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200 | Identify the content of subpanel Fig. 5i, titled "Summary circuitry diagram for pain sensitization and touch or tactile sensation.", from the attached file. The node at the bottom of subpanel Fig. 5i is 'SC'. And the name of the node at the bottom-right corner is 'RVM-OPRM1+'. Reorganize this subpanel into a structured... | "[\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 | [
"00/00f683ced1f6363acb7bb188ff44db976e6fa1dce9c4cd7ccd0f1c73ccf7898f.jpg",
"7b/7b166fbe4a993434552e585579eff4484ef48c9a0769d9083829bb4d7b9e3b0e.jpg",
"5a/5a2385bf7b2963b0df9ac2a43c653f89fdd95e503df4e4c5842816cab4082dda.jpg",
"0a/0aa3761f3b857380bb7bf2f07149dcc6da77ff41647d1129898ccb4aa4fe40f7.jpg",
"16/16ff... | [{"image":"00/00f683ced1f6363acb7bb188ff44db976e6fa1dce9c4cd7ccd0f1c73ccf7898f.jpg"},{"image":"7b/7b166fbe4a993434552e585579eff4484ef48c9a0769d9083829bb4d7b9e3b0e.jpg"},{"image":"5a/5a2385bf7b2963b0df9ac2a43c653f89fdd95e503df4e4c5842816cab4082dda.jpg"},{"image":"0a/0aa3761f3b857380bb7bf2f07149dcc6da77ff41647d1129898ccb... | np_c3_02_001__en_interleave | np_c3_02_001 | en_interleave | interleave | text | {} | np_c3_02 | scientific | en | np_c3_02_001 | [
"images/SciDocBench/00/00f683ced1f6363acb7bb188ff44db976e6fa1dce9c4cd7ccd0f1c73ccf7898f.jpg",
"images/SciDocBench/7b/7b166fbe4a993434552e585579eff4484ef48c9a0769d9083829bb4d7b9e3b0e.jpg",
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"images/SciDocBench/0a/0aa37... | |
201 | You are a physics paper reading assistant. Please read the provided paper and, for each of the following claims made in the main text or Methods, identify all specific Figures or Extended Data Figures that directly support the claim, and report the page number on which each figure appears.
Your response must be format... | {"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 | [
"c8/c85edf1f29c27ffde9ab6713a2e67c89aafc187ca45aa36ec233bc1a65fcf83c.jpg",
"1e/1ede144e03cf161d5440ce83732b14066d7dac08e4ffad8aadd831a14416985a.jpg",
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"d1/d137d1a76d512e0d9e27cc8e48d10f8133bd4a921bf8380f9980b34f34dec543.jpg",
"1e/1e21... | [{"text":"# Cascade of electronic transitions in magic-angle twisted bilayer graphene\n\nhttps://doi.org/10.1038/s41586-020-2339-0\n\nMagic-angle twisted bilayer graphene exhibits a variety of electronic states, including correlated insulators $^{1-3}$ , superconductors $^{2-4}$ and topological phases $^{3,5,6}$ . Unde... | np_a2_01_001__en_interleave | np_a2_01_001 | en_interleave | interleave | json | {} | np_a2_01 | scientific | en | np_a2_01_001 | [
"images/SciDocBench/c8/c85edf1f29c27ffde9ab6713a2e67c89aafc187ca45aa36ec233bc1a65fcf83c.jpg",
"images/SciDocBench/1e/1ede144e03cf161d5440ce83732b14066d7dac08e4ffad8aadd831a14416985a.jpg",
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"images/SciDocBench/d1/d137d... | |
202 | Analyze the provided quantum computing survey paper and identify all cited research works explicitly classified within the paper's taxonomic systems. Assign classification labels from the following dimensions:
Dimension 1: QKD_Type
- Discrete_Continuous
- COW
- DPS
- Six_State
- Decoy_State
Dimension 2: PQC_Type
- L... | {"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 | [
"8c/8c54610ec6d6ce36c2c039fb1de6bf0e13f860c312438d8d4221ad244c05f4ad.jpg",
"a6/a68e15d078dc86d61f44b29b5fd8323e0af689877d56a20f241df183e2e92d0f.jpg",
"01/017467834e593ee8e5e6bf062bb69922dbfdc0976834b9b3a39b33a3a1ccb030.jpg"
] | [{"text":"computation speed will grow faster by using these quantum algorithms. Li et al. [35] discussed merging the elements of quantum mechanics with the intelligent nature-inspired algorithms to mark the new era of computing in the making. Quantum optimization and quantum learning are two classifications based on wh... | np_a3_01_001__en_interleave | np_a3_01_001 | en_interleave | interleave | json | {} | np_a3_01 | scientific | en | np_a3_01_001 | [
"images/SciDocBench/8c/8c54610ec6d6ce36c2c039fb1de6bf0e13f860c312438d8d4221ad244c05f4ad.jpg",
"images/SciDocBench/a6/a68e15d078dc86d61f44b29b5fd8323e0af689877d56a20f241df183e2e92d0f.jpg",
"images/SciDocBench/01/017467834e593ee8e5e6bf062bb69922dbfdc0976834b9b3a39b33a3a1ccb030.jpg"
] | |
203 | You are a chemistry assistant. Please write out the complete chemical reaction equations in the organic compound preparation scheme/procedure of scheme 2 in the research paper with the following requirements:
1. Chemical reaction equations must include reaction products and reaction conditions
2. Molecular formulas mu... | "- $\\mathrm{C_3H_7NO_2\\,(7)} + \\mathrm{C_{10}H_{18}O_5\\,(Boc_2O)} \\xrightarrow[\\text{THF/H}_2\\text{O (1:2), 4°C to rt, 16\\,h}]{\\mathrm{Na_2CO_3}} \\mathrm{C_8H_{15}NO_4\\,(8)} + \\mathrm{C_4H_{10}O\\,(t\\text{-}BuOH)} + \\mathrm{CO_2} \\quad 75\\%$\n\n- $\\mathrm{C_8H_{15}NO_4\\,(8)} + \\mathrm{C_4H_{10}ClNO_3... | A5 | judge | [
"99/99de5e2b70c5e9cbe1bc6604e0f0e34f22bb813716d54d3f25b3486e58b174a8.jpg",
"0a/0aadf8370c2b8e2918195a273f59a37da9cc25b34acf658c28647774710546d1.jpg",
"8d/8df21e607dedd943e7c29a8298fc6b5cde6843c3ef91d2eee3cb6ba39fdac2d9.jpg",
"c5/c54ab4755aa69b2ec55063a747f35cb64f8d8faa2b61b8376d40b92104c1327e.jpg",
"98/98f3... | [{"text":"# Total Synthesis of Orbiculamide A"},{"image":"99/99de5e2b70c5e9cbe1bc6604e0f0e34f22bb813716d54d3f25b3486e58b174a8.jpg"},{"image":"0a/0aadf8370c2b8e2918195a273f59a37da9cc25b34acf658c28647774710546d1.jpg"},{"image":"8d/8df21e607dedd943e7c29a8298fc6b5cde6843c3ef91d2eee3cb6ba39fdac2d9.jpg"},{"text":"ABSTRACT: O... | np_a5_01_001__en_interleave | np_a5_01_001 | en_interleave | interleave | text | {} | np_a5_01 | scientific | en | np_a5_01_001 | [
"images/SciDocBench/99/99de5e2b70c5e9cbe1bc6604e0f0e34f22bb813716d54d3f25b3486e58b174a8.jpg",
"images/SciDocBench/0a/0aadf8370c2b8e2918195a273f59a37da9cc25b34acf658c28647774710546d1.jpg",
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"images/SciDocBench/c5/c54ab... | |
204 | According to the document, answer the following question.
After 20 epochs, in the representation space of layer 5, which of the following four categories: airplane, automobile, ship, and truck, still has relatively ambiguous boundaries?
Answer in the following format:
{"Category": "<e.g. Airplane, automobile>"} | {"Category": "airplane"} | A5 | json_match | [
"38/38892c57ecf59bcc8a286189ce4764a4fadc4e734e8d68eb977108a5fc85df80.jpg",
"31/3193c32b8edd3461db567ecd25997829b994b3a47597000fb5b8876038afc0d2.jpg"
] | [{"image":"38/38892c57ecf59bcc8a286189ce4764a4fadc4e734e8d68eb977108a5fc85df80.jpg"},{"text":"Figure 1: (a) Overview of the bidirectional spike-based distillation framework. (b) Illustration of feature alignment using the properties of pyramidal neurons, which receive feedforward and feedback signals through their basa... | np_a5_05_001__en_interleave | np_a5_05_001 | en_interleave | interleave | json | {} | np_a5_05 | scientific | en | np_a5_05_001 | [
"images/SciDocBench/38/38892c57ecf59bcc8a286189ce4764a4fadc4e734e8d68eb977108a5fc85df80.jpg",
"images/SciDocBench/31/3193c32b8edd3461db567ecd25997829b994b3a47597000fb5b8876038afc0d2.jpg"
] | |
205 | Regarding this article, the following statements are correct:
A. From Fig. 3, it can be observed that an adjustable pulley is designed for the belt to regulate its tension.
B. The physical model in Fig. 4 assumes that the cable force F(cable) and the rod lie in the same plane, and the force is parallel to the ground.
C... | {"Choice": "B,E,F"} | A5 | json_match | [
"63/63e10ebd3a6cdc139926c813f8eca00f61bc5f08b7c7feca385d3499a09ce0a8.jpg",
"db/db9c83ebb62add18cf7a1c625f78fdf0c1da477d51877bdf12efbb2fd88774a6.jpg",
"15/15660cdcadeeb76e9a30a3316196493a87c22e00e397f75a9c455dc4fe7e5796.jpg",
"fb/fbf0c3767d90468cc15195c96162995c1adb3461dee916d00d6892c3234496fe.jpg",
"ab/abbc... | [{"text":"# Design of a Variable Stiffness Quasi-Direct Drive Cable-Actuated Tensegrity Robot\n\nAbstract—Tensegrity robots excel in tasks requiring extreme levels of deformability and robustness. However, there are challenges in state estimation and payload versatility due to their high number of degrees of freedom an... | np_a5_07_001__en_interleave | np_a5_07_001 | en_interleave | interleave | json | {} | np_a5_07 | scientific | en | np_a5_07_001 | [
"images/SciDocBench/63/63e10ebd3a6cdc139926c813f8eca00f61bc5f08b7c7feca385d3499a09ce0a8.jpg",
"images/SciDocBench/db/db9c83ebb62add18cf7a1c625f78fdf0c1da477d51877bdf12efbb2fd88774a6.jpg",
"images/SciDocBench/15/15660cdcadeeb76e9a30a3316196493a87c22e00e397f75a9c455dc4fe7e5796.jpg",
"images/SciDocBench/fb/fbf0c... | |
206 | You are a Professional Scientific Editor and LaTeX Typesetter.
Your goal is to generate an "Index of Notations" for the provided research paper. Cover the principal mathematical symbols introduced in the main text only — do NOT include notations from Appendix A or any other appendix. The table is illustrative rather th... | "\\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 | [
"2b/2bf6c1f983f3b4414f83ab2ec5b389200d3c787b901aa48edf69330eb3db330c.jpg",
"a2/a24e5afb950396b83dc7a9745d16fd1d03d85e64d398daad10f20adaa07cd8a1.jpg",
"bc/bc22ea3692538ef604b1ab9b8b6a4d381e44e66bf48e36948dbf8698533bf669.jpg",
"0f/0fccafda34b9315a1c3d4bcdf5e1b514d48460620074e75831a719669d82e184.jpg",
"bf/bfa0... | [{"text":"# PACKED-ENSEMBLES FOR EFFICIENT UNCERTAINTY ESTIMATION\n\nOlivier Laurent, $^{1,2,*}$ Adrien Lafage, $^{2,*}$ Enzo Tartaglione, $^{3}$ Geoffrey Daniel, $^{1}$ Jean-Marc Martinez, $^{1}$ Andrei Bursuc $^{4}$ & Gianni Franchi $^{2,\\dagger}$\n\nUniversité Paris-Saclay, CEA, SGLS, $^{1}$ U2IS, ENSTA, Institut P... | np_b1_01_001__en_interleave | np_b1_01_001 | en_interleave | interleave | text | {} | np_b1_01 | scientific | en | np_b1_01_001 | [
"images/SciDocBench/2b/2bf6c1f983f3b4414f83ab2ec5b389200d3c787b901aa48edf69330eb3db330c.jpg",
"images/SciDocBench/a2/a24e5afb950396b83dc7a9745d16fd1d03d85e64d398daad10f20adaa07cd8a1.jpg",
"images/SciDocBench/bc/bc22ea3692538ef604b1ab9b8b6a4d381e44e66bf48e36948dbf8698533bf669.jpg",
"images/SciDocBench/0f/0fcca... | |
207 | According to Figure 2(b), answer the following question: How many bond centers are shown in Figure 2(b)? Answer in the following format:
{"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 | [
"62/6285f9f09bd379bcc6540b0891788bddbb0f2483530a5628a8ca06e0b8d3a6c3.jpg",
"e1/e1339af33e7d53db1e984ac60971b30aff6070a398089ceaeb3a3a3caf3e2981.jpg",
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"2f/2f85f7ea0d34ff52920d6d18bffc4024726f51b6032c8206afc8eb182de7106f.jpg",
"f7/f734... | [{"text":"pubs.acs.org/JACS\n\nCommunication\n\n# A Delocalized Mixed-Valence Dinuclear Ytterbium Complex That Displays Intervalence Charge Transfer\n\nTom J. N. Obey, Mukesh K. Singh, Angelos B. Canaj, Gary S. Nichol, Euan K. Brechin, and Jason B. Love\\*"},{"image":"62/6285f9f09bd379bcc6540b0891788bddbb0f2483530a5628... | np_b2_02_001__en_interleave | np_b2_02_001 | en_interleave | interleave | json | {} | np_b2_02 | scientific | en | np_b2_02_001 | [
"images/SciDocBench/62/6285f9f09bd379bcc6540b0891788bddbb0f2483530a5628a8ca06e0b8d3a6c3.jpg",
"images/SciDocBench/e1/e1339af33e7d53db1e984ac60971b30aff6070a398089ceaeb3a3a3caf3e2981.jpg",
"images/SciDocBench/44/44c6a07bbeb0f65a97cecdde7adf34dcffe23d060c69be84b165f34711e0b28b.jpg",
"images/SciDocBench/2f/2f85f... | |
208 | You are a physics data verification assistant. Please read the provided paper and perform a systematic numerical and logical consistency check across the main text, figure captions, and any embedded formulas.
Your task is to identify all instances where numerical values, physical parameters, experimental conditions, o... | [[{"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 | [
"a7/a70c06144da6de5ee879f95b2118c0424a09d117d75319575891a56351b7ec27.jpg",
"9e/9ea2e4883ec4e0a80415ef0110f5f8bb1f47e4e56282d4434801b757d61b1e79.jpg",
"c7/c7943bd7a3da02515193eeaaf386615ea6ff39fbe5046316ed93507c5b57ed06.jpg",
"1b/1b030b40474bb2790f3093ea73a4e565a092f64cedb289ba9c8e4c81049f0b63.jpg",
"bb/bb6d... | [{"text":"# Magic-angle graphene superlattices: a new platform for unconventional superconductivity\n\nTakashi Taniguchi, $^{3}$ Efthimos Kaxiras, $^{2,4}$ and Pablo Jarillo-Herrero $^{1,\\dagger}$"},{"image":"a7/a70c06144da6de5ee879f95b2118c0424a09d117d75319575891a56351b7ec27.jpg"},{"image":"9e/9ea2e4883ec4e0a80415ef0... | np_c1_02_001__en_interleave | np_c1_02_001 | en_interleave | interleave | json | {} | np_c1_02 | scientific | en | np_c1_02_001 | [
"images/SciDocBench/a7/a70c06144da6de5ee879f95b2118c0424a09d117d75319575891a56351b7ec27.jpg",
"images/SciDocBench/9e/9ea2e4883ec4e0a80415ef0110f5f8bb1f47e4e56282d4434801b757d61b1e79.jpg",
"images/SciDocBench/c7/c7943bd7a3da02515193eeaaf386615ea6ff39fbe5046316ed93507c5b57ed06.jpg",
"images/SciDocBench/1b/1b030... | |
209 | Read the paper end-to-end. Use only this paper. Do not explain. Output exactly one 18-character bitstring. Use 1 iff the claim is fully supported by the paper; otherwise use 0.
A. The paper explicitly asks whether Transformers can learn simple logic functions in TC^0 using gradient descent.
B. The introduction says pr... | "101111101011111010" | C1 | judge | [] | [{"text":"# Provable Failure of Language Models in Learning Majority Boolean Logic via Gradient Descent\n\nBo Chen $^{*}$ Zhenmei Shi $^{\\dagger}$ Zhao Song $^{\\ddagger}$ Jiahao Zhang $^{\\S}$\n\n## Abstract\n\nRecent advancements in Transformer-based architectures have led to impressive breakthroughs in natural lang... | np_c1_05_001__en_interleave | np_c1_05_001 | en_interleave | interleave | text | {} | np_c1_05 | scientific | en | np_c1_05_001 | [] | |
210 | According to Fig.3, answer the following problem.
For \chi_M'-frequency curve, describe the variation trend of slope's absolute value and inflection point's frequency. | 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 | [
"ae/ae0d0e601dcb7a1e15f4f6cef12fa8b90d69dafe527b899291ec4d22d1b5fb28.jpg",
"cb/cb66ad405b99ff7b547341f5eac983731fdcc6252440a1052e66ee2af992182f.jpg",
"8c/8c559f756f2332985f42fda7a242d6c1121e02aee56bfb8e224de9122adb5108.jpg",
"2e/2e1d8543cb758eeddf7b1bee280e05d9a25d177a468b80de0c7686336a4a1146.jpg",
"35/3557... | [{"text":"ARTICLE\n\nIncreasing the operating temperatures of single-molecule magnets—molecules that can retain magnetic polarization in the absence of an applied field—has potential implications toward information storage and computing, and may also inform the development of new bulk magnets. Progress toward these goa... | np_c3_04_001__en_interleave | np_c3_04_001 | en_interleave | interleave | text | {} | np_c3_04 | scientific | en | np_c3_04_001 | [
"images/SciDocBench/ae/ae0d0e601dcb7a1e15f4f6cef12fa8b90d69dafe527b899291ec4d22d1b5fb28.jpg",
"images/SciDocBench/cb/cb66ad405b99ff7b547341f5eac983731fdcc6252440a1052e66ee2af992182f.jpg",
"images/SciDocBench/8c/8c559f756f2332985f42fda7a242d6c1121e02aee56bfb8e224de9122adb5108.jpg",
"images/SciDocBench/2e/2e1d8... | |
211 | Based on Figure 1(b) in the paper, list the three categories of social interactions shown as separate subfigures.
