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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 | [
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"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",
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"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... | |
26 | You are given a scientific PDF document about non-Abelian topological semimetals and Euler class topology in acoustic metamaterials.
Draw the complete derivation/logic chain connecting the following premises to the two terminal conclusions:
**Starting premises:**
- C₂T or PT symmetry of the system
- Kagome lattice ge... | "{\"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__en_all_first | nonabl-semimetal_001 | en_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 | en | nonabl-semimetal_001 | [
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"images/SciDocBench/6c/6c8744ffbec766758e445eb2b867d91d397c1d99461debf12d88bef45b2398ae.jpg",
"images/SciDocBench/63/63c39... | |
27 | You are given a scientific PDF document about PoET (Protein Evolutionary Transformer).
Output a 2-level Mermaid tree (flowchart TD):
**Leaf Nodes** — one node per major stream of prior work that PoET builds on:
- Line 1: Citation aliases separated by `|`
- Line 2: `CommonBase: ` + shared theoretical foundation (noun/... | "{\"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__en_all_first | poet_001 | en_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 | en | poet_001 | [
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"images/SciDocBench/96/963c7... | |
28 | You are given a scientific PDF document. Please carefully examine all tables in the paper and check whether there are any errors or inconsistencies in them.
For each cell you identify as problematic, output a JSON object with the following fields:
- location: the specific table this issue belongs to, e.g. "Table 1", "T... | [{"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 | [
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"f5/f52520cbe47a37f8f94e70d47aa4651009015b447ca541f6939e073ada5de8a1.jpg",
"84/84b52a3b406eb8be26e5f5621c9dc0150c276420b02e2d0fe5634d836d110f55.jpg",
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"00/00a0... | spatial_ssrl_modify_001__en_all_first | spatial_ssrl_modify_001 | en_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 | en | spatial_ssrl_modify_001 | [
"images/SciDocBench/f6/f668a9231f3ea3c5f7a92ccba28dc1c61b75b7001d4628593f0044e05bd51c90.jpg",
"images/SciDocBench/f5/f52520cbe47a37f8f94e70d47aa4651009015b447ca541f6939e073ada5de8a1.jpg",
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"images/SciDocBench/e0/e0e4b... | ||
29 | In the document I provided, there is a radar chart on the right side. 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; however, dur... | {"value": "33.08"} | C2 | judge | [
"11/1137ad091ea38d5343f1ddd56dbf32cf70d8ba84cb3b86696453b9baede1e1af.jpg"
] | spatial-ssrl-fig1_001__en_all_first | spatial-ssrl-fig1_001 | en_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 | en | spatial-ssrl-fig1_001 | [
"images/SciDocBench/11/1137ad091ea38d5343f1ddd56dbf32cf70d8ba84cb3b86696453b9baede1e1af.jpg"
] | ||
30 | Read the paper end-to-end. Use only this paper.
Return only a JSON object with exactly one key: "bits".
Output requirements:
- The value of "bits" must be a 9-character string consisting only of 0 and 1.
- Use 1 iff the claim is fully supported by the paper; otherwise use 0.
- Do not output any explanation.
- Do not o... | {"bits": "101110111"} | C1 | json_match | [
"0b/0b9b6a4636f8ab9a50d004db0d6cdddd5f16d27869a43406ad760e0be2dcb5cc.jpg",
"34/34c7710aaf27a37106854b2236bdfb8668dfd7841bbeee388a54881618fc5a33.jpg",
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"f7/f7c9... | easy-to-hard-transformer_001__en_all_first | easy-to-hard-transformer_001 | en_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 | en | easy-to-hard-transformer_001 | [
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"images/SciDocBench/4b/4b8bb... | |||
31 | Read the paper end-to-end. Use only this paper. Do not explain.
Return only a JSON object of the form {"BITS":"<BITSTRING>"} where <BITSTRING> is exactly a 18-character bitstring. Use 1 iff the claim is fully supported by the paper; otherwise use 0.
A. Definition 1.1 requires beta > 1 and epsilon in (0, 1/16).
B. In De... | {"BITS": "101101011110010100"} | C1 | json_match | [
"6b/6b9aceeca2cb0d7d2218d234384bd93cd991d73243d141c11e88b96d563f4e4e.jpg",
"30/30f1ab160d701943739554d88c68e37a80338dc73678e18725a4ff5c9ac30c58.jpg",
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"06/06d0233257361200dd0341bd5a522697bacf7ce989b0ab7a0f6481d74e728a16.jpg",
"25/255e... | self-attn-metastability_001__en_all_first | self-attn-metastability_001 | en_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 | en | self-attn-metastability_001 | [
"images/SciDocBench/6b/6b9aceeca2cb0d7d2218d234384bd93cd991d73243d141c11e88b96d563f4e4e.jpg",
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"images/SciDocBench/06/06d02... | |||
32 | Task: Evaluate the consistency between textual data and visual evidence in the provided paper.
Reference Objects: Table 1 (Data product summary) and Figure 1 (Magnitude distribution histograms).
Requirements:
1. Cross-Modal Mapping: Map specific rows from Table 1 to their corresponding data curves/panels in Figure 1. I... | {"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__en_all_first | gaia-dr3_001 | en_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 | en | gaia-dr3_001 | [
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"images/SciDocBench/bd/bdcf4... | ||
33 | According to the provided document, the glycoform pair IgG1Gal & IgG3/4Bis can predict the degree of organ involvement effectively in SLE patients. Using Figure 4a (the random-forest decision-region plot) of the paper, for an SLE patient with concentrations (IgG3/4Bis, IgG1Gal) = (0.0175, 0.175), what is the random-for... | {"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__en_all_first | sle-glyco_002 | en_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 | en | sle-glyco_002 | [
"images/SciDocBench/65/6594a917e8eabc3c3cb6fc51dbe4c0d5be7e0699225dd589ddd9a130f955afb0.jpg",
"images/SciDocBench/b5/b5ac30b7eb69b07dc2ea8ccea9f9a19227e95ec78dafc86cc67c7b4ea7c06643.jpg",
"images/SciDocBench/58/58a0a4e2adb6ed38561d4f5cfe4565917971ca1bf8cef297072b148400441dc0.jpg",
"images/SciDocBench/68/684ac... | ||
34 | Now I have industrial waste water with a high concentration of Cadmium(II). Due to the demand for efficiency, I have about 30 minutes to process the wastewater. Which material should I use?
Put the final answer in the following format:
{"answer": "<Material Name>", "description": "<Explain how you derive this answer.>"... | {"answer": "PCB"} | C3 | judge | [
"d4/d4b4f5a0227fc5031b6bba56e323e155d90b04a6872d6ea9d2cbf8c1e5828a0a.jpg",
"b8/b8199e4d17d35417bb2ef285d11a7b2282a65d9a62604da07b628c5fe81fc369.jpg",
"fa/face3f4cbd7d4460097c33a61b86523a58e5129b4f6fb8af5bceece725a5afaf.jpg",
"ec/ec8c4cfc663d93ef4fdc4bcf94fe3e8688a9996b3903451737b0951215122311.jpg",
"df/dfd4... | pineapple-avocado-hm_001__en_all_first | pineapple-avocado-hm_001 | en_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 | en | pineapple-avocado-hm_001 | [
"images/SciDocBench/d4/d4b4f5a0227fc5031b6bba56e323e155d90b04a6872d6ea9d2cbf8c1e5828a0a.jpg",
"images/SciDocBench/b8/b8199e4d17d35417bb2ef285d11a7b2282a65d9a62604da07b628c5fe81fc369.jpg",
"images/SciDocBench/fa/face3f4cbd7d4460097c33a61b86523a58e5129b4f6fb8af5bceece725a5afaf.jpg",
"images/SciDocBench/ec/ec8c4... | |
35 | Is altH(4) rotational symmetric?
