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0 |
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 representing in JSON format:
{
"Basic_Characteristics": ["QI", "CQCT", "PM"],
"Algorithmic_Characteristics... | {"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... | ||||||
1 | 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 complete LaTeX file with a LaTeX Table code block. You may use the booktabs, amsmath, amsfonts package and other pac... | \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__en_all_first | aug_B1_1_2 | en_all_first | all_first | en | 7 | [
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"images/SciDocBench/91/91e7c... | ||||||
2 | Find the dependency chain of each item in Theorem 7.1, including 7.1(1a), 7.1(1b), 7.1(1c), 7.1(2), and 7.1(3).
Output Requirement: Please return a JSON object where each key represents an item or lemma, and the corresponding value is an array of its direct prerequisites. The format must strictly follow the example bel... | {"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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"images/SciDocBench/dc/dc446... | ||||||
3 | You are a theoretical physics assistant. Read the three provided papers:
- 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
Rewrite the fo... | [{"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 | [
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"images/SciDocBench/92/92dee... | ||||||
4 | You are an AI Bioinformatics research assisstant.Here is the file structure tree for article represented in JSON format::
{
"01_identify_mutations": [
"140gene_fasta_new_8species.R",
"TBXT_8species_03.csv",
"TBXT_new_8species.fasta",
"forloop_python.sh",
"gene140_location.csv",
"mutation.py"
... | {
"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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"images/SciDocBench/15/15837... | ||||||
5 | You are given a scientific PDF document. Your task is to extract and reproduce the body text of the section titled "3. MM-IFEval Benchmark" located on page 3 of the document.
Return your answer as a JSON dictionary in the following exact format:
{"extraction": "<the extracted LaTeX body text>"}
Follow these rules stric... | "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",
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"e9/e982... | mmifengine_001__en_all_first | mmifengine_001 | en_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 | en | mmifengine_001 | [
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"images/SciDocBench/eb/eb8dc... | |
6 | You are given a scientific PDF document. Your task is to locate the specific evidence within the paper that supports the following claim made in the abstract:
"LLaVA-CoT not only outperforms its base model by 9.4% on a wide range of multimodal reasoning benchmarks"
Follow these steps:
1. Identify which figure, table, o... | {"location": "Table 5"} | A2 | json_match | [
"a7/a7f9a113f47dad25ab171a75e5cd2c60f9985127236c6dcd494a7fc7bb0c5bd5.jpg",
"01/0129a4771975d1107ba796a304c2cbb20b3bca8f6c3f685a005027d3b33221fc.jpg",
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"dd/dd99... | llavacot_001__en_all_first | llavacot_001 | en_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 | en | llavacot_001 | [
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"images/SciDocBench/85/851d2... | ||
7 | You are given a scientific PDF document. Your task is to classify the role of each of the following citations as they appear in the paper text:
[Sch+15b], [Mni+16], [Wan+16], [Hee+17], [KL02]
# Citation Roles
- **Background**: General context only; introduces the research landscape, motivates the problem, or names rel... | {"[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__en_all_first | ppo_001 | en_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 | en | ppo_001 | [
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"images/SciDocBench/b8/b8f65... | ||
8 | You are given a scientific PDF document introducing the GSPO (Group Sequence Policy Optimization) algorithm. The paper introduces and uses several mathematical symbols within its numbered equations.
Your task: for each of the ten symbols listed below, identify the equation number in which it **first appears** in the p... | {"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__en_all_first | gspo_001 | en_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 | en | gspo_001 | [
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"images/SciDocBench/57/572c1... | ||
9 | You are given a scientific PDF document describing Spatial-SSRL, a self-supervised reinforcement learning framework for spatial reasoning in vision-language models.
Your task: extract the following ten configuration values from the paper. All values must be taken verbatim from the paper — do not infer or compute.
Ret... | {"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 | [
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"ae/ae7743b6e3ef6d2fe44097225b13fb8f73a079a52ea19565418d3cbc406aa3f5.jpg",
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"49/4982... | spatial-ssrl_001__en_all_first | spatial-ssrl_001 | en_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 | en | spatial-ssrl_001 | [
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10 | You are given a scientific PDF document (PPO: Proximal Policy Optimization Algorithms, Schulman et al. 2017).