Answer in the following JSON format (an array of three strings, in the order they appear in the figure):
{"Categories": ["<category 1>", "<category 2>", "<category 3>"]} | {"Categories": ["Advice requests", "Work-related orders", "Social invitations"]} | A2 | json_match | [
"4f/4fd1e8fb506a5bf28eac26cc7c03d88d35ac44b20a9ca5f262e473ff7dc05f15.jpg",
"3b/3bf407fe1cef2e205963fd12164dc0300226f398278aa1c8c00fb8e6307098f6.jpg",
"f8/f8482bebbe3e287eed4d35630024561794779b2f9661706b97a7e34504626a1b.jpg",
"a8/a8f13cfafd6ece8bc699dae60a624452add973f1b16a82bec693aca27c545431.jpg",
"57/574c... | [{"text":"# Inferring the internal structure of groups through the integration of statistical learning and causal reasoning\n\nIsaac Davis $^{1}$ , Julian Jara-Ettinger $^{1,2}$ & Yarrow Dunham $^{1,2}$\n\nHuman social life unfolds within richly structured networks of overlapping relationships, including friendships, h... | np_a2_02_001__en_interleave | np_a2_02_001 | en_interleave | interleave | json | {} | np_a2_02 | scientific | en | np_a2_02_001 | [
"images/SciDocBench/4f/4fd1e8fb506a5bf28eac26cc7c03d88d35ac44b20a9ca5f262e473ff7dc05f15.jpg",
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"images/SciDocBench/a8/a8f13... | |
212 | You are an AI for biology article analysis. Based on the provided pages, list ONLY the top-level databases that are explicitly named as being used in these works. Use canonical short names (e.g. "UniProt", "ClinVar", "ProteinGym", "gnomAD"). Do not list sub-datasets, do not invent databases, and do not include resource... | {"answer_list": ["ClinVar", "ProteinGym", "UniProt", "gnomAD"]} | A4 | json_match | [
"54/540d7a2d34d0ca9aba3c756abf62b38c0ae529264b83abbb9d18c34160e6f6ae.jpg",
"a2/a2707320c5c1c2766e22a32ec699c851339a5afea70e4be16229fb218bff1067.jpg",
"b2/b22ae79cdb1b9977368330a703fc19b675950c9d4b44401939dbd26cd8decc98.jpg",
"f0/f035e01cec2586cae4383bcd45449b4357251bdad72fdeb5465d7c863568820f.jpg",
"b5/b559... | [{"text":"Article\n\nCheck for updates\n\nTuan Dinh $^{1}$ , Seon-Kyeong Jang $^{2}$ , Noah Zaitlen $^{2,3,4}$ & Vasilis Ntranos $^{1,5,6,7}$\n\nPredicting the functional consequences of genetic variants, known as variant effect prediction (VEP), is a fundamental computational challenge with important applications in h... | np_a4_02_001__en_interleave | np_a4_02_001 | en_interleave | interleave | json | {} | np_a4_02 | scientific | en | np_a4_02_001 | [
"images/SciDocBench/54/540d7a2d34d0ca9aba3c756abf62b38c0ae529264b83abbb9d18c34160e6f6ae.jpg",
"images/SciDocBench/a2/a2707320c5c1c2766e22a32ec699c851339a5afea70e4be16229fb218bff1067.jpg",
"images/SciDocBench/b2/b22ae79cdb1b9977368330a703fc19b675950c9d4b44401939dbd26cd8decc98.jpg",
"images/SciDocBench/f0/f035e... | |
213 | Look at the panel in Figure 2 that plots quantitative measurements against the variable "Days on CLZ" (e.g., box plots / bar plots grouped by day count). For each value of "Days on CLZ" present on the x-axis, count the asterisks (stars) shown above that group's significance annotation, if any.
For each x-axis group:
-... | "{\"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 | [
"3f/3fa915b4d7b82e1a2bf4bb1f02d3f28bbf96200723574e4d1a06cc2ae7e6cb54.jpg",
"a5/a5dc57e9b8b683b1c5bc690e1aabd16d79b52f66421c27f4c6836aedb449f7ff.jpg",
"7b/7b515e866ac14fbfde66f4a71ab4bb6c050fe95842c685cc9edfa3fc92914dcf.jpg",
"76/76f82e1401d5e891463abcfcc1333a05b0400ada8d0f65ef771af0cf7f2afabb.jpg",
"c0/c020... | [{"text":"b"},{"image":"3f/3fa915b4d7b82e1a2bf4bb1f02d3f28bbf96200723574e4d1a06cc2ae7e6cb54.jpg"},{"image":"a5/a5dc57e9b8b683b1c5bc690e1aabd16d79b52f66421c27f4c6836aedb449f7ff.jpg"},{"text":"f"},{"image":"7b/7b515e866ac14fbfde66f4a71ab4bb6c050fe95842c685cc9edfa3fc92914dcf.jpg"},{"image":"76/76f82e1401d5e891463abcfcc133... | np_a5_03_001__en_interleave | np_a5_03_001 | en_interleave | interleave | text | {} | np_a5_03 | scientific | en | np_a5_03_001 | [
"images/SciDocBench/3f/3fa915b4d7b82e1a2bf4bb1f02d3f28bbf96200723574e4d1a06cc2ae7e6cb54.jpg",
"images/SciDocBench/a5/a5dc57e9b8b683b1c5bc690e1aabd16d79b52f66421c27f4c6836aedb449f7ff.jpg",
"images/SciDocBench/7b/7b515e866ac14fbfde66f4a71ab4bb6c050fe95842c685cc9edfa3fc92914dcf.jpg",
"images/SciDocBench/76/76f82... | |
214 | According to Figure 1, are there any energy intervals in which the counts of prompt signals are less than the counts of delay signals across the energy range shown? Answer "Yes" or "No".
Output format:
{"Has_Interval_Where_Prompt_Less_Than_Delay": "<Yes/No>"} | {"Has_Interval_Where_Prompt_Less_Than_Delay": "No"} | A5 | json_match | [
"de/ded8d193d530cdaa4c98122821271e1a22510a3bbfe94d878ce979a14ab4119e.jpg",
"59/599c81bf1d32771f0e42695e9ce7fdf9bae12bd84a1b736b66ff996e743c960f.jpg",
"34/349b5940d69da9622dad0a58d7514a929cf4faa0af0caf35bda0b09baaa3ee94.jpg",
"dc/dc8c1fcff01dc21d0cbd7de395de60908fd34ebb8b11461ebf02779e12047355.jpg",
"82/8276... | [{"text":"# New limits on the Pauli forbidden transitions in ${}^{12}$ C nuclei obtained with the complete Borexino dataset\n\nD. Basilico $^{1}$ , G. Bellini $^{1}$ , J. Benziger $^{2}$ , R. Biondi $^{3,10}$ , B. Caccianiga $^{1}$ , A. Caminata $^{5}$ , A. Chepurnov $^{6}$ , D. D'Angelo $^{1}$ , A. Derbin $^{7,8}$ , A... | np_a5_04_001__en_interleave | np_a5_04_001 | en_interleave | interleave | json | {} | np_a5_04 | scientific | en | np_a5_04_001 | [
"images/SciDocBench/de/ded8d193d530cdaa4c98122821271e1a22510a3bbfe94d878ce979a14ab4119e.jpg",
"images/SciDocBench/59/599c81bf1d32771f0e42695e9ce7fdf9bae12bd84a1b736b66ff996e743c960f.jpg",
"images/SciDocBench/34/349b5940d69da9622dad0a58d7514a929cf4faa0af0caf35bda0b09baaa3ee94.jpg",
"images/SciDocBench/dc/dc8c1... | |
215 | Regarding this article, the following statements are correct:
A. Figure 1 is only used to illustrate the relationship between tensegrity art and origami art.
B. The red labels in Figure 2 indicate the indices of the nodes.
C. In Figure 3, for the Miura origami unit cell, the eigenvalues of Order 2 and Order 3 differ by... | {"Choice": "C"} | A5 | json_match | [
"4d/4d3a1beb2687486ab8f9607cee489d255da3c556a476f0663dd2eee6f829cb31.jpg",
"c9/c93fbd6ba57faeb743a94771e03a76a9c6100b195ffcec6149c163e345ea4e8c.jpg",
"49/490505b01d8c5846b75a1856073907883300c24779df784c56b687da655fb535.jpg",
"08/08e1454c3acd68241f7f7074fbb9bae04f6fe6c8b359231c831ec62abc952930.jpg",
"8a/8ada... | [{"text":"# Statics of integrated origami and tensegrity systems\n\nThis study presents an explicit form of the static equilibrium equations of integrated origami and tensegrity systems. The analytical approach allows one to model and analyze the isolated origami and tensegrity paradigms as a whole system. The tensegri... | np_a5_06_001__en_interleave | np_a5_06_001 | en_interleave | interleave | json | {} | np_a5_06 | scientific | en | np_a5_06_001 | [
"images/SciDocBench/4d/4d3a1beb2687486ab8f9607cee489d255da3c556a476f0663dd2eee6f829cb31.jpg",
"images/SciDocBench/c9/c93fbd6ba57faeb743a94771e03a76a9c6100b195ffcec6149c163e345ea4e8c.jpg",
"images/SciDocBench/49/490505b01d8c5846b75a1856073907883300c24779df784c56b687da655fb535.jpg",
"images/SciDocBench/08/08e14... | |
216 | I am a beginner in tensegrity robots, and I recently came across a passage discussing a tensegrity mechanism with dual operation modes. I am particularly interested in the so-called “FCC” algorithm mentioned in the article. Could you please clarify how the “FCC”and its results are introduced in the paper?
A. It is thor... | {"Choice": "B"} | B2 | json_match | [
"18/18c1ace23a545b30148fe45f0b809e1391b912b7c6571295bc0c82309a256213.jpg",
"d9/d9e218655dc6bd90ee5376884a92d631beb6ba328395c7dc50562643a9cf9273.jpg",
"bc/bc92c4be9b153d7a6130d75053919c4f1df3adcd13b048ffece1c57c69b39670.jpg",
"d0/d083c298aa576e94eeb2fa2cb5cedd58932aafcfd2c0a0cb74b40e4d9bbc6046.jpg",
"8e/8e83... | [{"text":"Article\n\n# A Symmetric Three Degree of Freedom Tensegrity Mechanism with Dual Operation Modes for Robot Actuation\n\nTianyuan Wang $^{ID}$ and Mark A. Post $^{*ID}$"},{"image":"18/18c1ace23a545b30148fe45f0b809e1391b912b7c6571295bc0c82309a256213.jpg"},{"text":"Citation: Wang, T.; Post, M.A. A Symmetric Three... | np_b2_04_001__en_interleave | np_b2_04_001 | en_interleave | interleave | json | {} | np_b2_04 | scientific | en | np_b2_04_001 | [
"images/SciDocBench/18/18c1ace23a545b30148fe45f0b809e1391b912b7c6571295bc0c82309a256213.jpg",
"images/SciDocBench/d9/d9e218655dc6bd90ee5376884a92d631beb6ba328395c7dc50562643a9cf9273.jpg",
"images/SciDocBench/bc/bc92c4be9b153d7a6130d75053919c4f1df3adcd13b048ffece1c57c69b39670.jpg",
"images/SciDocBench/d0/d083c... | |
217 | Regarding this article, the following statements are correct:
A. Figure 3 shows that the clockwise rotational speed of the machine exceeds the counterclockwise rotational speed by more than 1.5 times.
B. From Figure 4 (D), it can be clearly observed that the machine passes beneath the horizontal bar.
C. The article doe... | {"Choice": "A,B,D,F"} | B2 | json_match | [
"8a/8aca6b5fb1105e470dd2ecc1a3e7395fb5d21e84b03c58eac0f9243389e50c5c.jpg",
"09/096eb7f235cf40c882729741dd8c370c1f09b7fe4fb92cf952b0354d9c24f9b4.jpg",
"f3/f318bcebed38e4e2dfe6d6b4cbc51a07c5439eded32aaf5a471459eec8dbaa77.jpg",
"a8/a800583ef185d7a11aaebe1d4fd49c43795899e941e8b2a3942fc9e0678ef687.jpg",
"be/be90... | [{"image":"8a/8aca6b5fb1105e470dd2ecc1a3e7395fb5d21e84b03c58eac0f9243389e50c5c.jpg"},{"image":"09/096eb7f235cf40c882729741dd8c370c1f09b7fe4fb92cf952b0354d9c24f9b4.jpg"},{"image":"f3/f318bcebed38e4e2dfe6d6b4cbc51a07c5439eded32aaf5a471459eec8dbaa77.jpg"},{"image":"a8/a800583ef185d7a11aaebe1d4fd49c43795899e941e8b2a3942fc9... | np_b2_05_001__en_interleave | np_b2_05_001 | en_interleave | interleave | json | {} | np_b2_05 | scientific | en | np_b2_05_001 | [
"images/SciDocBench/8a/8aca6b5fb1105e470dd2ecc1a3e7395fb5d21e84b03c58eac0f9243389e50c5c.jpg",
"images/SciDocBench/09/096eb7f235cf40c882729741dd8c370c1f09b7fe4fb92cf952b0354d9c24f9b4.jpg",
"images/SciDocBench/f3/f318bcebed38e4e2dfe6d6b4cbc51a07c5439eded32aaf5a471459eec8dbaa77.jpg",
"images/SciDocBench/a8/a8005... | |
218 | In the document I provided, there is a radar chart on the right side of Figure 1. Your task is to find an error inside the chart.
Chart Description: The chart is generated using Python's matplotlib package, with all experimental data subsequently labeled on the chart. It is known that all data annotations are correct; ... | {"value": "63.3", "model": "Qwen-VL-Chat", "benchmark": "GQA"} | C2 | json_match | [
"75/75fd5ca64e30c69775982cbc7a4b0262ded359528f21a72992d912a9fab7abbb.jpg",
"24/24f5945dee28f78d4721697550e3d706efab13c7579b71541b002e10c04ccf1a.jpg",
"9a/9a4e07a84f837b426db0076643af3b96f4eb70f9c7d1814275e9e920684fd0ef.jpg",
"fd/fd1e4c40ee310d7723f396d79b9a4434929f20e5b72b4abf2b5db1ff8cfd7717.jpg",
"77/7759... | [{"text":"# Improved Baselines with Visual Instruction Tuning\n\nLarge multimodal models (LMMs) have become increasingly popular in the research community, as they are the key building blocks towards general-purpose assistants $[2, 30, 43]$ . Recent studies on LMMs are converging on a central concept known as visual in... | np_c2_02_001__en_interleave | np_c2_02_001 | en_interleave | interleave | json | {} | np_c2_02 | scientific | en | np_c2_02_001 | [
"images/SciDocBench/75/75fd5ca64e30c69775982cbc7a4b0262ded359528f21a72992d912a9fab7abbb.jpg",
"images/SciDocBench/24/24f5945dee28f78d4721697550e3d706efab13c7579b71541b002e10c04ccf1a.jpg",
"images/SciDocBench/9a/9a4e07a84f837b426db0076643af3b96f4eb70f9c7d1814275e9e920684fd0ef.jpg",
"images/SciDocBench/fd/fd1e4... | |
219 | Regarding this article, the following statements are correct:
A. Figure 1 is only used to illustrate the conceptual origin and is not related to the theoretical derivations presented later in the paper.
B. Based on Figures 3 and 4, the semicircular beam exhibits the poorest tension–compression symmetry.
C. In Figure 7,... | {"Choice": "B,D"} | C3 | json_match | [
"e3/e3ca806b38f95a6343635d61bb90b57bee606a8cde51ec771d9b5574726ef15c.jpg",
"ef/ef68cc91e776b93592f174850d0319e566298675b7a63e4281690e87c68d16b1.jpg",
"b3/b397d6b27739a32b8a8508a29d71bde865422fbe7886095d1055c1b4087b0e02.jpg",
"fd/fdaa3818936864fab8e78e3451463d02b36f3fade7cb73ccbf8c15c90d7ceb55.jpg",
"13/13c9... | [{"text":"# A tensegrity-inspired bidirectional quasi-zero stiffness metamaterial for buffering and energy absorption"},{"image":"e3/e3ca806b38f95a6343635d61bb90b57bee606a8cde51ec771d9b5574726ef15c.jpg"},{"text":"## 1. Introduction\n\nMetamaterials are artificially designed with microstructures that are periodically ar... | np_c3_05_001__en_interleave | np_c3_05_001 | en_interleave | interleave | json | {} | np_c3_05 | scientific | en | np_c3_05_001 | [
"images/SciDocBench/e3/e3ca806b38f95a6343635d61bb90b57bee606a8cde51ec771d9b5574726ef15c.jpg",
"images/SciDocBench/ef/ef68cc91e776b93592f174850d0319e566298675b7a63e4281690e87c68d16b1.jpg",
"images/SciDocBench/b3/b397d6b27739a32b8a8508a29d71bde865422fbe7886095d1055c1b4087b0e02.jpg",
"images/SciDocBench/fd/fdaa3... | |
220 | You are a helpful AI Article Reader. The first PDF is "Strategies for Pre-Training Graph Neural Networks" (the baseline paper). Based ONLY on that baseline paper, list the GNN model architectures that are evaluated as variants of pretrain GNN. Output a JSON list of model names, sorted alphabetically.
Example output:
[... | {"answer_list": ["GAT", "GCN", "GIN", "GraphSAGE"]} | D2 | json_match | [
"01/019b816fc22ae0cbdda69c1eaacb82fc085abdb50635258d832bca2817399f08.jpg"
] | [{"text":"<table><tr><td colspan=\"2\">Dataset</td><td>BBBP</td><td>Tox21</td><td>ToxCast</td><td>SIDER</td><td>ClinTox</td><td>MUV</td><td>HIV</td><td>BACE</td><td>Average</td></tr><tr><td colspan=\"2\"># Molecules</td><td>2039</td><td>7831</td><td>8575</td><td>1427</td><td>1478</td><td>93087</td><td>41127</td><td>151... | np_d2_01_001__en_interleave | np_d2_01_001 | en_interleave | interleave | json | {} | np_d2_01 | scientific | en | np_d2_01_001 | [
"images/SciDocBench/01/019b816fc22ae0cbdda69c1eaacb82fc085abdb50635258d832bca2817399f08.jpg"
] | |
221 | You are an AI for biology article analysis. Based on the provided pages, list the top-level databases that are explicitly named as being used in these works. Use canonical short names. Do not list sub-datasets or invent databases. Include any database that is explicitly described as a data source used in these works (c... | {"answer_list": ["ActiveDriverDB", "ClinVar", "Genebass", "PhosphoSitePlus", "PTMint", "ProteinGym", "UniProt", "dbPTM", "gnomAD"]} | D2 | json_match | [
"ef/ef0a3ae63b7d97588397e1b672213d34b692bac42854540605e5fabfbefe9ac5.jpg",
"6c/6ced68d7137bb3b75c262c085cd71d85a6c03b8007ef50e2a38b765c04027dc4.jpg",
"20/20b1cff4fcd73c4708dc13d44c5f750b6de4eb07919559dce36137bd2fc0d048.jpg",
"ee/ee09133536a1c11f8bd107d88d67b11182d331fc677643c75ed777371609f062.jpg",
"0c/0c55... | [{"text":"to dominant clusters, yielding limited structural novelty and reduced broad-spectrum activity [8]. Experiments show that ProtFlow outperforms various state-of-the-art generative methods in generating high-quality peptides, and realistic, diverse, and novel AMPs with broad spectrum activity, especially against... | np_d2_02_001__en_interleave | np_d2_02_001 | en_interleave | interleave | json | {} | np_d2_02 | scientific | en | np_d2_02_001 | [
"images/SciDocBench/ef/ef0a3ae63b7d97588397e1b672213d34b692bac42854540605e5fabfbefe9ac5.jpg",
"images/SciDocBench/6c/6ced68d7137bb3b75c262c085cd71d85a6c03b8007ef50e2a38b765c04027dc4.jpg",
"images/SciDocBench/20/20b1cff4fcd73c4708dc13d44c5f750b6de4eb07919559dce36137bd2fc0d048.jpg",
"images/SciDocBench/ee/ee091... | |
222 | You are given two scientific PDF documents about training Qwen2.5-VL-7B on caption datasets ScaleCap-450k and CapRL-1M. List the benchmark names that appear in BOTH documents' main evaluation tables for these two models. Sort alphabetically (case-insensitive). Use the capitalization shown in the ScaleCap document.