Put the final answer in the following format:
{"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",
"9f/9f574a601c1bbd03aabc2fd0a4b632dc8ef32fb3dc6a3f5e48e4ab1cd53fa52c.jpg",
"a6/a643afac866f13bf825432530ed5cb386be4399fbcb819b7ca154b358c86bb21.jpg",
"d8/d8ca682fdb72afbc0b87499c490ea3f346a7fd77e2cf22fb159289f62dd14af0.jpg",
"98/9807... | euler-stratification_001__en_all_first | euler-stratification_001 | en_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 | en | euler-stratification_001 | [
"images/SciDocBench/a0/a006d0464d8f325867863827e67a3a7f0c129f6f3e8b876e13324625acb89c4d.jpg",
"images/SciDocBench/9f/9f574a601c1bbd03aabc2fd0a4b632dc8ef32fb3dc6a3f5e48e4ab1cd53fa52c.jpg",
"images/SciDocBench/a6/a643afac866f13bf825432530ed5cb386be4399fbcb819b7ca154b358c86bb21.jpg",
"images/SciDocBench/d8/d8ca6... | |
36 | If the transformation in Figure 6 is applied to the $$2 \times 2$$ region located at the upper-left of the intersection of the red and blue lines in Figure 2, does the resulting diagram remain reduced, and is the corresponding permutation preserved?
For each of the above question, output "Yes" or "No" in the final answ... | {"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__en_all_first | schubert-pipe-dreams_001 | en_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 | en | schubert-pipe-dreams_001 | [
"images/SciDocBench/2a/2a2a21c8439588d892675c20823aded5f56b302523055ae67aea4e57a4e148cf.jpg",
"images/SciDocBench/36/361ae383c8bc7933e7f51c8099b0647909cd48b280d737f5964be03abf434091.jpg",
"images/SciDocBench/f7/f7bdf5fffab4a17224fed160626c35e4afeedda7a5044b92dc70eb97bd2636bb.jpg",
"images/SciDocBench/81/814b4... | ||
37 | Read the paper carefully. Use only this paper. Do not explain. Output exactly one line as a valid JSON object:
{"BITS":"<BITS>","NUMS":<NUMS>,"CODE":"<CODE>"}
Rules:
1) BITS is a 10-character bitstring. Use 1 iff the claim is fully supported by the paper; otherwise use 0.
A. Theorem 1 gives an exact construction wi... | {"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__en_all_first | easy-to-hard-transformer_002 | en_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 | en | easy-to-hard-transformer_002 | [
"images/SciDocBench/23/234f3aa3f2cdebd5d7835b3b5b68903779b653d2169e013101e35eb04559be90.jpg",
"images/SciDocBench/ca/caaae0f4375f867e7773c7b336619d8b988c18c526350995aa61f6cb0770bf76.jpg",
"images/SciDocBench/0b/0b9b6a4636f8ab9a50d004db0d6cdddd5f16d27869a43406ad760e0be2dcb5cc.jpg",
"images/SciDocBench/34/34c77... | ||
38 | You are given 4 documents as page images.
- Document 1 (ppo): Images 1–2
- Document 2 (grpo): Images 3–5
- Document 3 (gspo): Images 6–7
- Document 4 (cispo): Images 8–9
You are a survey author unifying four RLHF policy-optimization papers under one notation.
=== UNIFIED NOTATION ===
- q: input prompt/question
- {o_i... | {"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__en_all_first | rlhf-formula-compare_001 | en_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 | en | rlhf-formula-compare_001 | [
"images/SciDocBench/8a/8a42354577f0049391ff5c41a92650e3e1a86c65744f8366c56c6232ddd59b8b.jpg",
"images/SciDocBench/ca/ca23f44fcc1e06d7228552919854d916196e60ba30be3765017ef31b17a93527.jpg",
"images/SciDocBench/f9/f90f82634d27987f2ce91d3b6884eee3dcad4db1977f1527b841387d79af68de.jpg",
"images/SciDocBench/fc/fc0b7... | ||
39 | You are given 2 documents as page images.
- Document 1 (eagle25): Image 1
- Document 2 (mammoth-vl): Image 2
Document 1 introduces Eagle 2.5 and Document 2 introduces MAmmoTH-VL.
From Table 1 in the Eagle 2.5 paper, extract the full list of dataset names used as training data sources. From Figure 3 in the MAmmoTH-VL p... | "\\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__en_all_first | eagle25-mammoth-vl_001 | en_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 | en | eagle25-mammoth-vl_001 | [
"images/SciDocBench/1d/1db33357eaf2b474115e7dcda4f33b8140a48572763c9ad33dcee1aac7a6bb09.jpg",
"images/SciDocBench/1d/1d3be9e85e6221e5f5a1ffea86b3d0dcca28d266678f7d0c3e6650953d087d2f.jpg"
] | |
40 | You are given 3 documents as page images.
- Document 1 (cambrian1) introduces the Cambrian-1 visual instruction tuning paper.
- Document 2 (llavaov) introduces LLaVA-OneVision.
- Document 3 (mammoth-vl) introduces MAmmoTH-VL.
List the official model/method names introduced by each of the three papers, in order.
Outpu... | {"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__en_all_first | cambrian-llavaov-mammoth_001 | en_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 | en | cambrian-llavaov-mammoth_001 | [
"images/SciDocBench/23/238f6d5d3156a5780dc2f463a50a8a9595242fce8d3ca1ac013091b0c5619353.jpg",
"images/SciDocBench/9d/9d154dd20a146167625e33c22de5cf91b5886c0ec938ed60f630f7c83f09a1f5.jpg",
"images/SciDocBench/7d/7dd0f2687cb0ccb9e628771f234b697a0139dee7102d8bc391f5c1d50ee142a3.jpg",
"images/SciDocBench/0e/0ef7b... | ||
41 | You are given 2 documents as page images.
- Document 1 (spatial-dreamer): Images 1–17
- Document 2 (spatial-ladder): Images 18–39
Each document introduces an approach improving spatial reasoning in Large Vision-language Models. Your task is to summarize the performance of four models (Qwen2.5-VL-3B, Qwen2.5-VL-7B, Spat... | {"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__en_all_first | spatial-dreamer-ladder_001 | en_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 | en | spatial-dreamer-ladder_001 | [
"images/SciDocBench/df/df1c5fbc30f4b80a1ca6999cc7df990049a58da3c83147eb19555b4c9398aab4.jpg",
"images/SciDocBench/46/469e4e79aeb3904fd1c85de14cbeb99bb92e41f0662795d2dd198d951e8f3fb0.jpg",
"images/SciDocBench/48/4845a85214865d291b1eca8397da49b115a7ee6284b16c94b92ef9e04570e578.jpg",
"images/SciDocBench/5b/5b9e5... | |
42 | You are given 2 documents as page images.
- Document 1 (ts-llava): Images 1–15
- Document 2 (pllava): Images 16–32
Document 1 introduces TS-LLaVA and Document 2 introduces PLLaVA. Each contains a table reporting model performance on MVBench. Your task is to consolidate the relevant results into a single unified table i... | {"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__en_all_first | ts-llava-pllava_001 | en_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 | en | ts-llava-pllava_001 | [
"images/SciDocBench/b8/b81764e45501a6f11e1f9c1148a050572c6b119a64c6d782adb0cad0cce5b5e4.jpg",
"images/SciDocBench/7b/7b281163dcd2607cfb437f121f8b463550be2dbad3cf17ad963f9866a5d00eef.jpg",
"images/SciDocBench/a1/a14f2f3ac5d195d3ec8f01806af403f102b1a2aebe4eb3ff37dc22f2180e4997.jpg",
"images/SciDocBench/55/554f2... | |
43 | Look at Figure 3 of the provided paper, which compares two training-reward curves. Identify the two methods being compared and the color of each curve.
Output JSON only:
{"method_1": "<method name>", "method_1_color": "<color>", "method_2": "<method name>", "method_2_color": "<color>"}
Use "purple" or "violet" interc... | {"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__en_all_first | gspo_002 | en_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 | en | gspo_002 | [
"images/SciDocBench/35/35148e3e76f7fbf7fe6c3a86e2b984dfcdce1ebed9968ce37d93c9d2b4efef19.jpg",
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"images/SciDocBench/57/572c1... | ||
44 | You are an expert Data Visualization Engineer and Python Developer (Matplotlib/Seaborn Specialist).
Objective: Your goal is to Reverse Engineer the provided scientific chart image into an executable Python script.