The paper contains 12 numbered equations (Eq. 1 through Eq. 12) on pages 2–5. Your task is to construct a Directed Acyclic Graph (DAG) that represents the logical derivation dependencies among these equations.... | {"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 | [
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"02/02d0... | ppo_002__en_all_first | ppo_002 | en_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 | en | ppo_002 | [
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"images/SciDocBench/b8/b8f65... | |
11 | Please carefully examine Figure 2 in the paper I input, and determine: at E = 2 MeV, which of the three curves has the largest cross-section value on the y-axis? Output your answer as a JSON object:
{"answer": "<curve name>"} | "Tentori and Belloni" | A1 | judge | [
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"14/14b01e39768f6d455274813b2146ac3584ab2d0c4e2b0b7c534958cae4a4d5ca.jpg",
"77/779b... | p11b_001__en_all_first | p11b_001 | en_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 | en | p11b_001 | [
"images/SciDocBench/3e/3e870f09609114de836353ecf120686affdc80ce4045cce73e89153cbe1b4a3f.jpg",
"images/SciDocBench/68/686f297e4e864ae75e508897de44b0933eaa90b0dbdaf7ff5c14e31eb8fac0dc.jpg",
"images/SciDocBench/f1/f1b77dc0e7cea3a6eae3f209a2b1299f1a4107c77382c4af58d7030436d5502a.jpg",
"images/SciDocBench/14/14b01... | |
12 | Read this document. There are multiple figures in it where, due to curve fitting, the predicted experimental results after fitting can produce differences even under the same experimental conditions. Which figure's curve did I use to derive the following predicted experimental results?
A. At contact time=20 min, Dose=0... | {"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__en_all_first | environment3_001 | en_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 | en | environment3_001 | [
"images/SciDocBench/d6/d6266e8ae9c584b7399e4eab2bdeb0701732d56df402c0475ae2ff25f7db4e8c.jpg",
"images/SciDocBench/78/78554a76472c16af4b51a6280a6295e59fc013a9d1bc6a4ca164256970a4cdd0.jpg",
"images/SciDocBench/e0/e0c4e402377ed7742ea641ebe4ddcb110710e110bc94ce238fbc0e2bb5b75dab.jpg",
"images/SciDocBench/b1/b1792... | ||
13 | You are a paper reading assistant. In the article *ProtFlowArticle*, for each of the citations below, classify the role(s) it plays in the paper. Use these categories:
1. Background: Provides theoretical support or research context.
2. Method: Adopts or compares against an existing method.
3. Result: Uses others' resul... | {"[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__en_all_first | ProtFlowArticle_001 | en_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 | en | ProtFlowArticle_001 | [
"images/SciDocBench/41/414626c0f3ab8b80064afaf12c99bae6d146fd6fab2b68a2b4305b87b6af2042.jpg",
"images/SciDocBench/81/81b9dad57a4c7e3c632d0835dd10cb4558b378a0d0d249b5df631df6518877e5.jpg",
"images/SciDocBench/a9/a9681eb57ca7dd3e746316e96ee2dee21574dfc51b840f6c73e2636162a03db4.jpg",
"images/SciDocBench/d2/d2909... | ||
14 | Please read this paper, and collate all datasets mentioned in it in the order they first appear.