Out... | {"answer_list": ["ChartQA", "InfoVQA", "MMStar", "MMVet"]} | D3 | json_match | [] | [{"text":"Table 1: Performance comparison using different pretraining datasets. CapRL-1M significantly outperforms other datasets across all 3 settings, and further improvements are observed when scaling the data to 5M. The best results are bold and the second-best results are underlined.\n\n<table><tr><td>Pretraining ... | np_d3_01_001__en_interleave | np_d3_01_001 | en_interleave | interleave | json | {} | np_d3_01 | scientific | en | np_d3_01_001 | [] | |
223 | You are a chemistry paper reading assistant. The provided PDFs are research articles on NCM-811 cathode materials and their cycling stability. Across all provided articles, find the HIGHEST capacity retention percentage reported for NCM-811 (or its modified form) at exactly 500 cycles. Report the value and the voltage ... | {"highest_retention_at_500_cycles_pct": null, "voltage_window": null} | D3 | json_match | [
"d7/d7fdb8587e0d89e2048a2d9213de48c16663f00745dc789dd281100202612723.jpg",
"e4/e4a76e0ba7d347746ffcac75a5380e443d13523ef1af4e817a7d4b6c6654e6df.jpg",
"7b/7b42f28235bc661a9cb26be07a5fdf7c7412190e928d50c530335dffcf003fd8.jpg",
"85/853b6d8b7f766b415cfd035e13d2caa89d7ade403ea4affb3181d3986242ab31.jpg",
"ba/ba07... | [{"image":"d7/d7fdb8587e0d89e2048a2d9213de48c16663f00745dc789dd281100202612723.jpg"},{"image":"e4/e4a76e0ba7d347746ffcac75a5380e443d13523ef1af4e817a7d4b6c6654e6df.jpg"},{"image":"7b/7b42f28235bc661a9cb26be07a5fdf7c7412190e928d50c530335dffcf003fd8.jpg"},{"text":"Figure 1. XRD patterns of (a) the uncoated NMC811 and LiF-... | np_d3_02_001__en_interleave | np_d3_02_001 | en_interleave | interleave | json | {} | np_d3_02 | scientific | en | np_d3_02_001 | [
"images/SciDocBench/d7/d7fdb8587e0d89e2048a2d9213de48c16663f00745dc789dd281100202612723.jpg",
"images/SciDocBench/e4/e4a76e0ba7d347746ffcac75a5380e443d13523ef1af4e817a7d4b6c6654e6df.jpg",
"images/SciDocBench/7b/7b42f28235bc661a9cb26be07a5fdf7c7412190e928d50c530335dffcf003fd8.jpg",
"images/SciDocBench/85/853b6... | |
224 | In Fig. 5b of the paper, three vertical layers of nodes are shown. List the labels of the blocks in the MIDDLE layer (top to bottom) and the labels of the nodes in the RIGHT layer (top to bottom).
Output JSON in this exact format:
{
"middle_layer_top_to_bottom": ["<label_1>", "<label_2>", ...],
"right_layer_top_to... | {"middle_layer_top_to_bottom": ["Progenitor maintenance", "Haemoglobin synthesis", "Autophagy", "Cell cycle"], "right_layer_top_to_bottom": ["MCH", "RDW", "IRF"]} | E1 | json_match | [
"48/482ca29e9d73ae53fb14a6b1117bb349915c9855abe5d2d0b37e41cfaebb74d8.jpg",
"e7/e701f66195bb314b9fed7f5dd6cb1e0d741b362ab99a16ba044119056c754b87.jpg"
] | [{"text":"## Article"},{"image":"48/482ca29e9d73ae53fb14a6b1117bb349915c9855abe5d2d0b37e41cfaebb74d8.jpg"},{"text":"b Program regulatory map explains cross-trait relationships \nC Program association with erythropoiesis"},{"image":"e7/e701f66195bb314b9fed7f5dd6cb1e0d741b362ab99a16ba044119056c754b87.jpg"},{"text":"Fig.... | np_e1_01_001__en_interleave | np_e1_01_001 | en_interleave | interleave | json | {} | np_e1_01 | scientific | en | np_e1_01_001 | [
"images/SciDocBench/48/482ca29e9d73ae53fb14a6b1117bb349915c9855abe5d2d0b37e41cfaebb74d8.jpg",
"images/SciDocBench/e7/e701f66195bb314b9fed7f5dd6cb1e0d741b362ab99a16ba044119056c754b87.jpg"
] | |
225 | You are an AI Bioinformatics research assisstant.Here is the file structure tree for article:
```
├── 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_mutation... | {
"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 | [
"a5/a5ea93fe4c325a63835ae0a0db0364d8ca2720b0b897c872755032f6c9f2f204.jpg",
"b7/b7f2c68826ae283911a17fb082604782b2626cd674cc9698cb44db7a9dc8441c.jpg",
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"82/8253e6f496055504aa157e7941dad5d690cb8ab5ed8a92ea3f5ab0e19aad3fd0.jpg",
"06/06a4... | [{"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... | np_f2_01_001__en_interleave | np_f2_01_001 | en_interleave | interleave | json | {} | np_f2_01 | scientific | en | np_f2_01_001 | [
"images/SciDocBench/a5/a5ea93fe4c325a63835ae0a0db0364d8ca2720b0b897c872755032f6c9f2f204.jpg",
"images/SciDocBench/b7/b7f2c68826ae283911a17fb082604782b2626cd674cc9698cb44db7a9dc8441c.jpg",
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"images/SciDocBench/82/8253e... | |
226 | Analyze the provided quantum computing survey paper and identify all cited research works explicitly classified within the paper's taxonomic systems. Assign classification labels from the following dimensions:
Dimension 1: Basic_Characteristics
- QI
- CQCT
- PM
Dimension 2: Algorithmic_Characteristics
- P
- ACQA
- T... | {
"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 | [
"8c/8c54610ec6d6ce36c2c039fb1de6bf0e13f860c312438d8d4221ad244c05f4ad.jpg",
"a6/a68e15d078dc86d61f44b29b5fd8323e0af689877d56a20f241df183e2e92d0f.jpg",
"01/017467834e593ee8e5e6bf062bb69922dbfdc0976834b9b3a39b33a3a1ccb030.jpg"
] | [{"text":"computation speed will grow faster by using these quantum algorithms. Li et al. [35] discussed merging the elements of quantum mechanics with the intelligent nature-inspired algorithms to mark the new era of computing in the making. Quantum optimization and quantum learning are two classifications based on wh... | np_a3_01_002__en_interleave | np_a3_01_002 | en_interleave | interleave | 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 | en | np_a3_01_002 | [
"images/SciDocBench/8c/8c54610ec6d6ce36c2c039fb1de6bf0e13f860c312438d8d4221ad244c05f4ad.jpg",
"images/SciDocBench/a6/a68e15d078dc86d61f44b29b5fd8323e0af689877d56a20f241df183e2e92d0f.jpg",
"images/SciDocBench/01/017467834e593ee8e5e6bf062bb69922dbfdc0976834b9b3a39b33a3a1ccb030.jpg"
] |
227 | According to the document, answer the following problem.
In Figure 1, what do the dashed lines and solid lines denote respectively? | 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 | [
"38/38892c57ecf59bcc8a286189ce4764a4fadc4e734e8d68eb977108a5fc85df80.jpg",
"31/3193c32b8edd3461db567ecd25997829b994b3a47597000fb5b8876038afc0d2.jpg"
] | [{"image":"38/38892c57ecf59bcc8a286189ce4764a4fadc4e734e8d68eb977108a5fc85df80.jpg"},{"text":"Figure 1: (a) Overview of the bidirectional spike-based distillation framework. (b) Illustration of feature alignment using the properties of pyramidal neurons, which receive feedforward and feedback signals through their basa... | np_a5_05_002__en_interleave | np_a5_05_002 | en_interleave | interleave | text | {} | np_a5_05 | scientific | en | np_a5_05_002 | [
"images/SciDocBench/38/38892c57ecf59bcc8a286189ce4764a4fadc4e734e8d68eb977108a5fc85df80.jpg",
"images/SciDocBench/31/3193c32b8edd3461db567ecd25997829b994b3a47597000fb5b8876038afc0d2.jpg"
] | |
228 | According to Fig.3, answer the following problems.
1. For \chi_M''-frequency curve, describe the variation trend of the peak's coordination. （Answer in the field of 'Figure 3(b) curve'）
2. Identify the frequency of the peak of \chi_M''-frequency curve at 51K. (Give an exact number)
Answer in the following format:
{"Fig... | {
"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 | [
"ae/ae0d0e601dcb7a1e15f4f6cef12fa8b90d69dafe527b899291ec4d22d1b5fb28.jpg",
"cb/cb66ad405b99ff7b547341f5eac983731fdcc6252440a1052e66ee2af992182f.jpg",
"8c/8c559f756f2332985f42fda7a242d6c1121e02aee56bfb8e224de9122adb5108.jpg",
"2e/2e1d8543cb758eeddf7b1bee280e05d9a25d177a468b80de0c7686336a4a1146.jpg",
"35/3557... | [{"text":"ARTICLE\n\nSelvan Demir $^{1,2}$ , Miguel I. Gonzalez $^{1}$ , Lucy E. Darago $^{1}$ , William J. Evans $^{3}$ & Jeffrey R. Long $^{1,4,5}$\n\nCoordination chemistry can be utilized to yield electronic structures designed to prevent through-barrier relaxation pathways, with the goal of attaining magnetic bloc... | np_c3_04_002__en_interleave | np_c3_04_002 | en_interleave | interleave | 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 | en | np_c3_04_002 | [
"images/SciDocBench/ae/ae0d0e601dcb7a1e15f4f6cef12fa8b90d69dafe527b899291ec4d22d1b5fb28.jpg",
"images/SciDocBench/cb/cb66ad405b99ff7b547341f5eac983731fdcc6252440a1052e66ee2af992182f.jpg",
"images/SciDocBench/8c/8c559f756f2332985f42fda7a242d6c1121e02aee56bfb8e224de9122adb5108.jpg",
"images/SciDocBench/2e/2e1d8... |
229 | Here are the descriptions of interaction within a group of people. Based on figure 1(b), which subfigure best describes their interaction type? And what are the possible corresponding relationships between the people and the icons in the subfigure?
You need to first locate which subfigure represents the type of itera... | {"Subfigure": "Advice requests", "Choice": "C,D"} | A2 | judge | [
"4f/4fd1e8fb506a5bf28eac26cc7c03d88d35ac44b20a9ca5f262e473ff7dc05f15.jpg",
"3b/3bf407fe1cef2e205963fd12164dc0300226f398278aa1c8c00fb8e6307098f6.jpg",
"f8/f8482bebbe3e287eed4d35630024561794779b2f9661706b97a7e34504626a1b.jpg",
"a8/a8f13cfafd6ece8bc699dae60a624452add973f1b16a82bec693aca27c545431.jpg",
"57/574c... | [{"text":"# Inferring the internal structure of groups through the integration of statistical learning and causal reasoning\n\nHuman social life unfolds within richly structured networks of overlapping relationships, including friendships, hierarchies, and collaborations. Yet the observable interactions that reveal the... | np_a2_02_002__en_interleave | np_a2_02_002 | en_interleave | interleave | 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 | en | np_a2_02_002 | [
"images/SciDocBench/4f/4fd1e8fb506a5bf28eac26cc7c03d88d35ac44b20a9ca5f262e473ff7dc05f15.jpg",
"images/SciDocBench/3b/3bf407fe1cef2e205963fd12164dc0300226f398278aa1c8c00fb8e6307098f6.jpg",
"images/SciDocBench/f8/f8482bebbe3e287eed4d35630024561794779b2f9661706b97a7e34504626a1b.jpg",
"images/SciDocBench/a8/a8f13... |
230 | You are a AI for biology article analyser.Please tell me all the databases used in these works.
Your answer format is:
{
<dataset_name> : [
<subdataset_name1>,
<subdataset_name2>,
...
],
...
}
Please give me json only | {
"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 | [
"54/540d7a2d34d0ca9aba3c756abf62b38c0ae529264b83abbb9d18c34160e6f6ae.jpg",
"a2/a2707320c5c1c2766e22a32ec699c851339a5afea70e4be16229fb218bff1067.jpg",
"b2/b22ae79cdb1b9977368330a703fc19b675950c9d4b44401939dbd26cd8decc98.jpg",
"f0/f035e01cec2586cae4383bcd45449b4357251bdad72fdeb5465d7c863568820f.jpg",
"b5/b559... | [{"text":"Article\n\nReceived: 10 May 2025\n\nCheck for updates\n\nProtein language models (PLMs) have recently emerged as a promising approach for next-generation variant-effect prediction (VEP). Most high-performing VEP methods currently utilize PLMs combined with additional information, such as homology, protein str... | np_a4_02_001_dup2__en_interleave | np_a4_02_001_dup2 | en_interleave | interleave | 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 | en | np_a4_02_001_dup2 | [
"images/SciDocBench/54/540d7a2d34d0ca9aba3c756abf62b38c0ae529264b83abbb9d18c34160e6f6ae.jpg",
"images/SciDocBench/a2/a2707320c5c1c2766e22a32ec699c851339a5afea70e4be16229fb218bff1067.jpg",
"images/SciDocBench/b2/b22ae79cdb1b9977368330a703fc19b675950c9d4b44401939dbd26cd8decc98.jpg",
"images/SciDocBench/f0/f035e... |
231 | According to Figure 1, in which energy intervals are the counts of prompt signals less than those of delay signals? | 2 to 2.5 MeV and 9 to 10 Mev，approximately. | A5 | judge | [
"de/ded8d193d530cdaa4c98122821271e1a22510a3bbfe94d878ce979a14ab4119e.jpg",
"59/599c81bf1d32771f0e42695e9ce7fdf9bae12bd84a1b736b66ff996e743c960f.jpg",
"34/349b5940d69da9622dad0a58d7514a929cf4faa0af0caf35bda0b09baaa3ee94.jpg",
"dc/dc8c1fcff01dc21d0cbd7de395de60908fd34ebb8b11461ebf02779e12047355.jpg",
"82/8276... | [{"text":"# New limits on the Pauli forbidden transitions in ${}^{12}$ C nuclei obtained with the complete Borexino dataset\n\nD. Basilico $^{1}$ , G. Bellini $^{1}$ , J. Benziger $^{2}$ , R. Biondi $^{3,10}$ , B. Caccianiga $^{1}$ , A. Caminata $^{5}$ , A. Chepurnov $^{6}$ , D. D'Angelo $^{1}$ , A. Derbin $^{7,8}$ , A... | np_a5_04_002__en_interleave | np_a5_04_002 | en_interleave | interleave | text | {} | np_a5_04 | scientific | en | np_a5_04_002 | [
"images/SciDocBench/de/ded8d193d530cdaa4c98122821271e1a22510a3bbfe94d878ce979a14ab4119e.jpg",
"images/SciDocBench/59/599c81bf1d32771f0e42695e9ce7fdf9bae12bd84a1b736b66ff996e743c960f.jpg",
"images/SciDocBench/34/349b5940d69da9622dad0a58d7514a929cf4faa0af0caf35bda0b09baaa3ee94.jpg",
"images/SciDocBench/dc/dc8c1... | |
232 | You are a helpful AI Artical Reader. Please read the following papers and summarize the performance of different variants of pretrain GNN on molecular task datasets, using the best scores achieved by all strategies, including scores, accuracy.
The papers you need to read are:
1. Strategies for Pre-Training Graph Neura... | ```
| Model \ Dataset | BBBP | Tox21 | ToxCast | SIDER | ClinTox | MUV | HIV | BACE | ESOL | FreeSolv | Lipo | QM7 | QM8 | QM9 |
| --------------- | ------- | ------- | ---------------- | ------- | ------- | ------- | ------- | ------- | -------- | -------- | -------... | D2 | judge | [
"7f/7f593ba614ebabe052cb2c5d585c064a24f958f89ad8758d5d51e1c710515a1c.jpg",
"5b/5b56c7db42e16247d6d319d2755e5e93ba86ebe61307ab2df08080de249f7994.jpg",
"03/03ebd4b91afec1b0b5cf3dd7cff4274f52d2f8e9db3d80b6f4ff3324865a63d9.jpg",
"17/173abdf49d3f6e9d3671efe3b49a0aa03fe52c1383dbd94ffbe8dd39f0bb17a0.jpg",
"9c/9ce7... | [{"text":"# Molecular Contrastive Learning of Representations via Graph Neural Networks\n\nMolecular representation is fundamental and essential in the design of functional and novel chemical compounds $^{1-3}$ . Due to the enormous magnitude of possible stable chemical compounds, development of an informative represen... | np_d2_01new_001__en_interleave | np_d2_01new_001 | en_interleave | interleave | 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 | en | np_d2_01new_001 | [
"images/SciDocBench/7f/7f593ba614ebabe052cb2c5d585c064a24f958f89ad8758d5d51e1c710515a1c.jpg",
"images/SciDocBench/5b/5b56c7db42e16247d6d319d2755e5e93ba86ebe61307ab2df08080de249f7994.jpg",
"images/SciDocBench/03/03ebd4b91afec1b0b5cf3dd7cff4274f52d2f8e9db3d80b6f4ff3324865a63d9.jpg",
"images/SciDocBench/17/173ab... |
233 | You are given two scientific PDF documents. Your task is to summarize the performance of the models obtained after training Qwen2.5-VL-7B on datasets ScaleCap-450k and CapRL-1M. Only retain the performance on the benchmarks that appear in both documents.