The Gold Standard:
1. Data Fidelity: The data points in your code must be as close as possible to the pi... | "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__en_all_first | llavacot_002 | en_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 | en | llavacot_002 | [
"images/SciDocBench/a7/a7f9a113f47dad25ab171a75e5cd2c60f9985127236c6dcd494a7fc7bb0c5bd5.jpg",
"images/SciDocBench/01/0129a4771975d1107ba796a304c2cbb20b3bca8f6c3f685a005027d3b33221fc.jpg",
"images/SciDocBench/37/37bdf5b9ba2c3fa5c79a51bd4eb144f1ecd2071f4b56ccb7e05058b720fecf50.jpg",
"images/SciDocBench/85/851d2... | |
45 | You are given a research paper (PDF). In Section 3.2, the paper describes an image preprocessing pipeline using a flowchart (Figure 1). Implement this pipeline as a Python script.
Constraints:
- Allowed libraries: PIL (Pillow) only. Do not use OpenCV, numpy, or any other library.
- Your script must accept an input ima... | {"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__en_all_first | constraintfollow_001 | en_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 | en | constraintfollow_001 | [
"images/SciDocBench/30/30b3b65d2a5c18a8e097e41fcd7c09649c108afe942037834eee5af839f88c53.jpg",
"images/SciDocBench/06/06de17ebb6debfbf39aa3faec08977e745631f1fa55c1111842282d1464d2ad1.jpg"
] | ||
46 | In the MM-IFEngine (mmifengine) paper, identify the section number that lists the order of the evaluation methods used in the framework.
Output JSON only:
{"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__en_all_first | mmifengine_003 | en_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 | en | mmifengine_003 | [
"images/SciDocBench/de/de6acb9a3f9a4cea3375adeddced89fdacf74e18d1db5717695b3314fd36a72b.jpg",
"images/SciDocBench/13/1385c6a246795bd9395dbafd230c7c2e148681cb2c633e1cb9157affd1ae13fc.jpg",
"images/SciDocBench/6f/6ff892681a86f47df62fbf288547c3de847255901ec451f7b838c0acf18f33be.jpg",
"images/SciDocBench/eb/eb8dc... | ||
47 | You are given 5 documents as page images.
- Document 1 (data_sample): Image 1 contains screenshots of multiple data samples arranged vertically.
Count how many distinct data samples are shown in Image 1 (numbered from top to bottom).
Output JSON only:
{"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__en_all_first | data_sample_001 | en_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 | en | data_sample_001 | [
"images/SciDocBench/72/7206ca81b1b667caf33c88961838334c31fa61b1cb4cb1884ebd1a1b968b71ab.jpg",
"images/SciDocBench/72/7206ca81b1b667caf33c88961838334c31fa61b1cb4cb1884ebd1a1b968b71ab.jpg",
"images/SciDocBench/13/1385c6a246795bd9395dbafd230c7c2e148681cb2c633e1cb9157affd1ae13fc.jpg",
"images/SciDocBench/72/7206c... | ||
48 | You are given 2 documents as page images.
- Document 1 (poet2): Image 1
- Document 2 (progen2): Images 2–3
Below are 4 dataset entries extracted from a mixed data pool. For each entry, identify:
- The Source Paper (ProGen2 or PoET-2).
- The Primary Database the data was drawn from.
- The Key Preprocessing Step (e.g., ... | "[{\"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__en_all_first | poet2-progen2_001 | en_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 | en | poet2-progen2_001 | [
"images/SciDocBench/68/68873f38d2a575029d31d50e97c0d8a0cfaac45a15cf34c47c11cee2312b889a.jpg",
"images/SciDocBench/6c/6c71cbec496cb9ce1dc48f8c1f0faaf273a742994e795d7c35520583b167125c.jpg",
"images/SciDocBench/5a/5ac5283de7a8c634e7968a2c67855068764977c0e105e91b997c340f97ec793a.jpg"
] | |
49 | You are given 2 documents as page images.
- Document 1 (sle-glyco): Images 1–9
- Document 2 (igg-glycans): Images 10–44
In Document 1, two specific IgG glycoforms are identified as having strong correlation with SLE (Systemic Lupus Erythematosus). List the names of these two glycoforms.
Output JSON only:
{"glycoforms... | {"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__en_all_first | sle-glyco-igg_001 | en_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 | en | sle-glyco-igg_001 | [
"images/SciDocBench/65/6594a917e8eabc3c3cb6fc51dbe4c0d5be7e0699225dd589ddd9a130f955afb0.jpg",
"images/SciDocBench/b5/b5ac30b7eb69b07dc2ea8ccea9f9a19227e95ec78dafc86cc67c7b4ea7c06643.jpg",
"images/SciDocBench/58/58a0a4e2adb6ed38561d4f5cfe4565917971ca1bf8cef297072b148400441dc0.jpg",
"images/SciDocBench/68/684ac... | ||
50 | I'm reading the attached paper on Stairway codes. Please reproduce Figure 3, Weight-8 case only (the bottom row of Fig. 3) as a LaTeX/TikZ figure.
The figure should contain two parts connected by a "≃" symbol:
Please make sure the output compiles cleanly as a standalone LaTeX document. | "\\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__en_all_first | stairway_001 | en_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 | en | stairway_001 | [
"images/SciDocBench/97/97ed9435076ae48fcace5e10cd48f1dbe18ef2ea7deb5e67882cb1f946043df6.jpg",
"images/SciDocBench/eb/ebaccfde884fb14277d42741ab53f3db7c1ab444fcf65a9fed2f5c14253a2e98.jpg",
"images/SciDocBench/c9/c95838c2a218fe9f45e72c0705ff9e2a894db88c15f736f4423d5f4f6982eb9b.jpg",
"images/SciDocBench/24/245a8... | |
51 | I'm attaching a physics paper. Look at Figure 1(b), which shows a 3-regular graph. Report the number of nodes (N) and the regularity (k) of this graph.
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__en_all_first | itwa-spin_001 | en_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 | en | itwa-spin_001 | [
"images/SciDocBench/14/14eba3e86d9d540a20acab554b51b99edfcb48d8a45a7d708a08a02712900c3b.jpg",
"images/SciDocBench/44/44fdf6e9b5b01a9605667de4c0e53f5f25582e09215863b7a22adef1b40c7b4c.jpg",
"images/SciDocBench/e4/e43c67deab2615bc416921a39a9ad47688473b74ec30eb6f5515c756691dbd24.jpg",
"images/SciDocBench/49/49bd7... | ||
52 | def conv1x1(in_feat: int, out_feat: int) -> None: ...
def res_transformer_block(feat: int, heads: int) -> None: ...
def down_conv2x2(in_feat: int, out_feat: int, stride: int) -> None: ...
def linear(in_feat: int, out_feat: int) -> None: ...
Refer to **Figure 6** and its caption. Based **solely on the figure**, reconst... | "conv1x1(in_feat, 64)\nres_transformer_block(64, 8)\nres_transformer_block(64, 8)\nres_transformer_block(64, 8)\nres_transformer_block(64, 8)\ndown_conv2x2(64, 128, 2)\nres_transformer_block(128, 8)\nres_transformer_block(128, 8)\nres_transformer_block(128, 8)\nres_transformer_block(128, 8)\nconv1x1(128, 256)\nlinear(2... | E2 | judge | [
"13/1347399ec8a601a7de6aaee624b719598fd4ba19c89be5c89c28650a258d9822.jpg",
"e0/e054dd417332962d656b12d937c04d87281d0212932be4b99bcec4e7d676bde5.jpg",
"9c/9c8f0f0fa6de3f515d63c7bb9f5f3eeeeb21621148c17fa644043908dbe5fbf9.jpg",
"50/5091c2a131b749208a147084d821f0e474a5381036f64f6bf65c156966591480.jpg",
"ef/efae... | qflownet_001__en_all_first | qflownet_001 | en_all_first | all_first | text | You are evaluating a model's reconstruction of the QFlowNet forward pass from Figure 6.
The correct sequential forward pass is (one call per line):
{answer}
Model prediction:
{prediction}
Scoring: 13 operations total, each worth 1/13. Award credit for each operation that is correct in both function name and all argu... | {"reasoning": "E2 flowchart reconstruction of QFlowNet forward pass from Figure 6 (Appendix A, page 7). Derivation: reading top-to-bottom from the figure: (1) conv1x1(in_feat, 64) — initial projection; (2-5) four res_transformer_block(64, 8) — first stage with 64 features, 8 attention heads; (6) down_conv2x2(64, 128, 2... | qflownet | cs | en | qflownet_001 | [
"images/SciDocBench/13/1347399ec8a601a7de6aaee624b719598fd4ba19c89be5c89c28650a258d9822.jpg",
"images/SciDocBench/e0/e054dd417332962d656b12d937c04d87281d0212932be4b99bcec4e7d676bde5.jpg",
"images/SciDocBench/9c/9c8f0f0fa6de3f515d63c7bb9f5f3eeeeb21621148c17fa644043908dbe5fbf9.jpg",
"images/SciDocBench/50/5091c... | |
53 | You are given four screenshots, labeled A, B, C, and D. Each screenshot contains a passage from the main text of the same scientific paper.