Output Format:
Return a valid flat JSON object. Number the datasets starting from 1 in order of first appearance, using keys of the form "{N}.location", "{N}.name", "{N}.models":
{
"1.location": "<page_number>-<section_t... | {"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__en_all_first | saprot_001 | en_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 | en | saprot_001 | [
"images/SciDocBench/77/77ff951f0a2ac8a40d31bda318095d5c38d3626e30ba3be5ad9f42fd3728db94.jpg",
"images/SciDocBench/a8/a89ccf10fe609a66257ad18172e3bd74d173341eb65b0247af97e01956c9b377.jpg",
"images/SciDocBench/54/54165fbadb6d931227fca931f535d1ab515589d63a1a0022242f8881951e802f.jpg",
"images/SciDocBench/8d/8dfac... | |||
15 | Extract the specific values, units, and first occurrence locations for the following variables. Output as a JSON array with objects following this exact structure:
[
{
"symbol": <symbol name in $$>,
"value": <value>,
"unit": <unit>,
"trap_type": <PT or AT>,
"location": "Page <page>-Section <secti... | [{"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__en_all_first | antiproton-spin_001 | en_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 | en | antiproton-spin_001 | [
"images/SciDocBench/d1/d13a4b43babe9c412d80d746c9ca07b2f2ad32be3d590350388dd2b24ce8460f.jpg",
"images/SciDocBench/f3/f3ff1610f79aeb25a43a99d806ad46ce03fe825912cc5d526f2c0e3b03f3125f.jpg",
"images/SciDocBench/c2/c223224fe72c091a4a7b1f8f83019bc5098949bed7ea09e359db5766a54f4381.jpg",
"images/SciDocBench/07/07409... | |
16 | You are given a scientific PDF document on the large-time behavior of weak solutions to the d-dimensional micropolar Rayleigh-Bénard problem.
Your task: reproduce equation (A.1) from the paper exactly as LaTeX code.
Return your answer as a JSON dictionary:
{"latex": "<the complete LaTeX code for equation (A.1), inclu... | "\\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__en_all_first | micropolar-rb_001 | en_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 | en | micropolar-rb_001 | [
"images/SciDocBench/bc/bcaff82c578aaebada23391ae57896b7edea0bcf3029c8d7a8eb65f55b05217d.jpg",
"images/SciDocBench/fa/fa84d3e95d5e91f35a575ec12bc1a3233c01f206a486247e50b327316906a4c0.jpg",
"images/SciDocBench/28/28b50421a366b92cc0d9fa043c6db506e0780e9ef9c2961a5dabbbfd9da42603.jpg",
"images/SciDocBench/87/87844... | |
17 | Read section 6.2 of the provided paper, which defines the metrics used to evaluate point-cloud reconstruction. List the names of the metrics defined in this section, in the order they are introduced. Output a JSON list of metric names.
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__en_all_first | depth-anything3_001 | en_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 | en | depth-anything3_001 | [
"images/SciDocBench/3e/3ec5ea2d53fb888ecb5e7874da24b2b605da7a2a105f473f69f4770ea11829df.jpg",
"images/SciDocBench/ee/eeb13fc579ea4c988928ea66b9d710592b23a1a81be2d3f7d12fa12b1cfb30ed.jpg",
"images/SciDocBench/bf/bf08a31e4e9293d2dd9a20beed22f2cff5ce415800f06adc1647494fa6b82025.jpg",
"images/SciDocBench/b9/b90a1... | ||
18 | You are given a scientific PDF document. Your task is to locate evidence that supports the following experimental result: the model performance corresponding to the magenta-colored entry in chart on the left side of the first page.
Follow these steps:
1. Identify the experimental result.
2. Identify which figure, tabl... | {"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__en_all_first | depth-anything3_002 | en_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 | en | depth-anything3_002 | [
"images/SciDocBench/3e/3ec5ea2d53fb888ecb5e7874da24b2b605da7a2a105f473f69f4770ea11829df.jpg",
"images/SciDocBench/ee/eeb13fc579ea4c988928ea66b9d710592b23a1a81be2d3f7d12fa12b1cfb30ed.jpg",
"images/SciDocBench/bf/bf08a31e4e9293d2dd9a20beed22f2cff5ce415800f06adc1647494fa6b82025.jpg",
"images/SciDocBench/b9/b90a1... | ||
19 | You are given a scientific PDF document. Your task is to classify the role of each of the following citations as they appear in the paper text:
[34], [17], [22], [46], [35]
# Citation Roles
- **Background**: General context only; introduces the research landscape, motivates the problem, or names related work. The pape... | {"[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__en_all_first | spa3r_001 | en_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 | en | spa3r_001 | [
"images/SciDocBench/ba/ba9172249b94313687d08c27029d6e35ab2d5a4e99182b55d5606dd8ec48860f.jpg",
"images/SciDocBench/99/99eba5fb823055b873d1788a01b0d9f6485ec0cf0b3ebaab74d133f16b113d85.jpg",
"images/SciDocBench/52/52d8b829ba098653a206f96775a2e0828acdd6ff7cbb18cc0915dd1996885753.jpg",
"images/SciDocBench/6b/6b1e4... | ||
20 | Please read this paper, and collate all datasets mentioned in it in the order they first appear.