Implement the following steps:
- Find the corresponding tables ... | [{"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 | [
"a7/a7061e0c9f459c91b4349abef997171518b954020f9ac4795aa4cc95519e190c.jpg",
"d9/d9074f6b4d42e92a491e6ccb4f7fedf6103a264bc7a3ab8ad2f136124810a1ee.jpg",
"8f/8f607b483f7e20df33d84d6f54832d53da1d6fb791691c263537670bf82d42f5.jpg",
"da/da7f94c2f0963fe2f4176b91513cd184c0b89733f80a4c8058879025f11997a2.jpg",
"32/3266... | [{"text":"# ScaleCap: Inference-Time Scalable Image Captioning via Dual-Modality Debiasing\n\nThis paper presents ScaleCap, an inference-time scalable image captioning strategy that generates comprehensive and detailed image captions. The key challenges of high-quality image captioning lie in the inherent biases of LVL... | np_d3_01new_001__en_interleave | np_d3_01new_001 | en_interleave | interleave | 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 | en | np_d3_01new_001 | [
"images/SciDocBench/a7/a7061e0c9f459c91b4349abef997171518b954020f9ac4795aa4cc95519e190c.jpg",
"images/SciDocBench/d9/d9074f6b4d42e92a491e6ccb4f7fedf6103a264bc7a3ab8ad2f136124810a1ee.jpg",
"images/SciDocBench/8f/8f607b483f7e20df33d84d6f54832d53da1d6fb791691c263537670bf82d42f5.jpg",
"images/SciDocBench/da/da7f9... |
234 | You are a chemistry paper reading assistant. Please help me verify the capacity retention data of NCM-811 or its modified materials under the following experimental conditions in these three papers:
1. Cycle number: $500$ cycles
2. Voltage window: around $2.8 \sim 4.5 V$
3. Mass loading: $10 mg cm^{-2}$
4. Charge rate... | {
"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 | [
"e7/e7726de563a7a49567ac5faf12545f60911fde848a71d26f2719626df9626a01.jpg",
"2f/2fdcbf302a46a98d2e192dec0fc9d8ce920b86330b1cc70edff56f02aa127c63.jpg",
"53/534491326725fd6194af7898986ab8a077bb70dc2b52a1b158d49ebcbc2b934c.jpg",
"fc/fc3ecbb9c1cf5bddbe31607c667599cdd5f5247b273218c931938d159b11199f.jpg",
"0a/0a80... | [{"text":"pubs.acs.org/cm\n\n# Long-Term Cyclability of NCM-811 at High Voltages in Lithium-Ion Batteries: an In-Depth Diagnostic Study"},{"image":"e7/e7726de563a7a49567ac5faf12545f60911fde848a71d26f2719626df9626a01.jpg"},{"image":"2f/2fdcbf302a46a98d2e192dec0fc9d8ce920b86330b1cc70edff56f02aa127c63.jpg"},{"text":"ABSTR... | np_d3_02new_001__en_interleave | np_d3_02new_001 | en_interleave | interleave | 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 | en | np_d3_02new_001 | [
"images/SciDocBench/e7/e7726de563a7a49567ac5faf12545f60911fde848a71d26f2719626df9626a01.jpg",
"images/SciDocBench/2f/2fdcbf302a46a98d2e192dec0fc9d8ce920b86330b1cc70edff56f02aa127c63.jpg",
"images/SciDocBench/53/534491326725fd6194af7898986ab8a077bb70dc2b52a1b158d49ebcbc2b934c.jpg",
"images/SciDocBench/fc/fc3ec... |
235 | Reproduce Image 5b in mermaid format, using red (#cc0000) and blue arrows(#0066cc) to distinguish. You only need draw the three columns with arrows. The left column starts with the node 'CAD'. The middle layer starts with the block 'Progenitor maintenance', and the right layer starts with the node 'MCH'. Ignore the col... | ```
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 | [
"69/69fb3b747f9a98012e9f0fa8d6572269bc358adbc50cbf1f1b861b04c9b86119.jpg",
"99/99230b4eebac9dbbf91a6388a45a39fd6158ceac166f245854acf5198470b831.jpg"
] | [{"text":"Overall, the Perturb-seq data efficiently captured biological pathways and their regulators, and comparison with gene associations enable us to identify the pathways relevant to each trait.\n\n## Complex interplay of programs\n\nAlthough the significant programs in Fig. 4c–e provide insight into biological co... | np_e1_01new_001__en_interleave | np_e1_01new_001 | en_interleave | interleave | 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 | en | np_e1_01new_001 | [
"images/SciDocBench/69/69fb3b747f9a98012e9f0fa8d6572269bc358adbc50cbf1f1b861b04c9b86119.jpg",
"images/SciDocBench/99/99230b4eebac9dbbf91a6388a45a39fd6158ceac166f245854acf5198470b831.jpg"
] |
236 | Please read the attached paper thoroughly and extract the following information in JSON format:
1. Model: List all models proposed in this paper. For each model, provide its name and the base model it is built upon.
2. Data: Provide the name of the training dataset constructed in this paper, and list all source dataset... | {
"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 | [
"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\n\nYuhong Liu $^{1,2}$ , Beichen Zhang $^{1,3}$ , Yuhang Zang $^{1}$ , Yuhang Cao $^{1}$ , Long Xing $^{1}$ Xiaoyi Dong $^{1}$ , Haodong Duan $^{1}$ , Dahua Lin $^{1,3}$ , Jiaqi Wang $^{1,4}$ $^{1}$ Shanghai AI Laborato... | spatial-ssrl_002__en_interleave | spatial-ssrl_002 | en_interleave | interleave | 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 | en | spatial-ssrl_002 | [
"images/SciDocBench/f4/f4ab7bfd1961eec716fd1d8a933556858e4a2503689ac4f95000821f0d46a08b.jpg",
"images/SciDocBench/b3/b338778f7d447ae15465957a7f5872c3e1c19a3c17f11c508b107d5a46f19928.jpg",
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"images/SciDocBench/c4/c4251... |
237 | You are a paper reading assistant. Please carefully classify the quoted texts in this article. The main categories are:
1. Background: Provide theoretical support or research context (e.g., the arrival of the AI era).
2. Method: Adopt or compare existing methods.
3. Result: Take others’ results as baseline for comparis... | {
"[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 | [
"ef/ef0a3ae63b7d97588397e1b672213d34b692bac42854540605e5fabfbefe9ac5.jpg",
"6a/6aed035c2512d7b3268aa61d97eb5e6805a8fc79b28fd875152f8dd5d6127eb3.jpg",
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"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_002__en_interleave | ProtFlowArticle_002 | en_interleave | interleave | 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 | en | ProtFlowArticle_002 | [
"images/SciDocBench/ef/ef0a3ae63b7d97588397e1b672213d34b692bac42854540605e5fabfbefe9ac5.jpg",
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238 | You are given a scientific PDF document. Your task is to extract and reproduce the body text of the subsection titled "Resolution metrics" in section 6.2 in LaTex.
Follow these rules strictly:
Include all running prose and paragraph text from the section body, preserving the original wording exactly.
Preserve all orig... | 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 | [
"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_003__en_interleave | depth-anything3_003 | en_interleave | interleave | text | {} | depth-anything3 | cs | en | depth-anything3_003 | [
"images/SciDocBench/d7/d7ef77ceb057751425b6f46b1d7cf9368539796729018cd3436dcc5cb7b0690d.jpg",
"images/SciDocBench/5a/5a22e4539681dd3f4edfb98720a1de3d878693417c6d27a127eff023ffb987b0.jpg",
"images/SciDocBench/77/77ec4333863d500d9a3e62027712a7343ce9cd05f29e4266df42a0e22589be27.jpg",
"images/SciDocBench/8f/8f9e5... | |
239 | You are a Professional Scientific Editor and LaTeX Typesetter.
Your goal is to generate a comprehensive "Index of Notations" for the provided research paper content.
Output Requirement:
Please generate a LaTeX Table code block using the booktabs package.
The table must have 3 columns:
Symbol: The LaTeX code for the sy... | $L$
$\ell$
$m$
$K$
$L_{\mathrm{res}}$
$L_{\mathrm{tr}}$
$x$
$y$
$B$
$\mathcal{L}_B(\theta)$
$\theta$
$\theta^+$
$\eta$
$\eta^\star$
$z^{(\ell)}(x;\theta)$
$z^{(\ell)}_i(x;\theta)$
$\sigma(\cdot)$
$\sigma'(\cdot)$
$q$
$\fan_{\mathrm{in}}(W)$
$W^{(\ell)}$
$W_{\mathrm{res}}$
$\partial_a$
$\Delta\theta_a$
$\Delta z^{(\ell)... | B1 | judge | [
"50/50979f59f6a72f7bd97cd524904a359f9c56617bae7410790d69ed06cd7dc002.jpg",
"b5/b55dd0a8b031104968d81463bef7007ce73be94c91dda133753b018b640a498f.jpg",
"10/10c593a6f50ca705eca2f7088a1b35f584039788c683e1f342137a17fd9f80a9.jpg",
"77/779f754f05260ec3d3b51f8961cdb31db1677aa421008cad67411dd0c31d9d38.jpg",
"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_002__en_interleave | hptransfer_002 | en_interleave | interleave | 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 | en | hptransfer_002 | [
"images/SciDocBench/50/50979f59f6a72f7bd97cd524904a359f9c56617bae7410790d69ed06cd7dc002.jpg",
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"images/SciDocBench/77/779f7... |
240 | You are given a scientific PDF document. Your task is to extract information about the composition and data sources of the MM-IFInstruct-23k dataset described in the paper, and reproduce it as a TikZ radial diagram in LaTeX.
Follow these rules strictly:
Extraction rules:
- Identify all source datasets that contribute t... | \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 | [
"bf/bf3a7030e7673dd1ff1421e48446f8d901d7cc5e13e00cf374800c4cc0ea1f3a.jpg",
"de/ded604c931d816239c8d45da34cba611b39736ccdefdd98ce684c944d0727249.jpg",
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"25/258da3c6ffeb1aade023df47a0914055290e35a1e672a66f7bc2b06188f13c29.jpg",
"b9/b960... | [{"text":"# MM-IFEngine: Towards Multimodal Instruction Following"},{"image":"bf/bf3a7030e7673dd1ff1421e48446f8d901d7cc5e13e00cf374800c4cc0ea1f3a.jpg"},{"text":"Figure 1. (a) Limitations of existing Multimodal Instruction Following (IF) benchmarks. (b) Overview of the MM-IFEval benchmark, which significantly surpasses ... | mmifenginenew_001__en_interleave | mmifenginenew_001 | en_interleave | interleave | 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 | en | mmifenginenew_001 | [
"images/SciDocBench/bf/bf3a7030e7673dd1ff1421e48446f8d901d7cc5e13e00cf374800c4cc0ea1f3a.jpg",
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"images/SciDocBench/25/258da... |
241 | You are given a scientific PDF document. Your task is to extract information about the composition and data sources of the ChartSFT dataset described in the paper, and reproduce it as a TikZ radial diagram in LaTeX.
Follow these rules strictly:
Extraction rules:
- Identify all source datasets (i.e., the column sources... | \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 | [
"4a/4af90b951af96cabc7d8ae3a447400fcfa6e7618b8db5b41f0a24b6b1353f78e.jpg",
"11/119d8a9bac844e575ec2e62f047487766a0810ff95dc767e222d1876d6421ed1.jpg",
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"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_002__en_interleave | chartassistant_002 | en_interleave | interleave | 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 | en | chartassistant_002 | [
"images/SciDocBench/4a/4af90b951af96cabc7d8ae3a447400fcfa6e7618b8db5b41f0a24b6b1353f78e.jpg",
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"images/SciDocBench/74/7408a... |
242 | # Role
You are an expert in **Scientometrics** and **LaTeX Visualization**. Your goal is to analyze the citations in a given scientific paper PDF and convert them into a **visual Citation Dashboard** using LaTeX/TikZ.
# Task
Generate a **standalone, compilable LaTeX file** that visualizes the citation network of the i... | \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 | [
"f6/f655dca68cfca8ca2c0c7b299667d834f627a87f18441c715150c28be7cf7309.jpg",
"5b/5b827bbe34f1a9b1e16632ad2aae08ec50b79ca947756c2a045650896e478570.jpg",
"da/da830b179aed8c38b3acf3359307ce8a27af06edf5ad5d6afcc2163acb547883.jpg"
] | [{"text":"# Spa3R: Predictive Spatial Field Modeling for 3D Visual Reasoning\n\nHaoyi Jiang $^{1*}$ Liu Liu $^{2\\dagger}$ Xinjie Wang $^{2}$ Yonghao He $^{3}$ Wei Sui $^{3}$ Zhizhong Su $^{2}$ Wenyu Liu $^{1}$ Xinggang Wang $^{1\\ddagger}$\n\n$^{1}$ Huazhong University of Science & Technology $^{2}$ Horizon Robotics $... | spa3r_002__en_interleave | spa3r_002 | en_interleave | interleave | 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 | en | spa3r_002 | [
"images/SciDocBench/f6/f655dca68cfca8ca2c0c7b299667d834f627a87f18441c715150c28be7cf7309.jpg",
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"images/SciDocBench/da/da830b179aed8c38b3acf3359307ce8a27af06edf5ad5d6afcc2163acb547883.jpg"
] |
243 | You are given three scientific PDF documents. The first introduces Cambrian-1, the second introduces LLaVA-OneVision, and the third introduces MAmmoTH-VL.
From the Cambrian-7M composition figure in the first paper and the Single-Image 3.2M composition figure in the second paper, extract all datasets in the yellow/oran... | {"group":"Group C","count":4,"datasets":["Hateful Memes","RefCOCO","VizWiz","VQAv2"]} | D2 | json_match | [
"37/3715439c3d2a59a7b5950e50beaec883bf13bc90f014bfb96b37ffc362d0436d.jpg",
"0e/0e7c7430efa1caf0d9addaef9002a8e487edac8cfb08ca03ac22ec0824a6d958.jpg",
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"ca/caf2660ba4236e638736ab0b9fcc573d6fd7699a4f0aa7c1a6d5ed6a572b11de.jpg",
"27/271e... | [{"text":"cross-attention to occur multiple times by inserting our proposal throughout the LLM layers—allowing consistent access to the uncompressed visual information (see Fig. 8-right).\n\nHyperparameters To flexibly modulate capacity, we introduce two hyperparameters D and G, which indicate the number of cross-atten... | cambrian-llavaov-mammoth_002__en_interleave | cambrian-llavaov-mammoth_002 | en_interleave | interleave | json | {} | ["cambrian1", "llavaov", "mammoth-vl"] | cs | en | cambrian-llavaov-mammoth_002 | [
"images/SciDocBench/37/3715439c3d2a59a7b5950e50beaec883bf13bc90f014bfb96b37ffc362d0436d.jpg",
"images/SciDocBench/0e/0e7c7430efa1caf0d9addaef9002a8e487edac8cfb08ca03ac22ec0824a6d958.jpg",
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"images/SciDocBench/ca/caf26... | |
244 | # Role
You are an expert in **Scientometrics** and **LaTeX Visualization**. Your goal is to analyze the citations in a given scientific paper PDF and convert them into a **visual Citation Dashboard** using LaTeX/TikZ.
# Task
Generate a **standalone, compilable LaTeX file** that visualizes the citation network of the i... | \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 | [
"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_003__en_interleave | ppo_003 | en_interleave | interleave | 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 | en | ppo_003 | [
"images/SciDocBench/5a/5ad2a24c1609a9ddeb84f25c73f030fa84fa3bc5a3aed8c1b96ead662219954f.jpg",
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"images/SciDocBench/f0/f06e8... |
245 | You are given 5 PDF files. The first PDF contains screenshots of 6 data samples arranged vertically from top to bottom, numbered 1 through 6. The remaining 4 PDF files are research papers, each describing a specific dataset. Your task:
1. Carefully examine each of the 6 data samples in the first PDF (numbered 1–6 from ... | [{"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 | [
"29/29371b964844a35b395c8879f1b35b2e665a3f192bea349eb1618db7a588073c.jpg",
"00/009c0043e58db79000c9a411b712d706a28c7f83dac0e32c2c42acf05b5f1f96.jpg",
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"63/63dc8af04f2dc95cd3d0876a191b684a4edfd569d0b1653b0d3f9e83d53147e6.jpg",
"d0/d005... | [{"text":"# TwiFF (Think With Future Frames): A Large-Scale Dataset for Dynamic Visual Reasoning\n\nJunhua Liu $^{1}$ Zhangcheng Wang $^{2}$ Zhike Han $^{31}$ Ningli Wang $^{4}$ Guotao Liang $^{5}$ Kun Kuang $^{1}$"},{"image":"29/29371b964844a35b395c8879f1b35b2e665a3f192bea349eb1618db7a588073c.jpg"},{"text":"## 2.1. Mu... | twiffnew_001__en_interleave | twiffnew_001 | en_interleave | interleave | 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 | en | twiffnew_001 | [
"images/SciDocBench/29/29371b964844a35b395c8879f1b35b2e665a3f192bea349eb1618db7a588073c.jpg",
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"images/SciDocBench/63/63dc8... |
246 | I'm reading two documents which may help me with my future research about SLE. In document 1, a type of specific IgG glycosylation is mentioned to have a strong correlation with SLE which I want to be involved in my future research. I am also interested in studying sialylated IgG in SLE. I have seen several substances... | {"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 | [
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"5f/5f9b... | [{"text":"Research review paper"},{"image":"24/245e2a2b06f8078a09c7504e10900ff1371db379c5f99c5bd9da5eec01c5e138.jpg"},{"text":"$^{c}$ University of Zagreb, Faculty of Pharmacy and Biochemistry, Ulica Ante Kovacića 1, Zagreb, Croatia\n\n## 1. Introduction\n\n## 2. Glycosylated IgG holds a central role in immunity\n\nIgG... | sle-glyco-iggnew_001__en_interleave | sle-glyco-iggnew_001 | en_interleave | interleave | 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 | en | sle-glyco-iggnew_001 | [
"images/SciDocBench/24/245e2a2b06f8078a09c7504e10900ff1371db379c5f99c5bd9da5eec01c5e138.jpg",
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"images/SciDocBench/83/834e5... |
247 | I'm attaching a physics paper. Please read it and reproduce the graph structure shown in Figure 1(b) — specifically the 3-regular graph illustration embedded in the figure — as a LaTeX/TikZ figure.