Your task is to determine the order in which these four passages appear in the paper, from earliest to latest. The passages may come from any of the following sections: Abstract, ... | "D>C>A>B" | A1 | judge | [
"ce/ce07ab28f0ab9448cd3c12fb2dff907840050841f49ef942bc4d92eecd8e73a1.jpg",
"e4/e469a292e46311691331ea71b20339af1e228b8a0fcc9317a49ea361a4799230.jpg",
"0b/0b51be56f499d6bd923f82bcc2ebbd1f0d7dae5b1d2dd051437d72857424a6b3.jpg",
"68/682c823bc1576e2a70ecb34385b6d905fa1f4ef32cbdbffc06bfdb905b4e39da.jpg"
] | np_a1_01_001__en_all_first | np_a1_01_001 | en_all_first | all_first | text | You are an expert evaluator for ranking tasks. I will provide you with the ground-truth and the model's predicted ranking. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
If the model's predicted ranking is entirely correct, it receiv... | {} | np_a1_01 | scientific | en | np_a1_01_001 | [
"images/SciDocBench/ce/ce07ab28f0ab9448cd3c12fb2dff907840050841f49ef942bc4d92eecd8e73a1.jpg",
"images/SciDocBench/e4/e469a292e46311691331ea71b20339af1e228b8a0fcc9317a49ea361a4799230.jpg",
"images/SciDocBench/0b/0b51be56f499d6bd923f82bcc2ebbd1f0d7dae5b1d2dd051437d72857424a6b3.jpg",
"images/SciDocBench/68/682c8... | |
54 | You are given five screenshots, labeled A, B, C, D and E. Each screenshot contains a passage from the main text of the same scientific paper.
Your task is to determine the order in which these five passages appear in the paper, from earliest to latest. The passages may come from any of the following sections: Abstract... | "E>A>D>C>B" | A1 | judge | [
"37/378b893956f9a9f2413f13566395e2885ec880d2a9308a032ea5c901931bf99e.jpg",
"65/65b616c6e02175eae567fa87e54c47212973f4724576c1c5933655281b738df1.jpg",
"4b/4b975143aec3d8f90f04870ff1d4396b972627ca8d2033df542a6748f16e4cdf.jpg",
"07/074f6859299f3086af4e6f6c850ddffcd4894e4329e704cdaf9e4c1e361a603d.jpg",
"41/413d... | np_a1_02_001__en_all_first | np_a1_02_001 | en_all_first | all_first | text | You are an expert evaluator for ranking tasks. I will provide you with the ground-truth and the model's predicted ranking. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
If the model's predicted ranking is entirely correct, it receiv... | {} | np_a1_02 | scientific | en | np_a1_02_001 | [
"images/SciDocBench/37/378b893956f9a9f2413f13566395e2885ec880d2a9308a032ea5c901931bf99e.jpg",
"images/SciDocBench/65/65b616c6e02175eae567fa87e54c47212973f4724576c1c5933655281b738df1.jpg",
"images/SciDocBench/4b/4b975143aec3d8f90f04870ff1d4396b972627ca8d2033df542a6748f16e4cdf.jpg",
"images/SciDocBench/07/074f6... | |
55 | You are given five screenshots, labeled A, B, C, D and E. Each screenshot contains a passage from the main text of the same scientific paper.
Your task is to determine the order in which these five passages appear in the paper, from earliest to latest. The passages may come from any of the following sections: Abstract... | "B>E>D>C>A" | A1 | judge | [
"c8/c8e4cbdac9bd0077122c57b1754e0143f999bcf7c834a6890f402f61053b9ae3.jpg",
"06/065be586dda3cdca385808f4a8d9943147c32f8967b8c027a8d85ab332c6f242.jpg",
"84/840d2aad5b36ff82cae3efe2599a7716bf63f4fbf357db912fbf704063314972.jpg",
"9b/9b912bd9fc6b68c116d262d61ce7d26f4abae73eb4e20cc0cc8fd82e381cc622.jpg",
"0f/0f43... | np_a1_03_001__en_all_first | np_a1_03_001 | en_all_first | all_first | text | You are an expert evaluator for ranking tasks. I will provide you with the ground-truth and the model's predicted ranking. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
If the model's predicted ranking is entirely correct, it receiv... | {} | np_a1_03 | scientific | en | np_a1_03_001 | [
"images/SciDocBench/c8/c8e4cbdac9bd0077122c57b1754e0143f999bcf7c834a6890f402f61053b9ae3.jpg",
"images/SciDocBench/06/065be586dda3cdca385808f4a8d9943147c32f8967b8c027a8d85ab332c6f242.jpg",
"images/SciDocBench/84/840d2aad5b36ff82cae3efe2599a7716bf63f4fbf357db912fbf704063314972.jpg",
"images/SciDocBench/9b/9b912... | |
56 | You are given five screenshots, labeled A, B, C, D and E. Each screenshot contains a passage from the main text of the same scientific paper.
Your task is to determine the order in which these five passages appear in the paper, from earliest to latest. The passages may come from any of the following sections: Abstract... | "A>E>C>B>D" | A1 | judge | [
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"81/816a... | np_a1_04_001__en_all_first | np_a1_04_001 | en_all_first | all_first | text | You are an expert evaluator for ranking tasks. I will provide you with the ground-truth and the model's predicted ranking. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
If the model's predicted ranking is entirely correct, it receiv... | {} | np_a1_04 | scientific | en | np_a1_04_001 | [
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57 | You are given five screenshots, labeled A, B, C, D and E. Each screenshot contains a passage from the main text of the same scientific paper.
Your task is to determine the order in which these five passages appear in the paper, from earliest to latest. The passages may come from any of the following sections: Abstract... | "D>B>C>E>A" | A1 | judge | [
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"93/93ad... | np_a1_05_001__en_all_first | np_a1_05_001 | en_all_first | all_first | text | You are an expert evaluator for ranking tasks. I will provide you with the ground-truth and the model's predicted ranking. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
If the model's predicted ranking is entirely correct, it receiv... | {} | np_a1_05 | scientific | en | np_a1_05_001 | [
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58 | Now I have asked the Deepseek-R1 （DS-R1） a physics question. My question bank contains many mathematical theorems and calculation methods extracted from AMC12, AMC10, AIME24, and OlympiadBench. By using the method mentioned in the document, I found that my question bank can also help DS-R1 answer this physics question ... | {"Location": "Table 2", "Description": "The phenomenon in the question uses examples from one domain (math) to help with a different domain (physics), which is about cross-domain generalizability. And the example bank has a low similarity to the target problem (since physics ≠ math). The section 'Generalizability' best... | A2 | judge | [
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"62/6230... | np_a2_03_001__en_all_first | np_a2_03_001 | en_all_first | all_first | json | You are an expert evaluator the model's response to a question. I will provide you with the ground-truth and the model's prediction. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
1. If the model's predicted 'Location' is incorrect, ... | {} | np_a2_03 | scientific | en | np_a2_03_001 | [
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59 | I need to use some of the datasets mentioned in this article to train my own model. Please help me select datasets according to the following steps:
1. Choose the top 15 datasets based on the number of tokens.
2. Only retain datasets from the "Embodied AI" and "Spatial Understanding" categories among these 15 datasets.... | {"Embodied AI": ["Eb-Alfred", "Eb-Habitat", "Muep", "OWMM-VLM Data", "Robo2VLM-1", "RoboVQA"], "Spatial Understanding": ["Vg-Llm-Scannet-Det", "Vica-322K", "Vsi 590K"]} | A4 | json_match | [
"f7/f75599ff0b8032a868da55eff38bc281f29930c96239b78da68f6486a6418224.jpg"
] | np_a4_01_001__en_all_first | np_a4_01_001 | en_all_first | all_first | json | {} | np_a4_01 | scientific | en | np_a4_01_001 | [
"images/SciDocBench/f7/f75599ff0b8032a868da55eff38bc281f29930c96239b78da68f6486a6418224.jpg"
] | ||
60 | You are a paper reading assisstant.Read the article, tell me the arrown on the sole's area II and III's direction(From up to bottom or from bottom to up?)
