有被标记为具有统计学显著性的项目配对。对于每一个这样的配对，请按以下格式在单独一行输出一个 JSON object：
{"Subplot": "<e.g. 4a>", "Items": "<item1> vs. <item2>", "Values": "<value1> vs. <value2>", "Significance": "<*|**|***>"}
列出你找到的每一个显著配对。如果某个... | "{\"Subplot\": \"4a\", \"Items\": \"(Tier)I vs. (Tier)III\", \"Values\": \"25.9% vs. 38.4%\", \"Significance\": \"*\"}\n{\"Subplot\": \"4c\", \"Items\": \"Nonphysician clinician vs. Md\", \"Values\": \"31% vs. 54%\", \"Significance\": \"**\"}\n{\"Subplot\": \"4c\", \"Items\": \"Attending vs. Medical student\", \"Values... | B2 | judge | [
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"43/431c... | [{"text":"Analysis\n\n# LLM-assisted systematic review of large language models in clinical medicine\n\nSully F. Chen $^{1}$ , Anton Alyakin $^{2,3,4}$ , Andreas Seas $^{1}$ , Eunice Yang $^{3,5}$ , Joanne J. Choi $^{3,6}$ , Jin Vivian Lee $^{2,3,4}$ , Amelia L. Chen $^{7}$ , Pranav I. Warman $^{8}$ , Rochelle T. Bitol... | llm-clinical-review_001__zh_interleave | llm-clinical-review_001 | zh_interleave | interleave | text | You are an expert evaluator for scientific figure interpretation tasks.
The task: identify all statistically significant pairwise comparisons marked with asterisks in Figure 4 of a clinical LLM review paper. There are exactly 4 significant pairs in the reference answer.
Reference answer:
{answer}
Model prediction:
{... | {"reasoning": "Figure 4 has 4 subplots (a-d). Significant markers:\n4a: * bracket over Tier I (25.9%) vs Tier III (38.4%). Tier II (33.2%) not in any significant pair.\n4b: year trend, no asterisks.\n4c: three brackets: ** Nonphysician clinician (31%) vs Md (54%); *** Attending (30%) vs Medical student (44%); * Attendi... | llm-clinical-review | medicine | zh | llm-clinical-review_001 | [
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397 | 给定一篇关于 in vivo site-specific T cell engineering 的科学 PDF 文档。
识别 Extended Data Fig. 3a 和 3c 中的所有直方图。对于每一个单独的直方图，根据其 y 轴高度将该直方图内的所有类别按降序排序。你需要输出其所属的子图、直方图名称、x 轴标签、y 轴标签，以及从最高到最低的所有类别排序。如果子图中标注了统计显著性，还需报告被比较的相应类别和显著性水平（例如，0.01、0.05）。请将每个直方图作为一个 JSON 对象单独输出一行，格式如下：
{"Subplot": "<e.g. 3a>", "Histogram": "<e.g. Sample A>", "... | "{\"Subplot\": \"3a\", \"Histogram\": \"CD4+ T cells\", \"X-axis\": \"Treatment\", \"Y-axis\": \"GFP+ (%)\", \"Ranking\": \"VSVG/AAV-hT7 > VSVG/AAV6 > αCD3/AAV-hT7 > αCD3/AAV6 > PBS\", \"Significance\": \"None\"}\n{\"Subplot\": \"3a\", \"Histogram\": \"CD8+ T cells\", \"X-axis\": \"Treatment\", \"Y-axis\": \"GFP+ (%)\"... | B2 | judge | [
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"7f/7f00... | [{"text":"# In vivo site-specific engineering to reprogram T cells\n\nTo assess Cas9–single guide RNA (sgRNA) delivery, we treated activated human primary T cells in vitro with the VSVG-EDV across a range of multiplicities of infection (MOI) and quantified TCR knockout (KO) using flow cytometry. EDV alone achieved 50% ... | invivo-cart_001__zh_interleave | invivo-cart_001 | zh_interleave | interleave | text | You are an expert evaluator for scientific figure interpretation tasks.
The task: identify histograms in Extended Data Fig. 3a and 3c of a T cell engineering paper, rank bars by height, and report statistical significance annotations.