Requirements:
- The graph has N = 22 nodes, each connected to exactly 3 edges (3-regular)
- Nodes should be rendered as fi... | \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 | [
"cc/ccd436a940892f67a3ec180bafd24b24ca4a03aa5561cffd43617c19135de4f0.jpg",
"83/837fd4a87d038a2e98dc23b26081174627f636bba6452b4c17142909db9cbbeb.jpg",
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"38/38e82190d688cc355a267249ae33c15ca233b0771922b7bd832f9e19a8800d26.jpg",
"2c/2cef... | [{"text":"# Imaginary-time evolution of interacting spin systems in the truncated Wigner approximation\n\nTom Schlegel, $^{1}$ Dennis Breu, $^{1}$ and Michael Fleischhauer $^{1}$\n\n$^{1}$ Department of Physics and Research Center OPTIMAS,\n\nWe present a semiclassical phase-space method to calculate thermal and ground... | itwa-spin_002__en_interleave | itwa-spin_002 | en_interleave | interleave | 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 | en | itwa-spin_002 | [
"images/SciDocBench/cc/ccd436a940892f67a3ec180bafd24b24ca4a03aa5561cffd43617c19135de4f0.jpg",
"images/SciDocBench/83/837fd4a87d038a2e98dc23b26081174627f636bba6452b4c17142909db9cbbeb.jpg",
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"images/SciDocBench/38/38e82... |
248 | 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 | [
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"images/SciDocBench/63/63ca9... | ||||||
249 | 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 | [
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"c7/c7a6... | aug_B1_1_2__zh_all_first | aug_B1_1_2 | zh_all_first | all_first | zh | 7 | [
"images/SciDocBench/ff/ffba42bc406ecf44a9a0cf4b90c97586964eb7ef89e4a9e4f028fc2643298b77.jpg",
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"images/SciDocBench/91/91e7c... | ||||||
250 | 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 | [
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"b1/b11e9b13fd11b14997fb35669f2b30ab0396f8eb655d3138a2b7732322ed6eef.jpg",
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"3d/3d79... | aug_B3_1_2__zh_all_first | aug_B3_1_2 | zh_all_first | all_first | zh | 10 | [
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"images/SciDocBench/b1/b11e9b13fd11b14997fb35669f2b30ab0396f8eb655d3138a2b7732322ed6eef.jpg",
"images/SciDocBench/45/45e24ec929e6b9d7875a7744cc3ff9abe61627d521fbb1b43bb107943730a7e6.jpg",
"images/SciDocBench/dc/dc446... | ||||||
251 | 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 | [
"89/89a2943286fdd4f6bb9e925b72af234c397a7f99d3ac057b5d90a3e19a7b96be.jpg",
"b5/b54c54cb00684e02446344ad3d54dc6dae2686a3f814ea1c54cc4654d45f0572.jpg",
"bd/bd6663233a7c2ba01b16564ff795ed3c38d48ee2c1e665e500866cce6ab41947.jpg",
"92/92dee68b61f923b49c8caa310104823c4b6567373c839e75981a7e37ae8c0876.jpg",
"ea/ea59... | aug_D1_1_2__zh_all_first | aug_D1_1_2 | zh_all_first | all_first | zh | 14 | [
"images/SciDocBench/89/89a2943286fdd4f6bb9e925b72af234c397a7f99d3ac057b5d90a3e19a7b96be.jpg",
"images/SciDocBench/b5/b54c54cb00684e02446344ad3d54dc6dae2686a3f814ea1c54cc4654d45f0572.jpg",
"images/SciDocBench/bd/bd6663233a7c2ba01b16564ff795ed3c38d48ee2c1e665e500866cce6ab41947.jpg",
"images/SciDocBench/92/92dee... | ||||||
252 | 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 | [
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"60/607a... | aug_F2_1_1__zh_all_first | aug_F2_1_1 | zh_all_first | all_first | zh | 22 | [
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"images/SciDocBench/15/15837... | ||||||
253 | 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 | [
"de/de6acb9a3f9a4cea3375adeddced89fdacf74e18d1db5717695b3314fd36a72b.jpg",
"13/1385c6a246795bd9395dbafd230c7c2e148681cb2c633e1cb9157affd1ae13fc.jpg",
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"e9/e982... | mmifengine_001__zh_all_first | mmifengine_001 | zh_all_first | all_first | 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/de/de6acb9a3f9a4cea3375adeddced89fdacf74e18d1db5717695b3314fd36a72b.jpg",
"images/SciDocBench/13/1385c6a246795bd9395dbafd230c7c2e148681cb2c633e1cb9157affd1ae13fc.jpg",
"images/SciDocBench/6f/6ff892681a86f47df62fbf288547c3de847255901ec451f7b838c0acf18f33be.jpg",
"images/SciDocBench/eb/eb8dc... | |
254 | 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 | [
"a7/a7f9a113f47dad25ab171a75e5cd2c60f9985127236c6dcd494a7fc7bb0c5bd5.jpg",
"01/0129a4771975d1107ba796a304c2cbb20b3bca8f6c3f685a005027d3b33221fc.jpg",
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"dd/dd99... | llavacot_001__zh_all_first | llavacot_001 | zh_all_first | all_first | 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/a7/a7f9a113f47dad25ab171a75e5cd2c60f9985127236c6dcd494a7fc7bb0c5bd5.jpg",
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"images/SciDocBench/85/851d2... | ||
255 | 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 | [
"4d/4dc402b42b16ddece764c41f8fb4a75d3e94bb5bccda4429d41ce9a208c79841.jpg",
"8a/8a42354577f0049391ff5c41a92650e3e1a86c65744f8366c56c6232ddd59b8b.jpg",
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"02/02d0... | ppo_001__zh_all_first | ppo_001 | zh_all_first | all_first | 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 | [
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"images/SciDocBench/b8/b8f65... | ||
256 | 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 | [
"35/35148e3e76f7fbf7fe6c3a86e2b984dfcdce1ebed9968ce37d93c9d2b4efef19.jpg",
"a5/a5d320faacad1bb14027003b9dc37c78b475247bd6252122ce79611fd7a15b88.jpg",
"14/14d5a0924ac981cf653042e3be06cc0d0ce504f307277739a98fc48bc5c4670a.jpg",
"57/572c100c77df8ae297fb6b875a2a90be5402d802181a232257daba9069721ad6.jpg",
"7c/7ccb... | gspo_001__zh_all_first | gspo_001 | zh_all_first | all_first | 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/35/35148e3e76f7fbf7fe6c3a86e2b984dfcdce1ebed9968ce37d93c9d2b4efef19.jpg",
"images/SciDocBench/a5/a5d320faacad1bb14027003b9dc37c78b475247bd6252122ce79611fd7a15b88.jpg",
"images/SciDocBench/14/14d5a0924ac981cf653042e3be06cc0d0ce504f307277739a98fc48bc5c4670a.jpg",
"images/SciDocBench/57/572c1... | ||
257 | 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 | [
"e8/e827d7ab11c5c5bb4a87f69e40b40198f24d3c5c1838d15196f70fcd3bed63e5.jpg",
"ae/ae7743b6e3ef6d2fe44097225b13fb8f73a079a52ea19565418d3cbc406aa3f5.jpg",
"95/95dc2f76bf3d2dda1577762cb132104ea3f785acf046ea74c8a29f90a25ea484.jpg",
"80/8012b2c1915396c72e32335b51f182619ff1ea21fa48f1b924e4ff9ff450b723.jpg",
"49/4982... | spatial-ssrl_001__zh_all_first | spatial-ssrl_001 | zh_all_first | all_first | 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/e8/e827d7ab11c5c5bb4a87f69e40b40198f24d3c5c1838d15196f70fcd3bed63e5.jpg",
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"images/SciDocBench/80/8012b... | ||
258 | 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 | [
"4d/4dc402b42b16ddece764c41f8fb4a75d3e94bb5bccda4429d41ce9a208c79841.jpg",
"8a/8a42354577f0049391ff5c41a92650e3e1a86c65744f8366c56c6232ddd59b8b.jpg",
"ca/ca23f44fcc1e06d7228552919854d916196e60ba30be3765017ef31b17a93527.jpg",
"b8/b8f65ab76203d978e4217de2cbcd8a3ff4c0fa1cb4ee6ac62a2980b682923a7e.jpg",
"02/02d0... | ppo_002__zh_all_first | ppo_002 | zh_all_first | all_first | 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/4d/4dc402b42b16ddece764c41f8fb4a75d3e94bb5bccda4429d41ce9a208c79841.jpg",
"images/SciDocBench/8a/8a42354577f0049391ff5c41a92650e3e1a86c65744f8366c56c6232ddd59b8b.jpg",
"images/SciDocBench/ca/ca23f44fcc1e06d7228552919854d916196e60ba30be3765017ef31b17a93527.jpg",
"images/SciDocBench/b8/b8f65... | |
259 | 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 | [
"3e/3e870f09609114de836353ecf120686affdc80ce4045cce73e89153cbe1b4a3f.jpg",
"68/686f297e4e864ae75e508897de44b0933eaa90b0dbdaf7ff5c14e31eb8fac0dc.jpg",
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"77/779b... | p11b_001__zh_all_first | p11b_001 | zh_all_first | all_first | 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/3e/3e870f09609114de836353ecf120686affdc80ce4045cce73e89153cbe1b4a3f.jpg",
"images/SciDocBench/68/686f297e4e864ae75e508897de44b0933eaa90b0dbdaf7ff5c14e31eb8fac0dc.jpg",
"images/SciDocBench/f1/f1b77dc0e7cea3a6eae3f209a2b1299f1a4107c77382c4af58d7030436d5502a.jpg",
"images/SciDocBench/14/14b01... | |
260 | 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 | [
"d6/d6266e8ae9c584b7399e4eab2bdeb0701732d56df402c0475ae2ff25f7db4e8c.jpg",
"78/78554a76472c16af4b51a6280a6295e59fc013a9d1bc6a4ca164256970a4cdd0.jpg",
"e0/e0c4e402377ed7742ea641ebe4ddcb110710e110bc94ce238fbc0e2bb5b75dab.jpg",
"b1/b1792c9dc3ee67f34f2f777be720a1de187b8752106e09d214513909a0750a35.jpg",
"1c/1c4b... | environment3_001__zh_all_first | environment3_001 | zh_all_first | all_first | 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/d6/d6266e8ae9c584b7399e4eab2bdeb0701732d56df402c0475ae2ff25f7db4e8c.jpg",
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"images/SciDocBench/b1/b1792... | ||
261 | 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 | [
"41/414626c0f3ab8b80064afaf12c99bae6d146fd6fab2b68a2b4305b87b6af2042.jpg",
"81/81b9dad57a4c7e3c632d0835dd10cb4558b378a0d0d249b5df631df6518877e5.jpg",
"a9/a9681eb57ca7dd3e746316e96ee2dee21574dfc51b840f6c73e2636162a03db4.jpg",
"d2/d29092a04a2391e0e4e28e4b376de5e0fa8f27313343e862dec3e9588c1f4501.jpg",
"9f/9fc3... | ProtFlowArticle_001__zh_all_first | ProtFlowArticle_001 | zh_all_first | all_first | 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/41/414626c0f3ab8b80064afaf12c99bae6d146fd6fab2b68a2b4305b87b6af2042.jpg",
"images/SciDocBench/81/81b9dad57a4c7e3c632d0835dd10cb4558b378a0d0d249b5df631df6518877e5.jpg",
"images/SciDocBench/a9/a9681eb57ca7dd3e746316e96ee2dee21574dfc51b840f6c73e2636162a03db4.jpg",
"images/SciDocBench/d2/d2909... | ||
262 | 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 | [
"77/77ff951f0a2ac8a40d31bda318095d5c38d3626e30ba3be5ad9f42fd3728db94.jpg",
"a8/a89ccf10fe609a66257ad18172e3bd74d173341eb65b0247af97e01956c9b377.jpg",
"54/54165fbadb6d931227fca931f535d1ab515589d63a1a0022242f8881951e802f.jpg",
"8d/8dfacb26729f86f333edf0d221c7450fad9af08fe62a32985cea61dd3021f04d.jpg",
"94/9402... | saprot_001__zh_all_first | saprot_001 | zh_all_first | all_first | {"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/77/77ff951f0a2ac8a40d31bda318095d5c38d3626e30ba3be5ad9f42fd3728db94.jpg",
"images/SciDocBench/a8/a89ccf10fe609a66257ad18172e3bd74d173341eb65b0247af97e01956c9b377.jpg",
"images/SciDocBench/54/54165fbadb6d931227fca931f535d1ab515589d63a1a0022242f8881951e802f.jpg",
"images/SciDocBench/8d/8dfac... | |||
263 | 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 | [
"d1/d13a4b43babe9c412d80d746c9ca07b2f2ad32be3d590350388dd2b24ce8460f.jpg",
"f3/f3ff1610f79aeb25a43a99d806ad46ce03fe825912cc5d526f2c0e3b03f3125f.jpg",
"c2/c223224fe72c091a4a7b1f8f83019bc5098949bed7ea09e359db5766a54f4381.jpg",
"07/0740997108b9b26071fefab18fb8ba9606ef01fa1d8714155fe8deea55625881.jpg",
"78/789d... | antiproton-spin_001__zh_all_first | antiproton-spin_001 | zh_all_first | all_first | 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/d1/d13a4b43babe9c412d80d746c9ca07b2f2ad32be3d590350388dd2b24ce8460f.jpg",
"images/SciDocBench/f3/f3ff1610f79aeb25a43a99d806ad46ce03fe825912cc5d526f2c0e3b03f3125f.jpg",
"images/SciDocBench/c2/c223224fe72c091a4a7b1f8f83019bc5098949bed7ea09e359db5766a54f4381.jpg",
"images/SciDocBench/07/07409... | |
264 | 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 | [
"bc/bcaff82c578aaebada23391ae57896b7edea0bcf3029c8d7a8eb65f55b05217d.jpg",
"fa/fa84d3e95d5e91f35a575ec12bc1a3233c01f206a486247e50b327316906a4c0.jpg",
"28/28b50421a366b92cc0d9fa043c6db506e0780e9ef9c2961a5dabbbfd9da42603.jpg",
"87/87844b8b451fc5556a7279f02c50c1acb09f14d3d026efaec2c2d6a83ff3c7f8.jpg",
"af/af75... | micropolar-rb_001__zh_all_first | micropolar-rb_001 | zh_all_first | all_first | 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/bc/bcaff82c578aaebada23391ae57896b7edea0bcf3029c8d7a8eb65f55b05217d.jpg",
"images/SciDocBench/fa/fa84d3e95d5e91f35a575ec12bc1a3233c01f206a486247e50b327316906a4c0.jpg",
"images/SciDocBench/28/28b50421a366b92cc0d9fa043c6db506e0780e9ef9c2961a5dabbbfd9da42603.jpg",
"images/SciDocBench/87/87844... | |
265 | 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 | [
"3e/3ec5ea2d53fb888ecb5e7874da24b2b605da7a2a105f473f69f4770ea11829df.jpg",
"ee/eeb13fc579ea4c988928ea66b9d710592b23a1a81be2d3f7d12fa12b1cfb30ed.jpg",
"bf/bf08a31e4e9293d2dd9a20beed22f2cff5ce415800f06adc1647494fa6b82025.jpg",
"b9/b90a171f3b85c196fe0a10b5b0c041403be23045d210157511fb4a64df84c091.jpg",
"72/7234... | depth-anything3_001__zh_all_first | depth-anything3_001 | zh_all_first | all_first | 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/3e/3ec5ea2d53fb888ecb5e7874da24b2b605da7a2a105f473f69f4770ea11829df.jpg",
"images/SciDocBench/ee/eeb13fc579ea4c988928ea66b9d710592b23a1a81be2d3f7d12fa12b1cfb30ed.jpg",
"images/SciDocBench/bf/bf08a31e4e9293d2dd9a20beed22f2cff5ce415800f06adc1647494fa6b82025.jpg",
"images/SciDocBench/b9/b90a1... | ||
266 | 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 | [
"3e/3ec5ea2d53fb888ecb5e7874da24b2b605da7a2a105f473f69f4770ea11829df.jpg",
"ee/eeb13fc579ea4c988928ea66b9d710592b23a1a81be2d3f7d12fa12b1cfb30ed.jpg",
"bf/bf08a31e4e9293d2dd9a20beed22f2cff5ce415800f06adc1647494fa6b82025.jpg",
"b9/b90a171f3b85c196fe0a10b5b0c041403be23045d210157511fb4a64df84c091.jpg",
"72/7234... | depth-anything3_002__zh_all_first | depth-anything3_002 | zh_all_first | all_first | 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/3e/3ec5ea2d53fb888ecb5e7874da24b2b605da7a2a105f473f69f4770ea11829df.jpg",
"images/SciDocBench/ee/eeb13fc579ea4c988928ea66b9d710592b23a1a81be2d3f7d12fa12b1cfb30ed.jpg",
"images/SciDocBench/bf/bf08a31e4e9293d2dd9a20beed22f2cff5ce415800f06adc1647494fa6b82025.jpg",
"images/SciDocBench/b9/b90a1... | ||
267 | 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 | [
"ba/ba9172249b94313687d08c27029d6e35ab2d5a4e99182b55d5606dd8ec48860f.jpg",
"99/99eba5fb823055b873d1788a01b0d9f6485ec0cf0b3ebaab74d133f16b113d85.jpg",
"52/52d8b829ba098653a206f96775a2e0828acdd6ff7cbb18cc0915dd1996885753.jpg",
"6b/6b1e4d3fa2ceb6520bc06ee8c830bcde1081fc5f14f758db38fcd64ae691d979.jpg",
"4d/4d3c... | spa3r_001__zh_all_first | spa3r_001 | zh_all_first | all_first | 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/ba/ba9172249b94313687d08c27029d6e35ab2d5a4e99182b55d5606dd8ec48860f.jpg",
"images/SciDocBench/99/99eba5fb823055b873d1788a01b0d9f6485ec0cf0b3ebaab74d133f16b113d85.jpg",
"images/SciDocBench/52/52d8b829ba098653a206f96775a2e0828acdd6ff7cbb18cc0915dd1996885753.jpg",
"images/SciDocBench/6b/6b1e4... | ||
268 | 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 | [
"de/de6acb9a3f9a4cea3375adeddced89fdacf74e18d1db5717695b3314fd36a72b.jpg",
"13/1385c6a246795bd9395dbafd230c7c2e148681cb2c633e1cb9157affd1ae13fc.jpg",
"6f/6ff892681a86f47df62fbf288547c3de847255901ec451f7b838c0acf18f33be.jpg",
"eb/eb8dc6f9387258bb6cfbf7a08a6bdf64b3e349395c27462f0bf715977fc3dc00.jpg",
"e9/e982... | mmifengine_002__zh_all_first | mmifengine_002 | zh_all_first | all_first | 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/de/de6acb9a3f9a4cea3375adeddced89fdacf74e18d1db5717695b3314fd36a72b.jpg",
"images/SciDocBench/13/1385c6a246795bd9395dbafd230c7c2e148681cb2c633e1cb9157affd1ae13fc.jpg",
"images/SciDocBench/6f/6ff892681a86f47df62fbf288547c3de847255901ec451f7b838c0acf18f33be.jpg",
"images/SciDocBench/eb/eb8dc... | ||
269 | 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 | [
"80/802636f63db827590e364d8419441e73933045a973417436a40e9eb4250601df.jpg",
"76/7654cd9e48bb47d36d5c1a92568fa0630385bedcb4c659fa9849ac69eb4e47c9.jpg",
"f7/f7a52cd7c72330a6b487082dbc45cecb71cb547bfc6705d87531dfb577bfce18.jpg",
"39/394ceb825bff651eb08f2ca091404b994537e860cdf917741b593da900979611.jpg",
"9c/9cb4... | chartassistant_001__zh_all_first | chartassistant_001 | zh_all_first | all_first | {"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 | [
"images/SciDocBench/80/802636f63db827590e364d8419441e73933045a973417436a40e9eb4250601df.jpg",
"images/SciDocBench/76/7654cd9e48bb47d36d5c1a92568fa0630385bedcb4c659fa9849ac69eb4e47c9.jpg",
"images/SciDocBench/f7/f7a52cd7c72330a6b487082dbc45cecb71cb547bfc6705d87531dfb577bfce18.jpg",
"images/SciDocBench/39/394ce... | |||
270 | 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 | [
"68/68178d5eede343a785c4ff6e78b93bcd2694112db4899c25da55cd0fdca4801a.jpg",
"3a/3a8f2a6472d752a20b51c24f3349dcc24b21d225717eb9edd504553f8c744df2.jpg",
"81/815fe4cf75639a2a3498ed3739ca5d2aeeb7f6e57806e3a91b6fd9338068280b.jpg",
"fd/fdbd25e79973cc8d3fee4ca657983746eab64328b5d5f704868586e7b2d132b5.jpg",
"87/871a... | hptransfer_001__zh_all_first | hptransfer_001 | zh_all_first | all_first | 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 | [
"images/SciDocBench/68/68178d5eede343a785c4ff6e78b93bcd2694112db4899c25da55cd0fdca4801a.jpg",
"images/SciDocBench/3a/3a8f2a6472d752a20b51c24f3349dcc24b21d225717eb9edd504553f8c744df2.jpg",
"images/SciDocBench/81/815fe4cf75639a2a3498ed3739ca5d2aeeb7f6e57806e3a91b6fd9338068280b.jpg",
"images/SciDocBench/fd/fdbd2... | ||
271 | 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 | [
"65/6594a917e8eabc3c3cb6fc51dbe4c0d5be7e0699225dd589ddd9a130f955afb0.jpg",
"b5/b5ac30b7eb69b07dc2ea8ccea9f9a19227e95ec78dafc86cc67c7b4ea7c06643.jpg",
"58/58a0a4e2adb6ed38561d4f5cfe4565917971ca1bf8cef297072b148400441dc0.jpg",
"68/684acc90564811f027f6f7f80500e82d8a10b690d8a3403619e7cd78a7538c07.jpg",
"63/633f... | sle-glyco_001__zh_all_first | sle-glyco_001 | zh_all_first | all_first | 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 | [
"images/SciDocBench/65/6594a917e8eabc3c3cb6fc51dbe4c0d5be7e0699225dd589ddd9a130f955afb0.jpg",
"images/SciDocBench/b5/b5ac30b7eb69b07dc2ea8ccea9f9a19227e95ec78dafc86cc67c7b4ea7c06643.jpg",
"images/SciDocBench/58/58a0a4e2adb6ed38561d4f5cfe4565917971ca1bf8cef297072b148400441dc0.jpg",
"images/SciDocBench/68/684ac... | |
272 | 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 | [
"2f/2f8e0055ddb11fc17d90c2709699033d3b67201d57df146ffcc66bdce9f22d3f.jpg",
"34/34a798dfe36525584f521496e9f236a30667c701cae1befcfa998cf1acbef857.jpg",
"0c/0c10a68f5d9a73bf38d8856e18a09124711be0e84168306c61eb20e499dce972.jpg",
"79/79c114680af741d6938ad9063bbf1a9725e86b244b695a82f260375de82a9555.jpg",
"30/30f6... | llm-clinical-review_001__zh_all_first | llm-clinical-review_001 | zh_all_first | all_first | 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 | [
"images/SciDocBench/2f/2f8e0055ddb11fc17d90c2709699033d3b67201d57df146ffcc66bdce9f22d3f.jpg",
"images/SciDocBench/34/34a798dfe36525584f521496e9f236a30667c701cae1befcfa998cf1acbef857.jpg",
"images/SciDocBench/0c/0c10a68f5d9a73bf38d8856e18a09124711be0e84168306c61eb20e499dce972.jpg",
"images/SciDocBench/79/79c11... | |
273 | 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 | [
"40/4090fd3c757c8b40d5b97b07ddf50668cb99cb4d1a90be805e61c3eb493afe37.jpg",
"60/60c9fb6502eb208b2ff3b295760980def938f6a9797417b582f7bd33b1f0de76.jpg",
"59/59cfbceb5e5177aec7aa32dc91ca084348e70ce14f6c001e518632bf7e8b446c.jpg",
"fd/fd6c323139182d83324864190336a68de540ed7081394eadc551c024ba66d123.jpg",
"73/7392... | invivo-cart_001__zh_all_first | invivo-cart_001 | zh_all_first | all_first | 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 | [
"images/SciDocBench/40/4090fd3c757c8b40d5b97b07ddf50668cb99cb4d1a90be805e61c3eb493afe37.jpg",
"images/SciDocBench/60/60c9fb6502eb208b2ff3b295760980def938f6a9797417b582f7bd33b1f0de76.jpg",
"images/SciDocBench/59/59cfbceb5e5177aec7aa32dc91ca084348e70ce14f6c001e518632bf7e8b446c.jpg",
"images/SciDocBench/fd/fd6c3... | |
274 | 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 | [
"ff/ff7c5e50592ab788f3725579aa6ae7f9d1d6c208c91dc0483677d4a404f2768e.jpg",
"1a/1ae7c3d3f2aadaaf7c845da7217b636b4f4286b8a7d4b755a235ec11bfb19acf.jpg",
"6c/6c8744ffbec766758e445eb2b867d91d397c1d99461debf12d88bef45b2398ae.jpg",
"63/63c39271f72c5342158728918328fbcedd0adfc886128e861ef5c00aaab02386.jpg",
"85/85ab... | nonabl-semimetal_001__zh_all_first | nonabl-semimetal_001 | zh_all_first | all_first | 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/ff/ff7c5e50592ab788f3725579aa6ae7f9d1d6c208c91dc0483677d4a404f2768e.jpg",
"images/SciDocBench/1a/1ae7c3d3f2aadaaf7c845da7217b636b4f4286b8a7d4b755a235ec11bfb19acf.jpg",
"images/SciDocBench/6c/6c8744ffbec766758e445eb2b867d91d397c1d99461debf12d88bef45b2398ae.jpg",
"images/SciDocBench/63/63c39... | |
275 | 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 | [
"db/db32808b2e2d81c95d3d56b1e3ecc755c758743ddd741f52ee757885e5a04e09.jpg",
"5e/5e8c83e92cd3e6d0a401fbae4f988aa7e53d349a52516801e2bdeabcac08206a.jpg",
"e0/e0ab5b7ba29162930e3026e77f92b732bb5899013cdfc9223b964b57370bc549.jpg",
"96/963c76d07ebb65549720e97131300b26ed57a5718d04475aa257c7fd6bb90cdc.jpg",
"a4/a457... | poet_001__zh_all_first | poet_001 | zh_all_first | all_first | 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 | [
"images/SciDocBench/db/db32808b2e2d81c95d3d56b1e3ecc755c758743ddd741f52ee757885e5a04e09.jpg",
"images/SciDocBench/5e/5e8c83e92cd3e6d0a401fbae4f988aa7e53d349a52516801e2bdeabcac08206a.jpg",
"images/SciDocBench/e0/e0ab5b7ba29162930e3026e77f92b732bb5899013cdfc9223b964b57370bc549.jpg",
"images/SciDocBench/96/963c7... | |
276 | 你将获得一篇科学 PDF 文档。请仔细检查论文中的所有表格，并判断其中是否存在任何错误或不一致之处。
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 object：
- location: 该问题所属的具体表格，例如 "Table 1", "Table 3"
- section: 该表格内的子部分（如适用），例如 "Baselines & Our Model (3B)", "3B Models"，如果不适用则为 "N/A"
- row: 该子部分中的行号，从 1 开始计数
- column: 该子部分中的列号，从 1 开始计数。计数列时，从左到右扫描每一行；每个连续的非分隔符（非空白、非定界符）字符组计为一列，无... | [{"location": "Table 1", "section": "Baselines & Our Model (3B)", "row": 4, "column": 7, "shown": "+3.17", "expected": "+0.86"}, {"location": "Table 1", "section": "Baselines & Our Model (7B)", "row": 4, "column": 4, "shown": "+1.98", "expected": "+3.14"}, {"location": "Table 1", "section": "Baselines & Our Model (7B)"... | C1 | judge | [
"f6/f668a9231f3ea3c5f7a92ccba28dc1c61b75b7001d4628593f0044e05bd51c90.jpg",
"f5/f52520cbe47a37f8f94e70d47aa4651009015b447ca541f6939e073ada5de8a1.jpg",
"84/84b52a3b406eb8be26e5f5621c9dc0150c276420b02e2d0fe5634d836d110f55.jpg",
"e0/e0e4b088bb1b9c610933484f66bdf48a1055a0d600b3ea427cd0d8739e471088.jpg",
"00/00a0... | spatial_ssrl_modify_001__zh_all_first | spatial_ssrl_modify_001 | zh_all_first | all_first | You are an expert evaluator. You are given a list of ground-truth errors found in a scientific paper's tables, and a model's predicted list of errors.