Your answer should be several single sentences or a word. | "Both from up to bottom" | A5 | judge | [
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"3b/3b7f... | np_a5_02_001__en_all_first | np_a5_02_001 | en_all_first | all_first | text | {} | np_a5_02 | scientific | en | np_a5_02_001 | [
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"images/SciDocBench/73/730d8... | ||
61 | In the bottom figure of Fig. 1, the ranges of $\epsilon$ values for the AQC(exp) and QAOA curves are closest to which of the following options?
A. 5e-3 to 5e-1
B. 5e-4 to 3e-1
C. 1e-3 to 3e-1
D. 5e-3 to 1e0
E. 5e-4 to 1e-1
F. 8e-4 to 1e0
G. 8e-4 to 5e-1
H. 1e-3 to 1e-1
Think carefully and output in the following forma... | {"AQC(exp) range": "G", "QAOA range": "C", "Description": "The bottom figure is a log-log plot with κ=10. The scale on the x-axis gradually becomes denser from left to right, then suddenly thins out after a certain mark, before becoming denser again, repeating this cycle. Based on the characteristics of logarithmic cur... | B2 | judge | [
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"d7/d767... | np_b2_01_001__en_all_first | np_b2_01_001 | en_all_first | all_first | json | You are an expert evaluator the model's response to a question. I will provide you with the question, the ground-truth and the model's prediction. You need to score the model's prediction.
Question: {prompt}
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
1. If the model cor... | {} | np_b2_01 | scientific | en | np_b2_01_001 | [
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62 | You are given a scientific PDF document. Please carefully examine each figure and subfigure in the document, and find all the places where the content/data does not match the main text.
For each cell you identify as problematic, output a JSON object with the following fields:
- Location: the specific table this issue b... | [{"Location": "Figure 8(b)", "Description": "The x-axis displays adsorption dosages of 0.5, 1.0, 1.5, 2.0, and 2.5 g/L, which contradicts the experimental dosages described in the main text (2, 4, 6, 8, and 10 g)."}, {"Location": "Figure 8(c)", "Description": "The x-axis displays agitation speed of of 50, 100, 150, 20... | C1 | judge | [
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"7b/7bf7fdce58d1b97528e80355c7d196197ba180a448c43848192a414178ab88bd.jpg",
"7d/7d16f5eaecec8797cadff724fc1655efc6778eed52980e1da8260d92ddd4de32.jpg"
] | np_c1_01_001__en_all_first | np_c1_01_001 | en_all_first | all_first | json | You are an expert evaluator. You will be given a question, a reference answer, and a model prediction.
Score the prediction from 0.0 to 1.0 based on how well it matches the reference answer in content and accuracy.
Question:
{prompt}
Reference answer:
{answer}
Model prediction:
{prediction}
Scoring rules (Total=1.0... | {} | np_c1_01 | scientific | en | np_c1_01_001 | [
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"images/SciDocBench/7d/7d16f5eaecec8797cadff724fc1655efc6778eed52980e1da8260d92ddd4de32.jpg"
] | |
63 | Read the paper end-to-end. Use only this paper. Do not explain. Output exactly one 20-character bitstring. Use 1 iff the claim is fully supported by the paper; otherwise use 0.
A. The paper studies the symmetric two-community SBM G(n,p,q), with two equal-size clusters and edge probability p within clusters and q acros... | "10101110101111101100" | C1 | judge | [
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"56/5616259ecd97054e9e36f10f3db8f5e315466c50ea6e1d59a2a6cdb078f376e6.jpg",
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"4e/4ef7... | np_c1_07_001__en_all_first | np_c1_07_001 | en_all_first | all_first | text | You are an expert evaluator the model's response to a question. I will provide you with the ground-truth and the model's prediction. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
1. The ground-truth is a bit-string with 20 bits. If t... | {} | np_c1_07 | scientific | en | np_c1_07_001 | [
"images/SciDocBench/90/90a9427cde889071385c2ad90e12b6956b5e38f67853d15010e2892842b8414b.jpg",
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64 | Read the paper end-to-end. Use only this paper. Do not explain. Output exactly one 20-character bitstring. Use 1 iff the claim is fully supported by the paper; otherwise use 0.
A. The trade-off function T(P,Q)(α) is defined as the infimum of type-II error over rejection rules whose type-I error is at most α.
B. Propos... | "10101101011101011101" | C1 | judge | [
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"66/66d5... | np_c1_09_001__en_all_first | np_c1_09_001 | en_all_first | all_first | text | You are an expert evaluator the model's response to a question. I will provide you with the ground-truth and the model's prediction. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
1. The ground-truth is a bit-string with 20 bits. If t... | {} | np_c1_09 | scientific | en | np_c1_09_001 | [
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65 | In the document I provided, there is a chart on the right side. 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; however, during th... | {"Dataset": "DenseFusion", "Description": "The bar height of DenseFusion is approximately at the middle of 1000 and 1500. However, the label is 1453, which should be near 1500. So the bar of DenseFusion is incorrectly plotted.\nFurthermore, the slope of the red line is nearly the same from LLaVa-23K to ShareGPT4V and ... | C2 | judge | [
"5e/5ecdc5ad6c5e9e809c5f6a4239fce6a21f7403661e15e0eb37eccb658973937e.jpg"
] | np_c2_01_001__en_all_first | np_c2_01_001 | en_all_first | all_first | json | You are an expert evaluator the model's response to a question. I will provide you with the ground-truth and the model's prediction. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
1. If the model's predicted 'Dataset' is incorrect, t... | {} | np_c2_01 | scientific | en | np_c2_01_001 | [
"images/SciDocBench/5e/5ecdc5ad6c5e9e809c5f6a4239fce6a21f7403661e15e0eb37eccb658973937e.jpg"
] | |
66 | I provided the first five pages of a document as well as the subfigure Figure 2b in Figure 2 introduced on page 5. However, in Figure 2b, the labels for the neural activity plots corresponding to two neurons are reversed, causing the content in the figure to be inconsistent with the analysis of Figure 2b in the documen... | {"Neuron 1": "SMDV", "Neuron 2": "SAAV", "Description": "From Figure 2b we know that the reversal ends at x=2. According to the text on page 5, SAAV activity is described as being higher during reversals in ventral turns (blue line) than in dorsal turns (green line). It suggests that SAAV may predict the future turning... | C2 | judge | [
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"0b/0b1e141d3d269801504f0255dd8e6fec030fad5daadeddd6864355c313b1b378.jpg",
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"08/0885db8b31807a8357b8c66dff941c98ae5ed1929ce3eb9f9b932c2b750dac68.jpg",
"27/27a0... | np_c2_03_001__en_all_first | np_c2_03_001 | en_all_first | all_first | json | You are an expert evaluator the model's response to a question. I will provide you with the ground-truth and the model's prediction. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
1. Both fields of 'Neurons' in the prediction MUST be... | {} | np_c2_03 | scientific | en | np_c2_03_001 | [
"images/SciDocBench/1d/1d7d3de0842dc9ed9d1b9d2224287627b0492ef87b7d10e4ecbe2d0f2287f9cb.jpg",
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67 | Identify the relative heights of the red line and yellow line in Fig (b) and (c) from the attached file.