Output Format:
Return a valid flat JSON object. Number the datasets starting from 1 in order of first appearance, using keys of the form "{N}.location", "{N}.name", "{N}.models":
{
"1.location": "<page_number>-<section_t... | {"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__en_all_first | mmifengine_002 | en_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 | en | mmifengine_002 | [
"images/SciDocBench/de/de6acb9a3f9a4cea3375adeddced89fdacf74e18d1db5717695b3314fd36a72b.jpg",
"images/SciDocBench/13/1385c6a246795bd9395dbafd230c7c2e148681cb2c633e1cb9157affd1ae13fc.jpg",
"images/SciDocBench/6f/6ff892681a86f47df62fbf288547c3de847255901ec451f7b838c0acf18f33be.jpg",
"images/SciDocBench/eb/eb8dc... | ||
21 | Please read this paper, and collate all source datasets that contribute to ChartSFT, as listed in the column headers of Table 1, in the order they appear from left to right.
Output Format:
Return a valid flat JSON object. Number the sources starting from 1 in left-to-right order, using keys of the form "{N}.location",... | {"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__en_all_first | chartassistant_001 | en_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 | en | chartassistant_001 | [
"images/SciDocBench/80/802636f63db827590e364d8419441e73933045a973417436a40e9eb4250601df.jpg",
"images/SciDocBench/76/7654cd9e48bb47d36d5c1a92568fa0630385bedcb4c659fa9849ac69eb4e47c9.jpg",
"images/SciDocBench/f7/f7a52cd7c72330a6b487082dbc45cecb71cb547bfc6705d87531dfb577bfce18.jpg",
"images/SciDocBench/39/394ce... | |||
22 | You are given a scientific PDF document introducing hyperparameter transfer laws for non-recurrent multi-path neural networks. The paper defines and uses several mathematical symbols within its numbered equations, spanning both the main body (pages 1-10) and the appendix (pages 11+).
Your task: for each of the twenty ... | {"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__en_all_first | hptransfer_001 | en_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 | en | hptransfer_001 | [
"images/SciDocBench/68/68178d5eede343a785c4ff6e78b93bcd2694112db4899c25da55cd0fdca4801a.jpg",
"images/SciDocBench/3a/3a8f2a6472d752a20b51c24f3349dcc24b21d225717eb9edd504553f8c744df2.jpg",
"images/SciDocBench/81/815fe4cf75639a2a3498ed3739ca5d2aeeb7f6e57806e3a91b6fd9338068280b.jpg",
"images/SciDocBench/fd/fdbd2... | ||
23 | You are given a scientific PDF document about using machine learning to classify organ involvement in systemic lupus erythematosus (SLE) based on anti-dsDNA IgG glycosylation profiles.
I am a doctor and if I want to reproduce the machine learning results of this paper with the data of my own patients, what data must I... | {"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__en_all_first | sle-glyco_001 | en_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 | en | sle-glyco_001 | [
"images/SciDocBench/65/6594a917e8eabc3c3cb6fc51dbe4c0d5be7e0699225dd589ddd9a130f955afb0.jpg",
"images/SciDocBench/b5/b5ac30b7eb69b07dc2ea8ccea9f9a19227e95ec78dafc86cc67c7b4ea7c06643.jpg",
"images/SciDocBench/58/58a0a4e2adb6ed38561d4f5cfe4565917971ca1bf8cef297072b148400441dc0.jpg",
"images/SciDocBench/68/684ac... | |
24 | You are given a scientific PDF document about a systematic review of large language models (LLMs) in clinical medicine.