Ground-truth errors (5 total, each worth 0.2 points):
{answer}
Model prediction:
{prediction}
Scoring rules:
- Award 0.2 for each ground-truth error the model correct... | {"reasoning": "Three arithmetic errors in Table 1 Improvement rows: (1) 3B What'sUp: 86.71-85.85=0.86 not 3.17; (2) 7B 3DSRBench and SpatialEval deltas are swapped (3.14 and 1.66 exchanged for 1.98 and 2.91). Two formatting errors in Table 4 3DSR-MultiObj column: 43.8>43.11 so 43.8 should be bold (best) and 43.11 doubl... | spatial_ssrl_modify | cs | zh | spatial_ssrl_modify_001 | [
"images/SciDocBench/f6/f668a9231f3ea3c5f7a92ccba28dc1c61b75b7001d4628593f0044e05bd51c90.jpg",
"images/SciDocBench/f5/f52520cbe47a37f8f94e70d47aa4651009015b447ca541f6939e073ada5de8a1.jpg",
"images/SciDocBench/84/84b52a3b406eb8be26e5f5621c9dc0150c276420b02e2d0fe5634d836d110f55.jpg",
"images/SciDocBench/e0/e0e4b... | ||
277 | 在我提供的文档中，右侧有一个雷达图。你的任务是在图表中找出一个错误。
图表描述：该图表使用 Python 的 matplotlib package 生成，所有实验数据随后都标注在图表上。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，导致曲线在某个特定 benchmark 上显示在错误的位置。
请按以下步骤操作：
1. 识别图表中的绘图错误。
2. 识别与此错误对应的已标注数值。
3. 按以下精确格式将你的答案作为 JSON dictionary 返回：
{"value": "<The corresponding numerical value>", "description": "<A... | {"value": "33.08"} | C2 | judge | [
"11/1137ad091ea38d5343f1ddd56dbf32cf70d8ba84cb3b86696453b9baede1e1af.jpg"
] | spatial-ssrl-fig1_001__zh_all_first | spatial-ssrl-fig1_001 | zh_all_first | all_first | You are an expert evaluator. You are given a ground-truth answer and a model prediction for a radar chart error detection task.
Ground-truth answer:
{answer}
Model prediction:
{prediction}
Scoring rules:
- Award 1.0 if the model correctly identifies the value "33.08" as the erroneous data point, regardless of whethe... | {"reasoning": "In the radar chart, the Qwen2.5-VL-7B (blue) curve is annotated as 33.08 on the VSI-Bench axis, but Table 1 shows Qwen2.5-VL-7B scores 38.08 on VSI-Bench. The digits 3 and 8 are transposed (33.08 vs 38.08), causing the blue curve to appear incorrectly inward on VSI-Bench while all other annotations on th... | spatial-ssrl-fig1 | cs | zh | spatial-ssrl-fig1_001 | [
"images/SciDocBench/11/1137ad091ea38d5343f1ddd56dbf32cf70d8ba84cb3b86696453b9baede1e1af.jpg"
] | ||
278 | 通读整篇论文。仅使用这篇论文。
只返回一个 JSON 对象，且恰好包含一个键："bits"。
输出要求：
- "bits" 的值必须是一个由 0 和 1 组成的 9 字符字符串。
- 当且仅当该声明被论文完全支持时使用 1；否则使用 0。
- 不要输出任何解释。
- 不要输出 markdown。
- 不要输出任何额外键。
声明：
A. 定理 1 给出了一个精确构造，其中 L = log2 k + 1 层且 d = kN(3 + log2 k)。
B. 定理 2 的下界仅针对 F 陈述，并排除了 F^cyc。
C. Algorithm 1 在每个阶段中先更新 theta_KQ，再更新 theta_Psi。
D. 定理 3 假设 k... | {"bits": "101110111"} | C1 | json_match | [
"0b/0b9b6a4636f8ab9a50d004db0d6cdddd5f16d27869a43406ad760e0be2dcb5cc.jpg",
"34/34c7710aaf27a37106854b2236bdfb8668dfd7841bbeee388a54881618fc5a33.jpg",
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"4b/4b8bb88c90456f2a3a8c210b8672205285dbd59bfb3a536e48e757b46eb77562.jpg",
"f7/f7c9... | easy-to-hard-transformer_001__zh_all_first | easy-to-hard-transformer_001 | zh_all_first | all_first | {"reasoning": "A=1: Theorem 1 states L=log2 k+1, d=kN(3+log2 k) exactly. B=0: Theorem 2 covers 'F or F^cyc', not F alone. C=1: Algorithm 1 updates theta_KQ then theta_Psi in each stage. D=1: Theorem 3 explicitly assumes k=2^(L-1). E=1: Theorem 4 proof outline states if W_KQ^(ell')=0 for ell'>=ell then gradients of late... | easy-to-hard-transformer | cs | zh | easy-to-hard-transformer_001 | [
"images/SciDocBench/0b/0b9b6a4636f8ab9a50d004db0d6cdddd5f16d27869a43406ad760e0be2dcb5cc.jpg",
"images/SciDocBench/34/34c7710aaf27a37106854b2236bdfb8668dfd7841bbeee388a54881618fc5a33.jpg",
"images/SciDocBench/1d/1d44523aba0cb5989fecdc4c67642bf13ab7edd7d5a24f8f36c15b5dc9357526.jpg",
"images/SciDocBench/4b/4b8bb... | |||
279 | 通读整篇论文。仅使用这篇论文。不要解释。
仅返回形式为 {"BITS":"<BITSTRING>"} 的 JSON 对象，其中 <BITSTRING> 必须恰好是一个 18 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. Definition 1.1 要求 beta > 1 且 epsilon in (0, 1/16)。
B. 在 Definition 1.1 中，alpha 是使用来自 S_i(epsilon) x S_j(epsilon) 且 i != j 的点来定义的。
C. Theorem 1.2 同时适用于 (SA) 和 (USA)。
D. 在 Theorem 1.2 中，如果 x_i(0) in... | {"BITS": "101101011110010100"} | C1 | json_match | [
"6b/6b9aceeca2cb0d7d2218d234384bd93cd991d73243d141c11e88b96d563f4e4e.jpg",
"30/30f1ab160d701943739554d88c68e37a80338dc73678e18725a4ff5c9ac30c58.jpg",
"f8/f8f747455bbe96e5d9fa7700f470dcc86586027de9a672ae80cf40eea4d1dff1.jpg",
"06/06d0233257361200dd0341bd5a522697bacf7ce989b0ab7a0f6481d74e728a16.jpg",
"25/255e... | self-attn-metastability_001__zh_all_first | self-attn-metastability_001 | zh_all_first | all_first | {"reasoning": "A=1: Def1.1 states beta>1 and epsilon in (0,1/16). B=0: alpha uses S_i(2eps)xS_j(2eps), not S_i(eps). C=1: Thm1.2 covers '(SA) or (USA)'. D=1: Thm1.2 item1 states x_i stays in S_q(2eps) for all t in [0,T2]. E=0: Thm1.2 does not say all merged into single cluster by T2; particles stay near k clusters. F=1... | self-attn-metastability | cs | zh | self-attn-metastability_001 | [
"images/SciDocBench/6b/6b9aceeca2cb0d7d2218d234384bd93cd991d73243d141c11e88b96d563f4e4e.jpg",
"images/SciDocBench/30/30f1ab160d701943739554d88c68e37a80338dc73678e18725a4ff5c9ac30c58.jpg",
"images/SciDocBench/f8/f8f747455bbe96e5d9fa7700f470dcc86586027de9a672ae80cf40eea4d1dff1.jpg",
"images/SciDocBench/06/06d02... | |||
280 | Task: 评估所提供论文中的文本数据与视觉证据之间的一致性。
Reference Objects: Table 1（数据产品摘要）和 Figure 1（星等分布直方图）。
Requirements:
1. Cross-Modal Mapping: 将 Table 1 中的具体行映射到 Figure 1 中其对应的数据曲线/面板。识别至少三个这样的配对。
2. Threshold Validation: 分析 Figure 1 中在 G = 17.65 和 G = 19 处的“sharp transitions”。根据文本说明，解释 Table 1 中哪些数据产品受到这些特定亮度限制的影响。
3. Consistency Check... | {"data_mapping": [{"table_row_name": "BP/RP mean spectra", "figure_panel": "Top", "mapping_logic": "Matches the 'XP spectra' curve in the Top panel. Table 1 lists ~219 million sources, and the figure shows this specific dataset terminating abruptly at the G=17.65 brightness limit."}, {"table_row_name": "APs from mean B... | C2 | judge | [
"15/15a7d67a10d9f0d6beeadcc8c22aa70e0dc4a0b65d6e4928ccbea7b79d2b520e.jpg",
"45/45b48b71f497e8278897ebc15929a8038f6b1fdd1d86ae1a20f57199638e4e6e.jpg",
"b1/b1889004b1a3d6454f3d3ea5e8f6729133556efdf38355daed245e8f8d48f728.jpg",
"bd/bdcf43c746ffdff7901df44b866efaa87385f26c6455167f1eeaeeacc87fb94c.jpg",
"a0/a079... | gaia-dr3_001__zh_all_first | gaia-dr3_001 | zh_all_first | all_first | You are an expert evaluator for scientific document understanding tasks.
Ground-truth answer:
{answer}
Model prediction:
{prediction}
Scoring rubric (total 1.0):
- data_mapping (0.3): Award 0.1 per correct table-row to figure-panel pair (up to 3). A pair is correct if the table row name, figure panel, and mapping lo... | {"reasoning": "data_mapping: BP/RP→Top (XP spectra curve, G<17.65 cutoff visible); APs from BP/RP→Middle (curve extends to G=19); SSO→Bottom (transit counts, not unique objects). threshold_analysis: G=17.65 is BP/RP spectra SNR limit; G=19 is AP estimation limit. fact_verification: FALSE — Table 1 shows BP/RP G<17.65, ... | gaia-dr3 | physics | zh | gaia-dr3_001 | [
"images/SciDocBench/15/15a7d67a10d9f0d6beeadcc8c22aa70e0dc4a0b65d6e4928ccbea7b79d2b520e.jpg",
"images/SciDocBench/45/45b48b71f497e8278897ebc15929a8038f6b1fdd1d86ae1a20f57199638e4e6e.jpg",
"images/SciDocBench/b1/b1889004b1a3d6454f3d3ea5e8f6729133556efdf38355daed245e8f8d48f728.jpg",
"images/SciDocBench/bd/bdcf4... | ||
281 | 根据提供的文档，糖型对 IgG1Gal & IgG3/4Bis 可以有效预测 SLE 患者器官受累的程度。使用该论文的 Figure 4a（随机森林决策区域图），对于一名浓度为 (IgG3/4Bis, IgG1Gal) = (0.0175, 0.175) 的 SLE 患者，随机森林模型的预测分类是什么：LOW 还是 HIGH？
仅输出 JSON：
{"location": "Figure 4a", "answer": "<LOW or HIGH>"} | {"location": "Figure 4a", "answer": "HIGH"} | C3 | json_match | [
"65/6594a917e8eabc3c3cb6fc51dbe4c0d5be7e0699225dd589ddd9a130f955afb0.jpg",
"b5/b5ac30b7eb69b07dc2ea8ccea9f9a19227e95ec78dafc86cc67c7b4ea7c06643.jpg",
"58/58a0a4e2adb6ed38561d4f5cfe4565917971ca1bf8cef297072b148400441dc0.jpg",
"68/684acc90564811f027f6f7f80500e82d8a10b690d8a3403619e7cd78a7538c07.jpg",
"63/633f... | sle-glyco_002__zh_all_first | sle-glyco_002 | zh_all_first | all_first | json | {"reasoning": "Figure 4a shows the RF classification decision boundary for IgG1Gal & IgG3/4Bis. The point (IgG3/4Bis=0.0175, IgG1Gal=0.175) falls in the red region. Per the legend, red = LOW class, blue = HIGH class."} | sle-glyco | biology | zh | sle-glyco_002 | [
"images/SciDocBench/65/6594a917e8eabc3c3cb6fc51dbe4c0d5be7e0699225dd589ddd9a130f955afb0.jpg",
"images/SciDocBench/b5/b5ac30b7eb69b07dc2ea8ccea9f9a19227e95ec78dafc86cc67c7b4ea7c06643.jpg",
"images/SciDocBench/58/58a0a4e2adb6ed38561d4f5cfe4565917971ca1bf8cef297072b148400441dc0.jpg",
"images/SciDocBench/68/684ac... | ||
282 | 现在我有含有高浓度 Cadmium(II) 的工业废水。由于对效率的需求，我大约有 30 分钟来处理废水。我应该使用哪种材料？
请将最终答案放入以下格式：
{"answer": "<Material Name>", "description": "<Explain how you derive this answer.>", "evidence": "<verbatim text from the paper supporting your answer>", "reasoning": "<step-by-step derivation>"} | {"answer": "PCB"} | C3 | judge | [
"d4/d4b4f5a0227fc5031b6bba56e323e155d90b04a6872d6ea9d2cbf8c1e5828a0a.jpg",
"b8/b8199e4d17d35417bb2ef285d11a7b2282a65d9a62604da07b628c5fe81fc369.jpg",
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"ec/ec8c4cfc663d93ef4fdc4bcf94fe3e8688a9996b3903451737b0951215122311.jpg",
"df/dfd4... | pineapple-avocado-hm_001__zh_all_first | pineapple-avocado-hm_001 | zh_all_first | all_first | json | You are an expert evaluator for a scientific document understanding benchmark.
A model was asked to read a paper about heavy metal removal using pineapple crown biochar (PCB) and avocado peel composite hydrogel (APCH), then recommend a material for Cd(II) removal within a 30-minute contact time.
Reference answer:
{ans... | {"reasoning": "Figure 5(a) shows the Cd PSO model kinetics curve for PCB and APCH. At t=30 min, PCB's Cd(II) uptake (~0.32) is higher than APCH's. APCH surpasses PCB only after several hours of contact time. Therefore PCB is optimal for a 30-minute processing window."} | pineapple-avocado-hm | chemistry | zh | pineapple-avocado-hm_001 | [
"images/SciDocBench/d4/d4b4f5a0227fc5031b6bba56e323e155d90b04a6872d6ea9d2cbf8c1e5828a0a.jpg",
"images/SciDocBench/b8/b8199e4d17d35417bb2ef285d11a7b2282a65d9a62604da07b628c5fe81fc369.jpg",
"images/SciDocBench/fa/face3f4cbd7d4460097c33a61b86523a58e5129b4f6fb8af5bceece725a5afaf.jpg",
"images/SciDocBench/ec/ec8c4... | |
283 | altH(4) 是否具有旋转对称性？
请将最终答案按以下格式给出：
{"answer": "<Yes or No>", "description": "<Explain how you derive this answer.>", "evidence": "<verbatim text or theorem from the paper>", "reasoning": "<step-by-step derivation>"} | {"answer": "Yes"} | C3 | judge | [
"a0/a006d0464d8f325867863827e67a3a7f0c129f6f3e8b876e13324625acb89c4d.jpg",
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"98/9807... | euler-stratification_001__zh_all_first | euler-stratification_001 | zh_all_first | all_first | json | You are an expert evaluator for a scientific document understanding benchmark.
A model was asked to examine Figure 5 in a math paper (Euler stratification) and determine whether altH(4) — the alternating hook minors diagram — is rotationally symmetric.