For each subplot, examine the full time course and divide it into sequential time stages based on the x-axis progression and any visually distinguishable phase boundaries. For each stage, determine the relative posi... | "[\n {\"Subplot\": \"Fig. (b)\", \"Stage_order\": \"1\", \"Relative_position\": \"red < yellow\"},\n {\"Subplot\": \"Fig. (b)\", \"Stage_order\": \"2\", \"Relative_position\": \"red > yellow\"},\n {\"Subplot\": \"Fig. (c)\", \"Stage_order\": \"1\", \"Relative_position\": \"red > yellow\"},\n {\"Subplot\": \"Fig. (c... | C3 | judge | [
"2f/2f128a7ea3f0be2c82142ff9ca2e437c6271329f595f9580abcf4922295cb2a2.jpg"
] | np_c3_03_001__en_all_first | np_c3_03_001 | en_all_first | all_first | text | You are an expert evaluator the model's response to a question. I will provide you with the ground-truth and the model's prediction. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
1. The ground-truth is a list of dictionaries. The or... | {} | np_c3_03 | scientific | en | np_c3_03_001 | [
"images/SciDocBench/2f/2f128a7ea3f0be2c82142ff9ca2e437c6271329f595f9580abcf4922295cb2a2.jpg"
] | |
68 | 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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"c0/c0f0... | np_d1_01_001__en_all_first | np_d1_01_001 | en_all_first | all_first | json | You are an expert evaluator the model's response to a question. I will provide you with the ground-truth and the model's prediction. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
1. The ground-truth is a list of 10 dictionaries. (If... | {} | np_d1_01 | scientific | en | np_d1_01_001 | [
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69 | Reproduce Figure (b) in mermaid format. Retain all nodes and the zigzag lines representing 'Gap junction'. Ignore the arrows of 'Chemical synapse'. Each node should strictly follow the name in the figure and you do not need to reproduce the colors used in the original figure. Find all 'Gap junction' connections and plo... | "```mermaid\ngraph LR\n 1[AIB]\n 2[AVE]\n 3[AVA]\n 4[RIM]\n 5[SMB]\n 6[SAA]\n 7[RMD]\n 8[SMD]\n 9[RIV]\n \n 1 --- 2\n 1 --- 4\n 1 --- 9\n 2 --- 3\n 2 --- 4\n 2 --- 7\n 2 --- 9\n 3 --- 4\n 5 --- 6\n 6 --- 7\n 7 --- 8\n 8 --- 9\n```" | E1 | judge | [
"d2/d2cfa1ed242929c4ea1708bfdf2a9caea63d4e91722ae1b26351717544154b9f.jpg"
] | np_e1_02_001__en_all_first | np_e1_02_001 | en_all_first | all_first | text | You are an expert evaluator the model's response to a question. I will provide you with the ground-truth and the model's prediction. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
1. Extract the content between ```mermaid and ``` (to... | {} | np_e1_02 | scientific | en | np_e1_02_001 | [
"images/SciDocBench/d2/d2cfa1ed242929c4ea1708bfdf2a9caea63d4e91722ae1b26351717544154b9f.jpg"
] | |
70 | Please draw a vertical flowchart for R-(BN)₂ using Mermaid syntax, strictly adhering to the following format:
1. Node Format:
- Content: Chemical formula + relative energy (unit: eV, values taken from the paper, Relative from S₀)
- Style: Unified fill color `#e8f5e9`, border color `#1b5e20`, black text
2. Edge F... | "flowchart TD\n A[\"2-NaphNH₂<br/>0.00 eV\"] -->|\"λ=-, τ=-\"| B[\"(BN)₁<br/>0.00 eV\"]\n A -->|\"λ=-, τ=-\"| C[\"R-(BN)₂<br/>0.00 eV\"]\n\n C -->|\"λ=280, τ=-\"| D[\"R-(BN)₂ S₁(¹LE)<br/>3.32 eV\"]\n D -->|\"λ=365, τ=3.0ns\"| C\n D -->|\"λ=-, τ=-\"| E[\"R-(BN)₂ ¹HLCT<br/>2.88 eV\"]\n E -->|\"λ=430, τ=... | B2 | judge | [
"06/0670c21fab94fc043653e00fdca33d4272ac01ead7ff555b5a92b52e93a7533a.jpg",
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"a3/a31a... | np_b2_03_001__en_all_first | np_b2_03_001 | en_all_first | all_first | text | You are an expert evaluator the model's response to a question. I will provide you with the ground-truth and the model's prediction. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
1. Determine the score based on how well the predicte... | {} | np_b2_03 | scientific | en | np_b2_03_001 | [
"images/SciDocBench/06/0670c21fab94fc043653e00fdca33d4272ac01ead7ff555b5a92b52e93a7533a.jpg",
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71 | 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).
Write a compact chain using arrows, for example:
Theorem 7.1(1c) <- Proposition 7.1(1a) + Lemma 3.9
Proposition 3.9 <- Lemma 3.9 + Assumption 3.6
Lemma 3.2 <- Definition 3.4 | "Theorem 7.1(1a) ← Proposition 6.3 + Lemma 3.10\nTheorem 7.1(1b) ← Theorem 7.1(1a)\nTheorem 7.1(1c) sharpness ← Proposition 3.5 + Lemma 2.1\nTheorem 7.1(2) ← Proposition 6.4 + Lemma 3.10\nTheorem 7.1(3) ← Proposition 6.5 + Lemma 3.10\nLemma 3.10 ← Lemma 3.9\nLemma 3.9 ← Definitions 3.6, 3.7 + Proposition 3.5\nPropositi... | B3 | judge | [
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"c3/c384... | np_b3_01_001__en_all_first | np_b3_01_001 | en_all_first | all_first | text | You are an expert evaluator the model's response to a question. I will provide you with the ground-truth and the model's prediction. You need to score the model's prediction.
Ground-truth answer: {answer}
Model prediction: {prediction}
Scoring rules (Total=1.0):
1. The initial score is set as 0.0.
2. Full score is ... | {} | np_b3_01 | scientific | en | np_b3_01_001 | [
"images/SciDocBench/dd/dde8f8885392e469d83602d0f1d082e12700bc0fed6710fba1de0d117c4a5f77.jpg",
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72 | In the document I provided, there are a few errors in the category of 'Spatial Models' in Table 1. Your task is to find all of them.
Follow these steps:
1. Identify the errors in the table.
2. Identify numerical value corresponding to the errors.
3. Return your answer as a list of three JSON dictionaries in the follow... | [{"value": "48.4", "description": "The average accuracy for Spatial-MLLM-4B is incorrectly calculated. The sum of the 8 category scores (65.3 + 34.8 + 63.1 + 45.1 + 41.3 + 46.2 + 33.5 + 46.3) equals 375.6, which should result in an average of 46.95 (rounded to 47.0), not 48.4."}, {"value": "47.8", "description": "The a... | C1 | judge | [
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"images/SciDocBench/6b/6b1e4... | ||
73 | Read the paper end-to-end. Use only this paper. Do not explain. Output exactly one 20-character bitstring. Use 1 iff the claim is fully supported by the paper; otherwise use 0.
A. The paper studies a simplified Transformer model with attention blocks only, omitting both MLPs and LayerNorm.
B. The introduction says tha... | "11101010111011110011" | C1 | judge | [
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"2e/2e4a... | np_c1_04_001__en_all_first | np_c1_04_001 | en_all_first | all_first | text | {} | np_c1_04 | scientific | en | np_c1_04_001 | [
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"images/SciDocBench/bb/bb542... | ||
74 | Read the paper end-to-end. Use only this paper. Do not explain. Output exactly one 20-character bitstring. Use 1 iff the claim is fully supported by the paper; otherwise use 0.
A. The abstract states that a constant number of self-attention layers can efficiently simulate, and be simulated by, a constant number of MPC... | "11001110101011010110" | C1 | judge | [
"77/776c438082593797575e5a1e140148105f70021d84c3b5007fb1375507497ca9.jpg",
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"27/2781... | np_c1_06_001__en_all_first | np_c1_06_001 | en_all_first | all_first | text | {} | np_c1_06 | scientific | en | np_c1_06_001 | [
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"images/SciDocBench/ed/ed340... | ||
75 | Read the paper end-to-end. Use only this paper. Do not explain. Output exactly one 20-character bitstring. Use 1 iff the claim is fully supported by the paper; otherwise use 0.