Reference answer (7 histograms):
{answer}
Model prediction:
{prediction}
## Scori... | {"reasoning": "Fig 3a has 5 histograms (CD4+, CD8+, CD34+ HSC, NK, Macrophages), each with 5 bars (PBS, VSVG/AAV6, VSVG/AAV-hT7, αCD3/AAV6, αCD3/AAV-hT7), y-axis = GFP+ (%); no significance brackets.\nFor T cells (CD4+, CD8+): VSVG/AAV-hT7 is highest, showing T cell targeting advantage.\nFor CD34+ HSC: VSVG/AAV6 is hig... | invivo-cart | biology | zh | invivo-cart_001 | [
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"images/SciDocBench/ba/ba6e0... |
398 | 你获得了一篇关于声学超材料中的非阿贝尔拓扑半金属和 Euler class 拓扑的科学 PDF 文档。
绘制完整的推导/逻辑链，将以下前提连接到两个终端结论：
**起始前提：**
- 系统的 C₂T 或 PT 对称性
- Kagome 晶格几何（Wyckoff 3c）
- 声学超材料实现（耦合谐振器）
**终端结论：**
- 节点稳定性判据：ξ = 0 → 可湮灭；|ξ| = 1 → 稳定双节点
- 实验验证：节点演化、三重点、电荷泵浦、边缘态转换
要求：
- 每个节点仅陈述一个结论（≤12 个词），使用与论文措辞密切匹配的短语
- 包含所有概念上必要的中间步骤
- 使用标准 Mermaid flowchart TD 语法 | "{\"key_facts\": [\"C2T/PT symmetry → real symmetric 3-band Hamiltonian\", \"real Hamiltonian → real eigenstates forming oriented orthonormal dreibein frame (SO(3))\", \"real-gauge sign ambiguity (ψ_n ~ -ψ_n) → flag variety SO(3)/D2\", \"flag structure SO(3)/D2 → Dirac strings (real-gauge sign discontinuities)\", \"dre... | B3 | judge | [
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"e5/e559... | [{"text":"# Observation of non-Abelian topological semimetals and their phase transitions\n\nTopological phases of matter lie at the heart of physics, connecting elegant mathematical principles to real materials that are believed to shape future electronic and quantum computing technologies. To date, studies in this di... | nonabl-semimetal_001__zh_interleave | nonabl-semimetal_001 | zh_interleave | interleave | text | You are an expert evaluator for scientific derivation graph tasks.
The task: the model was asked to draw a Mermaid derivation chain for a non-Abelian topological semimetal paper, covering the full logic from C2T/PT symmetry and Kagome lattice to node stability criterion and experimental verification.
Reference key fa... | {"reasoning": "GT edges traced from the provided Mermaid chart. F1-F8: core theory chain (symmetry→Hamiltonian→dreibein→Euler class/frame charge). F9-F11: three DS rules (each independently scorable). F12-F14: non-Abelian braiding chain with specific example and Euler class change. F15-F17: Kagome model realization con... | nonabl-semimetal | physics | zh | nonabl-semimetal_001 | [
"images/SciDocBench/2e/2ea8a4a44b57bc1d1ff706f9ea68cd50174cdd72aa01d7c5656db81f8b49ba1c.jpg",
"images/SciDocBench/6b/6b425a4e33b02de00682f10de020755f6cf4a3f0264c95437781271918175d07.jpg",
"images/SciDocBench/bf/bf215a6df787fb14bae0ba897ca61663743cdb2f1f1798bb4509f6fc17b2ce21.jpg",
"images/SciDocBench/c4/c4517... |
399 | 你将获得一篇关于 PoET (Protein Evolutionary Transformer) 的科学 PDF 文档。
输出一个 2 层 Mermaid 树（flowchart TD）：
**叶节点** — PoET 所基于的每个主要先前工作流派各一个节点：
- 第 1 行：Citation aliases，用 `|` 分隔
- 第 2 行：`CommonBase: ` + 共享的理论基础（仅名词/术语）
**边** — 描述具体扩展或贡献的简洁名词/术语或公式
**根节点**（恰好一个）：
- 第 1 行：论文的单一关键公式或主张（翻译成平实英语）
- 第 2 行：一个具体的数值或概念实例化（无标签，仅该值）
ROOT... | "{\"key_facts\": [\"F1: Root node contains PoET's autoregressive factorization formula: joint double product over sequences i=1..n and positions j=1..L_i of P(s_{i,j} | s_{<i}, s_{i,<j}) — both levels of conditioning (inter-sequence and intra-sequence) must be present\", \"F2: Root node includes '201M parameters' or eq... | B3 | judge | [
"be/be3b3096886e84bd9aaa55ea63c3d22d297ec78dfad3b09265aeb38500d6e365.jpg",
"44/441a24726e6f54c8c33e051b35c76bdf38dab8628a7693eff7257cba5dc2a2de.jpg",
"9c/9cf14d6fdcf54e3464a63317ae0ce3db990a8e5c4e10f4952d29de001d14afa7.jpg",
"37/37cf8b913660b8fc92cb432388a0caa754ca8c2f4e0a8923376d61856899f0cb.jpg",
"21/215e... | [{"text":"# PoET: A generative model of protein families as sequences-of-sequences\n\nGenerative protein language models are a natural way to design new proteins with desired functions. However, current models are either difficult to direct to produce a protein from a specific family of interest, or must be trained on ... | poet_001__zh_interleave | poet_001 | zh_interleave | interleave | text | You are an expert evaluator for scientific literature synthesis tasks.
The task: the model was asked to draw a 2-level Mermaid tree showing how prior work streams lead to PoET (Protein Evolutionary Transformer), with a central root node containing PoET's keystone formula.
Reference key facts (10 facts, each worth 0.1... | {"reasoning": "GT has 7 leaf groups → 1 root (PoET). Root formula: Eq.(1) P(x)=∏_i∏_j P(s_{i,j}|s_{<i},s_{i,<j}), 201M params. Leaf A: neural autoregressive LMs (GPT/ProtGPT2/ProGen/meier2021/nijkamp2022/hesslow2022) → hierarchical factorization. Leaf B: retrieval-augmented LMs (REALM/RETRO/FiD/guu2018-2020/borgeaud202... | poet | biology | zh | poet_001 | [
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"images/SciDocBench/9c/9cf14d6fdcf54e3464a63317ae0ce3db990a8e5c4e10f4952d29de001d14afa7.jpg",
"images/SciDocBench/37/37cf8... |
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