Reference answer:
{answer}
Model prediction:
{prediction}
Score ... | {"reasoning": "Figure 5 (page 28) shows the alternating hook minors altH(4) as a grid diagram. The pattern has 180-degree rotational symmetry about the center: rotating diagonally (top-right ↔ bottom-left) maps the figure onto itself."} | euler-stratification | math | zh | euler-stratification_001 | [
"images/SciDocBench/a0/a006d0464d8f325867863827e67a3a7f0c129f6f3e8b876e13324625acb89c4d.jpg",
"images/SciDocBench/9f/9f574a601c1bbd03aabc2fd0a4b632dc8ef32fb3dc6a3f5e48e4ab1cd53fa52c.jpg",
"images/SciDocBench/a6/a643afac866f13bf825432530ed5cb386be4399fbcb819b7ca154b358c86bb21.jpg",
"images/SciDocBench/d8/d8ca6... | |
284 | 如果将 Figure 6 中的变换应用于 Figure 2 中红线和蓝线交点左上方的 $$2 \times 2$$ 区域，所得图是否仍然是 reduced，并且对应的 permutation 是否被保留？
对于上述每个问题，在最终答案中输出 "Yes" 或 "No"。
请按以下格式将你的答案作为 JSON 字典返回：
{"Question 1": "<Yes or No>", "Question 2": "<Yes or No>"} | {"Question 1": "Yes", "Question 2": "No"} | C3 | json_match | [
"2a/2a2a21c8439588d892675c20823aded5f56b302523055ae67aea4e57a4e148cf.jpg",
"36/361ae383c8bc7933e7f51c8099b0647909cd48b280d737f5964be03abf434091.jpg",
"f7/f7bdf5fffab4a17224fed160626c35e4afeedda7a5044b92dc70eb97bd2636bb.jpg",
"81/814b48d66b96129020dc9e1fe39422ad0a84605b1dfdd18cda79d7f9701f881e.jpg",
"3b/3bbc... | schubert-pipe-dreams_001__zh_all_first | schubert-pipe-dreams_001 | zh_all_first | all_first | json | {"reasoning": "Figure 2 shows a reduced BPD for permutation 2143. The red and blue pipes cross at a cross tile; the 2x2 region at the upper-left of that crossing contains tiles amenable to a type (b) flip from Figure 6 (cross creation/removal). Applying this flip yields a valid reduced BPD (reducedness preserved), but ... | schubert-pipe-dreams | math | zh | schubert-pipe-dreams_001 | [
"images/SciDocBench/2a/2a2a21c8439588d892675c20823aded5f56b302523055ae67aea4e57a4e148cf.jpg",
"images/SciDocBench/36/361ae383c8bc7933e7f51c8099b0647909cd48b280d737f5964be03abf434091.jpg",
"images/SciDocBench/f7/f7bdf5fffab4a17224fed160626c35e4afeedda7a5044b92dc70eb97bd2636bb.jpg",
"images/SciDocBench/81/814b4... | ||
285 | 仔细阅读论文。仅使用这篇论文。不要解释。恰好输出一行，作为有效的 JSON 对象：
{"BITS":"<BITS>","NUMS":<NUMS>,"CODE":"<CODE>"}
规则：
1) BITS 是一个 10 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. 定理 1 给出了一个精确构造，其中 L = log2 k + 1 层且 d = kN(3 + log2 k)。
B. 定理 2 的下界仅针对 F 陈述，并排除了 F^cyc。
C. Algorithm 1 在每个阶段内先更新 theta_KQ，再更新 theta_Psi。
D. 定理 3 假设 k = 2^(L-1)。
E. 定理 4 ... | {"BITS": "1011110111", "NUMS": [1, 2, 3, 2, 2, 2, 5, 16, 5, 4, 8, 5, 100000], "CODE": "ABCDEBCD"} | C3 | json_match | [
"23/234f3aa3f2cdebd5d7835b3b5b68903779b653d2169e013101e35eb04559be90.jpg",
"ca/caaae0f4375f867e7773c7b336619d8b988c18c526350995aa61f6cb0770bf76.jpg",
"0b/0b9b6a4636f8ab9a50d004db0d6cdddd5f16d27869a43406ad760e0be2dcb5cc.jpg",
"34/34c7710aaf27a37106854b2236bdfb8668dfd7841bbeee388a54881618fc5a33.jpg",
"1d/1d44... | easy-to-hard-transformer_002__zh_all_first | easy-to-hard-transformer_002 | zh_all_first | all_first | json | {"reasoning": "BITS: A=1 (Thm1 exact), B=0 (lower bound covers both F and F^cyc), C=1 (Alg1 updates KQ then Psi), D=1 (Thm3 assumes k=2^(L-1)), E=1 (Thm4 proof: zero layers => zero gradients), F=1 (Fig3 mid: k=16,N=5 from caption), G=0 (100000 mixed from {1,2,4}-fold is correct but paper says the same model is tested, ... | easy-to-hard-transformer | cs | zh | easy-to-hard-transformer_002 | [
"images/SciDocBench/23/234f3aa3f2cdebd5d7835b3b5b68903779b653d2169e013101e35eb04559be90.jpg",
"images/SciDocBench/ca/caaae0f4375f867e7773c7b336619d8b988c18c526350995aa61f6cb0770bf76.jpg",
"images/SciDocBench/0b/0b9b6a4636f8ab9a50d004db0d6cdddd5f16d27869a43406ad760e0be2dcb5cc.jpg",
"images/SciDocBench/34/34c77... | ||
286 | 给定 4 个文档的页面图像。
- Document 1 (ppo): Images 1–2
- Document 2 (grpo): Images 3–5
- Document 3 (gspo): Images 6–7
- Document 4 (cispo): Images 8–9
你是一名综述作者，正在用统一记号整合四篇 RLHF 策略优化论文。
=== 统一记号 ===
- q: 输入 prompt/question
- {o_i}_{i=1}^G: 从 π_old(·|q) 采样得到的 G 个 responses 组成的组
- |o_i|: response o_i 的 token 长度
- π_θ: 当前（可训练的）p... | {"PPO_LLM.outer_norm": "1/|o_i|", "PPO_LLM.KL_location": "in_reward", "PPO_LLM.KL_estimator": "log_ratio", "PPO_LLM.IS_ratio_length_norm": "no", "PPO_LLM.IS_weight_sg": "no", "PPO_LLM.needs_value_model": "yes", "PPO_LLM.clip_bound_symmetry": "symmetric", "PPO_LLM.advantage_per_token_uniform": "no", "PPO_LLM.objective_s... | D1 | json_match | [
"8a/8a42354577f0049391ff5c41a92650e3e1a86c65744f8366c56c6232ddd59b8b.jpg",
"ca/ca23f44fcc1e06d7228552919854d916196e60ba30be3765017ef31b17a93527.jpg",
"f9/f90f82634d27987f2ce91d3b6884eee3dcad4db1977f1527b841387d79af68de.jpg",
"fc/fc0b75b97f4cdfa9dda6378898e3c395c9b3845f4e353ba889556c4edb00996d.jpg",
"d8/d822... | rlhf-formula-compare_001__zh_all_first | rlhf-formula-compare_001 | zh_all_first | all_first | json | {"reasoning": "PPO_LLM: 1/|o| norm (Eq.1), KL in reward (Eq.2: r_t -= β log π_θ/π_ref → log_ratio), token IS ρ_{i,t} (not length-normalized), no sg, needs V_ψ via GAE, symmetric clip, GAE gives per-token A_t (not uniform), innermost sum over tokens, single response (no group G). GRPO: (1/G)*(1/|o_i|) norm (Eq.3), KL in... | ["ppo", "grpo", "gspo", "cispo"] | cs | zh | rlhf-formula-compare_001 | [
"images/SciDocBench/8a/8a42354577f0049391ff5c41a92650e3e1a86c65744f8366c56c6232ddd59b8b.jpg",
"images/SciDocBench/ca/ca23f44fcc1e06d7228552919854d916196e60ba30be3765017ef31b17a93527.jpg",
"images/SciDocBench/f9/f90f82634d27987f2ce91d3b6884eee3dcad4db1977f1527b841387d79af68de.jpg",
"images/SciDocBench/fc/fc0b7... | ||
287 | 给定 2 个作为页面图像的文档。
- Document 1 (eagle25): Image 1
- Document 2 (mammoth-vl): Image 2
Document 1 介绍 Eagle 2.5，Document 2 介绍 MAmmoTH-VL。
从 Eagle 2.5 论文的表 1 中，提取用作训练数据来源的数据集名称完整列表。从 MAmmoTH-VL 论文的图 3 中，提取 MAmmoTH-VL-Instruct (12M) 集合中的数据集名称完整列表。
仅使用精确的不区分大小写的名称匹配来计算这两个列表的交集——不使用缩写或替代拼写。
将结果作为按不区分大小写的字母顺序排序的 \begin{itemi... | "\\documentclass[border=8pt]{standalone}\n\\usepackage{enumitem}\n\\begin{document}\n\\begin{minipage}{10cm}\n\\textbf{Intersection of Eagle-2.5 and MAmmoTH-VL-Instruct (12M) source datasets}\n\\begin{itemize}\n \\item DocVQA\n \\item L-Video-NeXT-Q\n\\end{itemize}\n\\end{minipage}\n\\end{document}" | D2 | judge | [
"1d/1db33357eaf2b474115e7dcda4f33b8140a48572763c9ad33dcee1aac7a6bb09.jpg",
"1d/1d3be9e85e6221e5f5a1ffea86b3d0dcca28d266678f7d0c3e6650953d087d2f.jpg"
] | eagle25-mammoth-vl_001__zh_all_first | eagle25-mammoth-vl_001 | zh_all_first | all_first | text | You are an expert evaluator for a cross-document dataset comparison benchmark.
A model was asked to compute the intersection of dataset names between Eagle 2.5 (Table 1) and MAmmoTH-VL-Instruct 12M (Figure 3), then output a LaTeX itemize list.
Reference answer:
{answer}
Model prediction:
{prediction}
Score the predi... | {"reasoning": "Eagle 2.5 Table 1 datasets: Kinetics710, Something-Something-v2, ActivityNet, HACS Segment, COIN, HIREST, FineAction, PortraitMode-400, Ego4D-MQ, Perception-Test, Charade-STA, QVHighlight, Ego4D-NLQ, Didemo, QueryD, MedVidQA, Youcook2, FineVideo, EgoExoLearn, ActivityNet-RTL, TVQA, CLEVRER, L-Video-NeXT-... | ["eagle25", "mammoth-vl"] | cs | zh | eagle25-mammoth-vl_001 | [
"images/SciDocBench/1d/1db33357eaf2b474115e7dcda4f33b8140a48572763c9ad33dcee1aac7a6bb09.jpg",
"images/SciDocBench/1d/1d3be9e85e6221e5f5a1ffea86b3d0dcca28d266678f7d0c3e6650953d087d2f.jpg"
] | |
288 | 你会看到 3 份文档的页面图像。
- Document 1 (cambrian1) 介绍 Cambrian-1 视觉指令调优论文。
- Document 2 (llavaov) 介绍 LLaVA-OneVision。
- Document 3 (mammoth-vl) 介绍 MAmmoTH-VL。
按顺序列出这三篇论文分别引入的官方模型/方法名称。
仅输出 JSON：
{"document_1_name": "<name>", "document_2_name": "<name>", "document_3_name": "<name>"} | {"document_1_name": "Cambrian-1", "document_2_name": "LLaVA-OneVision", "document_3_name": "MAmmoTH-VL"} | D2 | json_match | [
"23/238f6d5d3156a5780dc2f463a50a8a9595242fce8d3ca1ac013091b0c5619353.jpg",
"9d/9d154dd20a146167625e33c22de5cf91b5886c0ec938ed60f630f7c83f09a1f5.jpg",
"7d/7dd0f2687cb0ccb9e628771f234b697a0139dee7102d8bc391f5c1d50ee142a3.jpg",
"0e/0ef7b0a0ed6a2dc730d0649800cd4919af1ebf1b531b028e78048959195a2b8c.jpg",
"ea/ead2... | cambrian-llavaov-mammoth_001__zh_all_first | cambrian-llavaov-mammoth_001 | zh_all_first | all_first | json | {"reasoning": "Cambrian-1 Figure 9 General (yellow/orange): HatefulMemes, ALLaVA, OODVQA, Q-Instruct, SketchyVQA, DocVQA, LNQA, LVIS-Instruct4V, VisualGenome, VQAv2, GQA, A-OKVQA, TextCaps, LLaVA150K, ShareGPT, OCRVQA, LLAVAR, ST-VQA, IconQA, RefCOCO, VizWiz, ChartQA, WTQ. LLaVA-OV Figure 4 General (yellow/orange): Hat... | ["cambrian1", "llavaov", "mammoth-vl"] | cs | zh | cambrian-llavaov-mammoth_001 | [
"images/SciDocBench/23/238f6d5d3156a5780dc2f463a50a8a9595242fce8d3ca1ac013091b0c5619353.jpg",
"images/SciDocBench/9d/9d154dd20a146167625e33c22de5cf91b5886c0ec938ed60f630f7c83f09a1f5.jpg",
"images/SciDocBench/7d/7dd0f2687cb0ccb9e628771f234b697a0139dee7102d8bc391f5c1d50ee142a3.jpg",
"images/SciDocBench/0e/0ef7b... | ||
289 | 你将获得 2 个文档，形式为页面图像。
- Document 1 (spatial-dreamer): 图像 1–17
- Document 2 (spatial-ladder): 图像 18–39
每个文档都介绍了一种改进 Large Vision-language Models 空间推理能力的方法。你的任务是总结四个模型（Qwen2.5-VL-3B, Qwen2.5-VL-7B, SpatialLadder-3B, SpatialDreamer）在 VSI-Bench 所有子任务上的性能，并以 LaTeX 格式表格输出。
严格遵循以下规则：
总结规则：
- 如果某个模型在两个文档中都在所有 VSI-Bench 子任务上进行了... | {"Qwen2.5-VL-3B": {"Obj_Cnt": 32.9, "Obj_Size": 17.3, "Room_Size": 31.5, "Abs_Dist": 22.1, "Rel_Dist": 32.8, "Appr_Order": 28.5, "Route_Plan": 26.3, "Rel_Dir": 44.2, "Avg": 29.4}, "Qwen2.5-VL-7B": {"Obj_Cnt": 42.2, "Obj_Size": 46.0, "Room_Size": 25.9, "Abs_Dist": 15.0, "Rel_Dist": 37.5, "Appr_Order": 31.8, "Route_Plan"... | D3 | judge | [
"df/df1c5fbc30f4b80a1ca6999cc7df990049a58da3c83147eb19555b4c9398aab4.jpg",
"46/469e4e79aeb3904fd1c85de14cbeb99bb92e41f0662795d2dd198d951e8f3fb0.jpg",
"48/4845a85214865d291b1eca8397da49b115a7ee6284b16c94b92ef9e04570e578.jpg",
"5b/5b9e58cb83967dc0f811c8b9a9d351d43ac82d0dfb9e1337916bbb25d63b97c3.jpg",
"1c/1cce... | spatial-dreamer-ladder_001__zh_all_first | spatial-dreamer-ladder_001 | zh_all_first | all_first | json | You are an expert evaluator for a cross-document table extraction benchmark.
The task required the model to:
1. Read VSI-Bench results from two papers (SpatialDreamer and SpatialLadder)
2. Average Qwen2.5-VL-7B values across both papers (it appears in both)
3. Report single-paper values for Qwen2.5-VL-3B, SpatialLadde... | {"reasoning": "Cross-document D3 task: consolidate VSI-Bench results from SpatialDreamer (Table 1) and SpatialLadder (Table 3) into one LaTeX table with 4 models × 8 subtasks + Avg. Derivation: Qwen2.5-VL-3B and Qwen2.5-VL-7B baselines appear in both papers — SpatialLadder Table 3 provides the 8-subtask breakdown (Obj_... | ["spatial-dreamer", "spatial-ladder"] | cs | zh | spatial-dreamer-ladder_001 | [
"images/SciDocBench/df/df1c5fbc30f4b80a1ca6999cc7df990049a58da3c83147eb19555b4c9398aab4.jpg",
"images/SciDocBench/46/469e4e79aeb3904fd1c85de14cbeb99bb92e41f0662795d2dd198d951e8f3fb0.jpg",
"images/SciDocBench/48/4845a85214865d291b1eca8397da49b115a7ee6284b16c94b92ef9e04570e578.jpg",
"images/SciDocBench/5b/5b9e5... | |
290 | 你将获得 2 份文档的页面图像。
- 文档 1 (ts-llava)：图像 1–15
- 文档 2 (pllava)：图像 16–32
文档 1 介绍 TS-LLaVA，文档 2 介绍 PLLaVA。每份文档都包含一个报告模型在 MVBench 上性能的表格。你的任务是将相关结果整合为一个统一的 LaTeX 格式表格。严格遵循以下规则：
总结规则：
- 包含在两份文档中找到的所有 TS-LLaVA 和 PLLaVA 模型变体。
- 包含任何在两份文档中名称、LLM Size 和 Vision Encoder 完全相同的其他模型。
- 如果某个模型在两份文档中的分数不同，则对每个子任务和总体平均值报告两者的平均值，四舍五入到一位小数... | {"row_order": "TS-LLaVA-7B, TS-LLaVA-34B, PLLaVA-7B, PLLaVA-13B, PLLaVA-34B, VideoChat-7B, VideoChat2-7B", "excluded_models": "Video-LLaMA, LLaMA-Adapter, Video-ChatGPT, ST-LLM, GPT-4V appear in only one paper and are not TS-LLaVA/PLLaVA variants — must NOT be included", "pllava_column_remapping": "PLLaVA paper column ... | D3 | judge | [
"b8/b81764e45501a6f11e1f9c1148a050572c6b119a64c6d782adb0cad0cce5b5e4.jpg",
"7b/7b281163dcd2607cfb437f121f8b463550be2dbad3cf17ad963f9866a5d00eef.jpg",
"a1/a14f2f3ac5d195d3ec8f01806af403f102b1a2aebe4eb3ff37dc22f2180e4997.jpg",
"55/554f2ab29353015fc040aae0ee8661193ba59fee9b0bddeb8061f21a31368417.jpg",
"1d/1d6f... | ts-llava-pllava_001__zh_all_first | ts-llava-pllava_001 | zh_all_first | all_first | text | You are an expert evaluator for a cross-document table consolidation benchmark.