A. The paper studies linear regression in a separable Hilbert space, using the minimum-norm interpolating estimator.
B. The covariate assumpti... | "11100111011010101011" | C1 | judge | [
"3c/3ced1d04bc0fe76549252cb8ad4780767839e5adeb51220b547357833f832cba.jpg",
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"images/SciDocBench/9b/9bacb... | ||
76 | 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 | [
"49/49e27e6cd4b00680c10fd8f62792f83623c8e905aad4a385be531b990c4e0ac3.jpg"
] | np_c3_02_001__en_all_first | np_c3_02_001 | en_all_first | all_first | text | {} | np_c3_02 | scientific | en | np_c3_02_001 | [
"images/SciDocBench/49/49e27e6cd4b00680c10fd8f62792f83623c8e905aad4a385be531b990c4e0ac3.jpg"
] | ||
77 | 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 | [
"56/560c39560b9e15153e501652b37299bced0e02f45f40c0133af990a73a4c2ecb.jpg",
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78 | 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 | [
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79 | 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 | [
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80 | 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 | [
"25/25977f25d76158f527b825d69c07d2c6862e2a059c44758c934fca9b353dc6b9.jpg",
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81 | 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 | [
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82 | 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 | [
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"cf/cfcc... | np_b1_01_001__en_all_first | np_b1_01_001 | en_all_first | all_first | text | {} | np_b1_01 | scientific | en | np_b1_01_001 | [
"images/SciDocBench/84/84a2993a0abd71e78d023d7f4e521b79cd54b587994accb33716654f538dd1c1.jpg",
"images/SciDocBench/11/11e3ef0cc4e07233ead245a1ce7910d03ee40663834df5ac96ba124067663d40.jpg",
"images/SciDocBench/5f/5fb5295428bf2fbffaf6c191cb35beb1f397f7a51019bc90fcdb62769f88ce83.jpg",
"images/SciDocBench/39/39173... | ||
83 | 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 | [
"9e/9e6b2c02af648f4041b35abc89ae2a13c86d8be999472bf515240804e31d67d1.jpg",
"b2/b2497ab2325c6b6e024cdde62a11cef83615ae58088d738ef8fa853343ce8dfa.jpg",
"1f/1fff7731d7e399dd3fc3f163cafc51d80a7afa51535a9cd92d55a7cf5e205b7c.jpg",
"11/11baae7d537bc9a726c9a2c4bd8313c05525fcb9274821c50ac011448bc9c87a.jpg",
"80/8085... | np_b2_02_001__en_all_first | np_b2_02_001 | en_all_first | all_first | json | {} | np_b2_02 | scientific | en | np_b2_02_001 | [
"images/SciDocBench/9e/9e6b2c02af648f4041b35abc89ae2a13c86d8be999472bf515240804e31d67d1.jpg",
"images/SciDocBench/b2/b2497ab2325c6b6e024cdde62a11cef83615ae58088d738ef8fa853343ce8dfa.jpg",
"images/SciDocBench/1f/1fff7731d7e399dd3fc3f163cafc51d80a7afa51535a9cd92d55a7cf5e205b7c.jpg",
"images/SciDocBench/11/11baa... | ||
84 | 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 | [
"b7/b78c343ac14fd269b65f5d6bbbe4c419a1e4eff60df0d03201f2e835e8741413.jpg",
"eb/eb64456f0186fba6c22a7e6d861303b8d739a8bffce52e8b12ed2ddfa0139db1.jpg",
"ee/eeb1c63069f78e2652157df5b8dcdd82470423426196c7e6d237d5ec775d372c.jpg",
"80/80fe72cf23ff62084de941145016311afa1057a912387b77c2cc1956e0dd9d79.jpg",
"1b/1be4... | np_c1_02_001__en_all_first | np_c1_02_001 | en_all_first | all_first | json | {} | np_c1_02 | scientific | en | np_c1_02_001 | [
"images/SciDocBench/b7/b78c343ac14fd269b65f5d6bbbe4c419a1e4eff60df0d03201f2e835e8741413.jpg",
"images/SciDocBench/eb/eb64456f0186fba6c22a7e6d861303b8d739a8bffce52e8b12ed2ddfa0139db1.jpg",
"images/SciDocBench/ee/eeb1c63069f78e2652157df5b8dcdd82470423426196c7e6d237d5ec775d372c.jpg",
"images/SciDocBench/80/80fe7... | ||
85 | 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 | [
"a2/a20d1a4d7a7a3e4f3bef194de8b854ded4f0474a38270345c2c04e81b2d3052c.jpg",
"71/71d8d7571614a39b0314e560bffad6133369367610b54d0fb0a247fd3a70d679.jpg",
"b8/b8e931f3e5ffd3ff27c6ab4786d825f7e2cbf2a1df6b0a544d3454f03aa63d5c.jpg",
"84/845cfc133b3ef49203e06c817aa65dd48c9c0024a944767ee5d85d140e6b02ae.jpg",
"0b/0b87... | np_c1_05_001__en_all_first | np_c1_05_001 | en_all_first | all_first | text | {} | np_c1_05 | scientific | en | np_c1_05_001 | [
"images/SciDocBench/a2/a20d1a4d7a7a3e4f3bef194de8b854ded4f0474a38270345c2c04e81b2d3052c.jpg",
"images/SciDocBench/71/71d8d7571614a39b0314e560bffad6133369367610b54d0fb0a247fd3a70d679.jpg",
"images/SciDocBench/b8/b8e931f3e5ffd3ff27c6ab4786d825f7e2cbf2a1df6b0a544d3454f03aa63d5c.jpg",
"images/SciDocBench/84/845cf... | ||
86 | 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 | [
"f4/f44efee4be07513f225b233d1857381a255367badc2b552b7834c9387023fcd0.jpg",
"1d/1d9f5694c346b83e5a8b68478aaacb59b63fdb0b5470b1d83d3f7262f63266d1.jpg",
"61/61f8fe026cfc6ea5062c587ab91531fb3879f34453dbad8ca7daecd0b7505557.jpg",
"c3/c3f89f2a14dce744140b0b7d1191d06283a0a2b196ca6e0d44ca64c3403e02cd.jpg",
"0c/0c84... | np_c3_04_001__en_all_first | np_c3_04_001 | en_all_first | all_first | text | {} | np_c3_04 | scientific | en | np_c3_04_001 | [
"images/SciDocBench/f4/f44efee4be07513f225b233d1857381a255367badc2b552b7834c9387023fcd0.jpg",
"images/SciDocBench/1d/1d9f5694c346b83e5a8b68478aaacb59b63fdb0b5470b1d83d3f7262f63266d1.jpg",
"images/SciDocBench/61/61f8fe026cfc6ea5062c587ab91531fb3879f34453dbad8ca7daecd0b7505557.jpg",
"images/SciDocBench/c3/c3f89... | ||
87 | 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 | [
"b6/b6a5fe9dee4707b62de68db118ece04a96320c1d28c7d2cfbda0d5d1a36a06a3.jpg",
"08/08bd7fe2c499b45289fa6bcf92d672b59519a6bb28048570ade18f74887b45ab.jpg",
"38/383b4abb4c6a9e2b6160c67a539ddf9d116bdc09beedae5473f5ce146a33fbb4.jpg",
"41/41358b9c6b4f85e24d50d7bd5031d7a56a457caa50477ebcf4570f2bed0db59e.jpg",
"17/17ff... | np_a2_02_001__en_all_first | np_a2_02_001 | en_all_first | all_first | json | {} | np_a2_02 | scientific | en | np_a2_02_001 | [
"images/SciDocBench/b6/b6a5fe9dee4707b62de68db118ece04a96320c1d28c7d2cfbda0d5d1a36a06a3.jpg",
"images/SciDocBench/08/08bd7fe2c499b45289fa6bcf92d672b59519a6bb28048570ade18f74887b45ab.jpg",
"images/SciDocBench/38/383b4abb4c6a9e2b6160c67a539ddf9d116bdc09beedae5473f5ce146a33fbb4.jpg",
"images/SciDocBench/41/41358... | ||
88 | 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 | [