Figure 4 shows the performance of LLMs compared to human experts across different conditions. Some subplots contain markers (*, **, ***) indicating statistically significant differences between speci... | "{\"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__en_all_first | llm-clinical-review_001 | en_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 | en | llm-clinical-review_001 | [
"images/SciDocBench/2f/2f8e0055ddb11fc17d90c2709699033d3b67201d57df146ffcc66bdce9f22d3f.jpg",
"images/SciDocBench/34/34a798dfe36525584f521496e9f236a30667c701cae1befcfa998cf1acbef857.jpg",
"images/SciDocBench/0c/0c10a68f5d9a73bf38d8856e18a09124711be0e84168306c61eb20e499dce972.jpg",
"images/SciDocBench/79/79c11... | |
25 | You are given a scientific PDF document about in vivo site-specific T cell engineering.
Identify all histograms in Extended Data Fig. 3a and 3c. For each individual histogram, sort all categories within that histogram in descending order based on their y-axis heights. You need to output which subplot it belongs to, th... | "{\"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__en_all_first | invivo-cart_001 | en_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 | en | invivo-cart_001 | [
"images/SciDocBench/40/4090fd3c757c8b40d5b97b07ddf50668cb99cb4d1a90be805e61c3eb493afe37.jpg",
"images/SciDocBench/60/60c9fb6502eb208b2ff3b295760980def938f6a9797417b582f7bd33b1f0de76.jpg",
"images/SciDocBench/59/59cfbceb5e5177aec7aa32dc91ca084348e70ce14f6c001e518632bf7e8b446c.jpg",
"images/SciDocBench/fd/fd6c3... |
SciDocBench
Official data release for SciDocBench: A Workflow-Centered Benchmark and Data Pipeline for Scientific Document Understanding. Project and evaluation code: InternLM/SciDocBench.
Dataset Summary
SciDocBench contains 496 evaluation instances derived from 124 expert-authored scientific-document questions. Each question is represented under four matched settings that cross English/Chinese questions with all-images-first/interleaved document representations.
| Partition | Instances |
|---|---|
| EN, all images first | 124 |
| EN, interleaved | 124 |
| ZH, all images first | 124 |
| ZH, interleaved | 124 |
The release references 7,052 images across instances and contains 2,758 unique image files after SHA-256 deduplication.
Files
data/test-00000-of-00001.parquet: Hugging Face test split.SciDocBench.tsv: portable VLMEvalKit-compatible source table.scidocbench.tsv: compatibility alias for the original repository path.images/SciDocBench/: content-addressed document images.release_manifest.json: counts and checksums.manifests/images.jsonl: per-image hashes and reference counts.
In the Parquet file, image_path is a list of paths relative to the VLMEvalKit image
root, while images contains repository-relative paths. Interleaved segments retain
their ordering and refer to the rewritten image_path values.
Usage
Load the table with Hugging Face Datasets:
from datasets import load_dataset
dataset = load_dataset("HenryExcellent/SciDocBench", split="test")
For a local VLMEvalKit checkout, download the repository and place the TSV and image
tree under LMUData:
hf download HenryExcellent/SciDocBench --repo-type dataset --local-dir SciDocBench
cp SciDocBench/SciDocBench.tsv "$LMUData/SciDocBench.tsv"
mkdir -p "$LMUData/images/SciDocBench"
cp -a SciDocBench/images/SciDocBench/. "$LMUData/images/SciDocBench/"
Evaluation
Use the SciDocBench integration in VLMEvalKit for standardized inference and evaluation.
License and Use
Benchmark annotations and scripts are released for research use under the terms stated by the SciDocBench project. Paper pages and figures originate from heterogeneous scientific sources and may retain source-specific copyright or license terms. Users are responsible for checking the applicable terms before redistribution or commercial use. No blanket license is asserted over third-party document imagery.
Citation
@article{wu2026scidocbench,
title = {SciDocBench: A Workflow-Centered Benchmark and Data Pipeline for Scientific Document Understanding},
author = {Wu, Shenxi and Liu, Yuhong and Zhang, Haosong and Zou, Tongjin and Zhang, Yanxun and Chen, Gaochang and Liang, Dun and Wang, Jiaqi and Wang, Zhecan James and Zang, Yuhang and Lin, Dahua},
journal = {arXiv preprint arXiv:2609.05141},
year = {2026}
}
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