The task required the model to:
1. Extract MVBench results from TS-LLaVA (Table 5a, page 7) and PLLaVA (Table 3, page 11)
2. Include TS-LLaVA variants, PLLaVA variants, and shared models only
3. Remap PLLaVA's different column order to the... | {"reasoning": "TS-LLaVA Table 5a (page 7) uses column order AA,AC,AL,AP,AS,CO,CI,EN,ER,FA,FP,MA,MC,MD,OE,OI,OS,ST,SC,UA. PLLaVA Table 3 (page 11) uses column order AS,AP,AA,FA,UA,OE,OI,OS,MD,AL,ST,AC,MC,MA,SC,FP,CO,EN,ER,CI. VideoChat identical in both (35.5 avg). VideoChat2: TS has 60.4, PLLaVA has 51.1 — must average... | ["ts-llava", "pllava"] | cs | zh | ts-llava-pllava_001 | [
"images/SciDocBench/b8/b81764e45501a6f11e1f9c1148a050572c6b119a64c6d782adb0cad0cce5b5e4.jpg",
"images/SciDocBench/7b/7b281163dcd2607cfb437f121f8b463550be2dbad3cf17ad963f9866a5d00eef.jpg",
"images/SciDocBench/a1/a14f2f3ac5d195d3ec8f01806af403f102b1a2aebe4eb3ff37dc22f2180e4997.jpg",
"images/SciDocBench/55/554f2... | |
291 | 查看所提供论文的 Figure 3，其中比较了两条训练奖励曲线。识别被比较的两种方法以及每条曲线的颜色。
仅输出 JSON：
{"method_1": "<method name>", "method_1_color": "<color>", "method_2": "<method name>", "method_2_color": "<color>"}
“purple” 或 “violet” 可互换使用；“orange” 同理。 | {"method_1": "GRPO w/ Routing Replay", "method_1_color": "purple", "method_2": "GRPO w/o Routing Replay", "method_2_color": "orange"} | E1 | json_match | [
"35/35148e3e76f7fbf7fe6c3a86e2b984dfcdce1ebed9968ce37d93c9d2b4efef19.jpg",
"a5/a5d320faacad1bb14027003b9dc37c78b475247bd6252122ce79611fd7a15b88.jpg",
"14/14d5a0924ac981cf653042e3be06cc0d0ce504f307277739a98fc48bc5c4670a.jpg",
"57/572c100c77df8ae297fb6b875a2a90be5402d802181a232257daba9069721ad6.jpg",
"7c/7ccb... | gspo_002__zh_all_first | gspo_002 | zh_all_first | all_first | json | {"reasoning": "Figure 3 page 6 of GSPO paper. Purple=GRPO w/ Routing Replay stays roughly 0.35-0.55 with noise. Orange=GRPO w/o Routing Replay starts ~0.40 then declines toward 0.25-0.30. Both curves are noisy training curves over training compute steps."} | gspo | cs | zh | gspo_002 | [
"images/SciDocBench/35/35148e3e76f7fbf7fe6c3a86e2b984dfcdce1ebed9968ce37d93c9d2b4efef19.jpg",
"images/SciDocBench/a5/a5d320faacad1bb14027003b9dc37c78b475247bd6252122ce79611fd7a15b88.jpg",
"images/SciDocBench/14/14d5a0924ac981cf653042e3be06cc0d0ce504f307277739a98fc48bc5c4670a.jpg",
"images/SciDocBench/57/572c1... | ||
292 | 你是一名专家级数据可视化工程师和 Python 开发者（Matplotlib/Seaborn 专家）。
目标：你的目标是将所提供的科学图表图像逆向工程为一个可执行的 Python 脚本。
黄金标准：
1. 数据保真度：你代码中的数据点必须尽可能接近图像中的像素。
2. 视觉保真度：生成的图在以下方面必须与原始图像看起来完全一致：
- 坐标轴标签（包括 LaTeX 数学方程）
- 图例位置和文本
- 线型（虚线、实线、点线）、标记（圆形、三角形）和颜色
- 坐标轴范围和刻度（对数 vs. 线性）
- 网格线和背景样式
输出：一个单独的、自包含的 Python 代码块。
输入：所提供论文的图 5。 | "Figure 5: Scaling Curve Across Methods on MMStar (Log Scale). Three curves plotted on log-scale x-axis. Best-of-N Search (green dashed, circles): 4 points, y=[57.6, 58.7, 60.5, 60.1], plateaus after 10000s. Stage-wise Beam Search (blue dashed, circles): 5 points, y=[57.6, 58.3, 60.8, 61.0, 61.2], only 58.3 and 61.2 ar... | E1 | judge | [
"a7/a7f9a113f47dad25ab171a75e5cd2c60f9985127236c6dcd494a7fc7bb0c5bd5.jpg",
"01/0129a4771975d1107ba796a304c2cbb20b3bca8f6c3f685a005027d3b33221fc.jpg",
"37/37bdf5b9ba2c3fa5c79a51bd4eb144f1ecd2071f4b56ccb7e05058b720fecf50.jpg",
"85/851d26c2d38025fc3c8407fec32c19ca2ef146fe530045b68ee7a3c6e45019ba.jpg",
"dd/dd99... | llavacot_002__zh_all_first | llavacot_002 | zh_all_first | all_first | text | You are evaluating a Python Matplotlib script that attempts to reproduce Figure 5 from the LLaVA-CoT paper.
Ground truth data:
- Best-of-N Search (green dashed, circle markers): 4 data points, y-values = [57.6, 58.7, 60.5, 60.1] (plateaus at the end)
- Stage-wise Beam Search (blue dashed, circle markers): 5 data point... | {"reasoning": "Figure 5 page 7 of LLaVA-CoT. GT data: Best-of-N=[57.6,58.7,60.5,60.1] (4pts), Beam=[57.6,58.3,60.8,61.0,61.2] (5pts, only 58.3/61.2 annotated in figure), Retracing=[60.8,61.0,62.3] (3pts, starts later on x-axis). Key traps: colors (red/blue/green), Retracing solid vs others dashed, Retracing starts at h... | llavacot | cs | zh | llavacot_002 | [
"images/SciDocBench/a7/a7f9a113f47dad25ab171a75e5cd2c60f9985127236c6dcd494a7fc7bb0c5bd5.jpg",
"images/SciDocBench/01/0129a4771975d1107ba796a304c2cbb20b3bca8f6c3f685a005027d3b33221fc.jpg",
"images/SciDocBench/37/37bdf5b9ba2c3fa5c79a51bd4eb144f1ecd2071f4b56ccb7e05058b720fecf50.jpg",
"images/SciDocBench/85/851d2... | |
293 | 你会得到一篇研究论文（PDF）。在第 3.2 节中，该论文使用流程图（Figure 1）描述了一个图像预处理流水线。请将该流水线实现为一个 Python 脚本。
约束：
- 允许使用的库：仅 PIL (Pillow)。不要使用 OpenCV、numpy 或任何其他库。
- 你的脚本必须接受一个输入图像路径和一个输出图像路径作为命令行参数。
所需接口：
Usage: python solution.py input.png output.png
仔细阅读 Figure 1，并严格按照所示顺序实现其中显示的操作。
在代码之前，包括：
{"evidence": "<describe each operation you see... | {"input_path": "pdfs/constraintfollow/image.png", "reference_script": "import sys\nfrom PIL import Image\n\nimg = Image.open(sys.argv[1])\nw, h = img.size\nleft = (w - 240) // 2\ntop = (h - 200) // 2\nimg = img.crop((left, top, left + 240, top + 200))\nimg = img.convert(\"L\")\nimg = img.transpose(Image.ROTATE_270)\nim... | E2 | exec_match | [
"30/30b3b65d2a5c18a8e097e41fcd7c09649c108afe942037834eee5af839f88c53.jpg",
"06/06de17ebb6debfbf39aa3faec08977e745631f1fa55c1111842282d1464d2ad1.jpg"
] | constraintfollow_001__zh_all_first | constraintfollow_001 | zh_all_first | all_first | text | {"reasoning": "E2 exec_match: implement the Section 3.2 / Figure 1 preprocessing pipeline. Derivation from figure: step 1 — center-crop to 240×200; step 2 — convert to grayscale (L mode); step 3 — rotate 270° (counterclockwise). The reference script: open image → compute center crop box ((w-240)//2, (h-200)//2, ...) → ... | constraintfollow | cs | zh | constraintfollow_001 | [
"images/SciDocBench/30/30b3b65d2a5c18a8e097e41fcd7c09649c108afe942037834eee5af839f88c53.jpg",
"images/SciDocBench/06/06de17ebb6debfbf39aa3faec08977e745631f1fa55c1111842282d1464d2ad1.jpg"
] | ||
294 | 在 MM-IFEngine (mmifengine) 论文中，确定列出该框架中使用的评估方法顺序的章节编号。
仅输出 JSON：
{"section_number": "<e.g. 3.1>"} | {"section_number": "3.1"} | F1 | json_match | [
"de/de6acb9a3f9a4cea3375adeddced89fdacf74e18d1db5717695b3314fd36a72b.jpg",
"13/1385c6a246795bd9395dbafd230c7c2e148681cb2c633e1cb9157affd1ae13fc.jpg",
"6f/6ff892681a86f47df62fbf288547c3de847255901ec451f7b838c0acf18f33be.jpg",
"eb/eb8dc6f9387258bb6cfbf7a08a6bdf64b3e349395c27462f0bf715977fc3dc00.jpg",
"e9/e982... | mmifengine_003__zh_all_first | mmifengine_003 | zh_all_first | all_first | json | {"reasoning": "F1 paper-code alignment: multi-step reasoning chain. Step 1: Pipeline figure (Figure 2, page 3) shows the overall MM-IFEngine method — the first evaluation method in the pipeline is 'Rule-based Evaluation'. Step 2: In the code repo, eval_mmifeval/ directory contains rule-based evaluation scripts; eval_mm... | mmifengine | cs | zh | mmifengine_003 | [
"images/SciDocBench/de/de6acb9a3f9a4cea3375adeddced89fdacf74e18d1db5717695b3314fd36a72b.jpg",
"images/SciDocBench/13/1385c6a246795bd9395dbafd230c7c2e148681cb2c633e1cb9157affd1ae13fc.jpg",
"images/SciDocBench/6f/6ff892681a86f47df62fbf288547c3de847255901ec451f7b838c0acf18f33be.jpg",
"images/SciDocBench/eb/eb8dc... | ||
295 | 你将获得 5 个文档，形式为页面图像。
- 文档 1 (data_sample)：图像 1 包含多个数据样本的截图，这些截图垂直排列。
数一数图像 1 中显示了多少个不同的数据样本（从上到下编号）。
仅输出 JSON：
{"sample_count": <integer>} | {"sample_count": 6} | G1 | json_match | [
"72/7206ca81b1b667caf33c88961838334c31fa61b1cb4cb1884ebd1a1b968b71ab.jpg",
"72/7206ca81b1b667caf33c88961838334c31fa61b1cb4cb1884ebd1a1b968b71ab.jpg",
"13/1385c6a246795bd9395dbafd230c7c2e148681cb2c633e1cb9157affd1ae13fc.jpg",
"72/7206ca81b1b667caf33c88961838334c31fa61b1cb4cb1884ebd1a1b968b71ab.jpg",
"13/1385... | data_sample_001__zh_all_first | data_sample_001 | zh_all_first | all_first | json | {"reasoning": "G1 dataset provenance: match 6 visual samples to source papers. Derivation: Sample 1 shows a video frame sequence with future frame prediction annotations → matches TwiFF (Think With Future Frames) which focuses on dynamic visual reasoning with future frame prediction. Sample 2 shows a complex web UI int... | ["data_sample", "mmifengine", "agentvista", "twiff", "spatial-ssrl"] | cs | zh | data_sample_001 | [
"images/SciDocBench/72/7206ca81b1b667caf33c88961838334c31fa61b1cb4cb1884ebd1a1b968b71ab.jpg",
"images/SciDocBench/72/7206ca81b1b667caf33c88961838334c31fa61b1cb4cb1884ebd1a1b968b71ab.jpg",
"images/SciDocBench/13/1385c6a246795bd9395dbafd230c7c2e148681cb2c633e1cb9157affd1ae13fc.jpg",
"images/SciDocBench/72/7206c... | ||
296 | 你将获得 2 份文档的页面图像。
- Document 1 (poet2): Image 1
- Document 2 (progen2): Images 2–3
下面是从混合数据池中提取的 4 个数据集条目。对于每个条目，请识别：
- Source Paper（ProGen2 或 PoET-2）。
- 数据所来源的 Primary Database。
- 文本中提到的 Key Preprocessing Step（例如，聚类阈值或过滤方法）。
[Dataset Entries]
Entry A: 来自 80 项研究的 1.5B 抗体序列集合，后来以 85% 序列同一性进行聚类以减少冗余。
Entry B: UniRef90 与... | "[{\"entry\": \"Entry A\", \"source_paper\": \"ProGen2: Exploring the Boundaries of Protein Language Models\", \"primary_database\": \"Observed Antibody Space (OAS)\", \"key_preprocessing\": \"Clustered at 85% sequence identity using Linclust\"}, {\"entry\": \"Entry B\", \"source_paper\": \"ProGen2: Exploring the Bound... | G1 | judge | [
"68/68873f38d2a575029d31d50e97c0d8a0cfaac45a15cf34c47c11cee2312b889a.jpg",
"6c/6c71cbec496cb9ce1dc48f8c1f0faaf273a742994e795d7c35520583b167125c.jpg",
"5a/5ac5283de7a8c634e7968a2c67855068764977c0e105e91b997c340f97ec793a.jpg"
] | poet2-progen2_001__zh_all_first | poet2-progen2_001 | zh_all_first | all_first | text | You are evaluating a model's answer to a dataset attribution question.
The correct answer has 4 entries:
Entry A: source_paper=ProGen2, primary_database=OAS (Observed Antibody Space), key_preprocessing=clustered at 85% identity using Linclust
Entry B: source_paper=ProGen2, primary_database=Uniref90+BFD30, key_preproce... | {"reasoning": "G1 dataset provenance: classify 4 entries to PoET-2 or ProGen2. Derivation: Entry A describes 1.5B antibody sequences from 80 studies, clustered at 85% identity using Linclust → ProGen2 Section 2.1 describes using OAS (Observed Antibody Space) with Linclust clustering at 85%. Entry B describes mixing Uni... | ["poet2", "progen2"] | bio | zh | poet2-progen2_001 | [
"images/SciDocBench/68/68873f38d2a575029d31d50e97c0d8a0cfaac45a15cf34c47c11cee2312b889a.jpg",
"images/SciDocBench/6c/6c71cbec496cb9ce1dc48f8c1f0faaf273a742994e795d7c35520583b167125c.jpg",
"images/SciDocBench/5a/5ac5283de7a8c634e7968a2c67855068764977c0e105e91b997c340f97ec793a.jpg"
] | |
297 | 你将获得 2 份文档的页面图像。
- 文档 1 (sle-glyco): 图像 1–9
- 文档 2 (igg-glycans): 图像 10–44
在文档 1 中，有两种特定的 IgG 糖型被确定为与 SLE (Systemic Lupus Erythematosus) 具有强相关性。列出这两种糖型的名称。
仅输出 JSON：
{"glycoforms": ["<glycoform_1>", "<glycoform_2>"]} | {"glycoforms": ["IgG1Gal", "IgG3/4Bis"]} | D3 | json_match | [
"65/6594a917e8eabc3c3cb6fc51dbe4c0d5be7e0699225dd589ddd9a130f955afb0.jpg",
"b5/b5ac30b7eb69b07dc2ea8ccea9f9a19227e95ec78dafc86cc67c7b4ea7c06643.jpg",
"58/58a0a4e2adb6ed38561d4f5cfe4565917971ca1bf8cef297072b148400441dc0.jpg",
"68/684acc90564811f027f6f7f80500e82d8a10b690d8a3403619e7cd78a7538c07.jpg",
"63/633f... | sle-glyco-igg_001__zh_all_first | sle-glyco-igg_001 | zh_all_first | all_first | json | {"reasoning": "doc1 (sle-glyco) identifies fucosylation of anti-dsDNA IgG1 as strongly correlated with SLE. Combined with sialylation requirement, relevant substances are fucosylated+sialylated IgG glycans (FA*S1/S2 with fucose). Parenthesized peaks 16a/16b/17 are uncertain assignments in doc2 Figure 2."} | ["sle-glyco", "igg-glycans"] | biology | zh | sle-glyco-igg_001 | [
"images/SciDocBench/65/6594a917e8eabc3c3cb6fc51dbe4c0d5be7e0699225dd589ddd9a130f955afb0.jpg",
"images/SciDocBench/b5/b5ac30b7eb69b07dc2ea8ccea9f9a19227e95ec78dafc86cc67c7b4ea7c06643.jpg",
"images/SciDocBench/58/58a0a4e2adb6ed38561d4f5cfe4565917971ca1bf8cef297072b148400441dc0.jpg",
"images/SciDocBench/68/684ac... | ||
298 | 我正在阅读所附的关于 Stairway codes 的论文。请仅复现 Figure 3 中的 Weight-8 case（Fig. 3 的底行），作为一个 LaTeX/TikZ 图。
该图应包含由一个 “≃” 符号连接的两个部分：
请确保输出能够作为一个 standalone LaTeX 文档干净地编译。 | "\\documentclass[border=12pt]{standalone}\n\\usepackage{tikz}\n\\usetikzlibrary{arrows.meta, calc}\n\n\\tikzset{\n sp/.style={\n circle,\n fill=pink!50!red!30,\n draw=black!60,\n line width=0.55pt,\n minimum size=7mm,\n inner sep=0pt\n },\n spbig/.style={sp, minimum size=11mm},\n bond/.style={line... | E1 | judge | [
"97/97ed9435076ae48fcace5e10cd48f1dbe18ef2ea7deb5e67882cb1f946043df6.jpg",
"eb/ebaccfde884fb14277d42741ab53f3db7c1ab444fcf65a9fed2f5c14253a2e98.jpg",
"c9/c95838c2a218fe9f45e72c0705ff9e2a894db88c15f736f4423d5f4f6982eb9b.jpg",
"24/245a8b9e3f87a378126d827fcef56e162238aaa65ee7eeff66a822e2866ada07.jpg",
"17/17a2... | stairway_001__zh_all_first | stairway_001 | zh_all_first | all_first | text | You are evaluating a model's LaTeX/TikZ reproduction of the Weight-8 case (bottom row) of Figure 3 from a paper on Stairway codes.
The figure must contain two parts connected by a ≃ symbol:
- LEFT: a single large spider node with 8 directed legs (4 outgoing upward-ish, 4 incoming downward-ish)
- RIGHT: 8 spider nodes ... | {"reasoning": "E1 TikZ reproduction of Figure 3 Weight-8 spider decomposition. Derivation: The bottom row of Figure 3 shows a ZX-calculus tensor network decomposition. Left side: a single large spider node with 8 legs (weight-8). Right side (after ≃): the decomposition into smaller spiders connected by bonds. Reading f... | stairway | cs | zh | stairway_001 | [
"images/SciDocBench/97/97ed9435076ae48fcace5e10cd48f1dbe18ef2ea7deb5e67882cb1f946043df6.jpg",
"images/SciDocBench/eb/ebaccfde884fb14277d42741ab53f3db7c1ab444fcf65a9fed2f5c14253a2e98.jpg",
"images/SciDocBench/c9/c95838c2a218fe9f45e72c0705ff9e2a894db88c15f736f4423d5f4f6982eb9b.jpg",
"images/SciDocBench/24/245a8... | |
299 | 我附上了一篇物理论文。查看 Figure 1(b)，其中显示了一个 3-regular graph。报告该图的节点数 (N) 和正则性 (k)。
Output JSON only:
{"num_nodes": <integer>, "regularity": <integer>} | {"num_nodes": 22, "regularity": 3} | E1 | json_match | [
"14/14eba3e86d9d540a20acab554b51b99edfcb48d8a45a7d708a08a02712900c3b.jpg",
"44/44fdf6e9b5b01a9605667de4c0e53f5f25582e09215863b7a22adef1b40c7b4c.jpg",
"e4/e43c67deab2615bc416921a39a9ad47688473b74ec30eb6f5515c756691dbd24.jpg",
"49/49bd7850f7615ead8d482052f6ef533ecc96b28665f2bac66e19e50cd5832b1e.jpg",
"83/8320... | itwa-spin_001__zh_all_first | itwa-spin_001 | zh_all_first | all_first | json | {"reasoning": "E1 TikZ reproduction of Figure 1(b) 3-regular graph, N=22. Derivation: reading edge connectivity from the figure node by node — each of the 22 nodes connects to exactly 3 others. The adjacency was extracted by careful inspection: node 1 connects to 3, 12, 13; node 2 connects to 4, 9, 10; node 3 connects ... | itwa-spin | physics | zh | itwa-spin_001 | [
"images/SciDocBench/14/14eba3e86d9d540a20acab554b51b99edfcb48d8a45a7d708a08a02712900c3b.jpg",
"images/SciDocBench/44/44fdf6e9b5b01a9605667de4c0e53f5f25582e09215863b7a22adef1b40c7b4c.jpg",
"images/SciDocBench/e4/e43c67deab2615bc416921a39a9ad47688473b74ec30eb6f5515c756691dbd24.jpg",
"images/SciDocBench/49/49bd7... |
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