"ba/ba93306680b9d682019e0a14b016f75ab371a742c28a3f8693eee67b6369709e.jpg",
"a3/a31e907f55ced28e90770031674cf71233332eb2812ac551c4e3bfa87d510ee6.jpg",
"26/268b15c95fa8fb0d9a6e0ec47e76dd95d85de5b9644bcb1bb851db1c43a862a6.jpg",
"28/281646ea0e2f60c035753e64607504b0e80940bb367969389b4c7a9039ecea9a.jpg",
"2a/2a3a... | np_a4_02_001__en_all_first | np_a4_02_001 | en_all_first | all_first | json | {} | np_a4_02 | scientific | en | np_a4_02_001 | [
"images/SciDocBench/ba/ba93306680b9d682019e0a14b016f75ab371a742c28a3f8693eee67b6369709e.jpg",
"images/SciDocBench/a3/a31e907f55ced28e90770031674cf71233332eb2812ac551c4e3bfa87d510ee6.jpg",
"images/SciDocBench/26/268b15c95fa8fb0d9a6e0ec47e76dd95d85de5b9644bcb1bb851db1c43a862a6.jpg",
"images/SciDocBench/28/28164... | ||
89 | 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 | [
"1e/1e9b75a61e0078f8e1aacfe82ff78110fd57f2a57a4f9bc891de5b990f0dbf01.jpg"
] | np_a5_03_001__en_all_first | np_a5_03_001 | en_all_first | all_first | text | {} | np_a5_03 | scientific | en | np_a5_03_001 | [
"images/SciDocBench/1e/1e9b75a61e0078f8e1aacfe82ff78110fd57f2a57a4f9bc891de5b990f0dbf01.jpg"
] | ||
90 | 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 | [
"63/6343627fa261d841c4bb09371f0af6c1eaff19401d992aff450fdd4f36952560.jpg",
"7e/7edd31db926e69c3a9328481500b1e1ff5a4ede8f19423659b7c559251348d3b.jpg",
"ba/ba4e2d8d94bdf9ee89d56f938e7792af0c43b7100c2035b6841c0273f3929433.jpg",
"23/2319c10b940f0d49aeaf0bca0bcb3a8a8a499bf323e238c57197a291bb87dbd5.jpg",
"10/1083... | np_a5_04_001__en_all_first | np_a5_04_001 | en_all_first | all_first | json | {} | np_a5_04 | scientific | en | np_a5_04_001 | [
"images/SciDocBench/63/6343627fa261d841c4bb09371f0af6c1eaff19401d992aff450fdd4f36952560.jpg",
"images/SciDocBench/7e/7edd31db926e69c3a9328481500b1e1ff5a4ede8f19423659b7c559251348d3b.jpg",
"images/SciDocBench/ba/ba4e2d8d94bdf9ee89d56f938e7792af0c43b7100c2035b6841c0273f3929433.jpg",
"images/SciDocBench/23/2319c... | ||
91 | 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 | [
"a8/a8fef1b99279556303e79a058ef899f2611c1df0a3dcb8133c4b0aeabf73efb4.jpg",
"c2/c22b3c3f1b1b7280e5a091bda44d97d5f978a43c1dd965e93a7c2df644a282eb.jpg",
"2e/2e1a21e813cd214722e0d447626690c694dd97f948d332c64824c52ff9b86630.jpg",
"e2/e2cedc653c69a66810dc3ea4bb953ec5d02ad4768241d54677fb95b1d2cea960.jpg",
"98/9881... | np_a5_06_001__en_all_first | np_a5_06_001 | en_all_first | all_first | json | {} | np_a5_06 | scientific | en | np_a5_06_001 | [
"images/SciDocBench/a8/a8fef1b99279556303e79a058ef899f2611c1df0a3dcb8133c4b0aeabf73efb4.jpg",
"images/SciDocBench/c2/c22b3c3f1b1b7280e5a091bda44d97d5f978a43c1dd965e93a7c2df644a282eb.jpg",
"images/SciDocBench/2e/2e1a21e813cd214722e0d447626690c694dd97f948d332c64824c52ff9b86630.jpg",
"images/SciDocBench/e2/e2ced... | ||
92 | 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 | [
"e4/e4cefea666faaf908907299f51aa2b584a2101312dbe460a1bfb70a38ed10767.jpg",
"7b/7bbad53e5e076b04176c878240149ed778048bd2cbeb86f7139b24a5e05ecad3.jpg",
"93/93d2ab91c1873071190cc043829d91cd892a45cf65cde74de808b0291adf563c.jpg",
"95/95a4343921bf90d389107a8542a5f09d92c5f8a0c9cd7dcd557ea07bbaf03ac6.jpg",
"14/14ff... | np_b2_04_001__en_all_first | np_b2_04_001 | en_all_first | all_first | json | {} | np_b2_04 | scientific | en | np_b2_04_001 | [
"images/SciDocBench/e4/e4cefea666faaf908907299f51aa2b584a2101312dbe460a1bfb70a38ed10767.jpg",
"images/SciDocBench/7b/7bbad53e5e076b04176c878240149ed778048bd2cbeb86f7139b24a5e05ecad3.jpg",
"images/SciDocBench/93/93d2ab91c1873071190cc043829d91cd892a45cf65cde74de808b0291adf563c.jpg",
"images/SciDocBench/95/95a43... | ||
93 | 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 | [
"29/29021e9fa17d38eecb43b4de5d83c26f901d286f7b4a657cf5cda9001a0ccef5.jpg",
"0d/0dc585d604f35ea9fdd157b842bbc0e38b1ac57533938167a6ca8e075992bd19.jpg",
"d5/d54b434ffc8d272b85a9e99777fc8731bc4d2bacf44b31ed134609860105cce1.jpg",
"46/466caaf647d0376d9c60361a6767f5660d9aa41789e6f3c859ceb90a88b7a3ad.jpg",
"7a/7abc... | np_b2_05_001__en_all_first | np_b2_05_001 | en_all_first | all_first | json | {} | np_b2_05 | scientific | en | np_b2_05_001 | [
"images/SciDocBench/29/29021e9fa17d38eecb43b4de5d83c26f901d286f7b4a657cf5cda9001a0ccef5.jpg",
"images/SciDocBench/0d/0dc585d604f35ea9fdd157b842bbc0e38b1ac57533938167a6ca8e075992bd19.jpg",
"images/SciDocBench/d5/d54b434ffc8d272b85a9e99777fc8731bc4d2bacf44b31ed134609860105cce1.jpg",
"images/SciDocBench/46/466ca... | ||
94 | 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 | [
"e2/e2da6c79a6f543c0f49b40da603be045826cfe378bb16c10c7364f4be85dc9fe.jpg",
"d7/d74776e82727d11d80b6541aee9e466f43ccdc47ccaeb9c53eed5c0ee7d15325.jpg",
"d4/d411690c77a42d773f3c3ad121d1cf878b0d5a57f7b9ffa08b70a17db480c230.jpg",
"ec/ec9b760890cb7c42e849537075388546f620da431c7e764cce45c1090e9c0085.jpg",
"f6/f66f... | np_c2_02_001__en_all_first | np_c2_02_001 | en_all_first | all_first | json | {} | np_c2_02 | scientific | en | np_c2_02_001 | [
"images/SciDocBench/e2/e2da6c79a6f543c0f49b40da603be045826cfe378bb16c10c7364f4be85dc9fe.jpg",
"images/SciDocBench/d7/d74776e82727d11d80b6541aee9e466f43ccdc47ccaeb9c53eed5c0ee7d15325.jpg",
"images/SciDocBench/d4/d411690c77a42d773f3c3ad121d1cf878b0d5a57f7b9ffa08b70a17db480c230.jpg",
"images/SciDocBench/ec/ec9b7... | ||
95 | 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 | [
"21/216e146a8eb0798a4882cc569b15a11d7c53e9f7cb38af950b049502dd923b3b.jpg",
"76/762300d9b0a9006a727720e9ec5588833ffc31584f45e024cda9ab969953837f.jpg",
"8d/8d9e5d593a5631b95714159d3297fc9626bf8a84273d245270d7a82ca7f52ca5.jpg",
"c0/c01a6e3e052384e3ca291ec0160e5d592edc633a3f91a6463ed68eeb12cb9849.jpg",
"9d/9d36... | np_c3_05_001__en_all_first | np_c3_05_001 | en_all_first | all_first | json | {} | np_c3_05 | scientific | en | np_c3_05_001 | [
"images/SciDocBench/21/216e146a8eb0798a4882cc569b15a11d7c53e9f7cb38af950b049502dd923b3b.jpg",
"images/SciDocBench/76/762300d9b0a9006a727720e9ec5588833ffc31584f45e024cda9ab969953837f.jpg",
"images/SciDocBench/8d/8d9e5d593a5631b95714159d3297fc9626bf8a84273d245270d7a82ca7f52ca5.jpg",
"images/SciDocBench/c0/c01a6... | ||
96 | 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 | [
"f9/f9ae61614c8705153c27f727a6247a3b438e5896ec3454f68bb7fcd46051cbd1.jpg",
"c1/c19c11f814fa446e92a9b83e5053b6138782b08b59aa0b3f05ebcd9dd20e2d7b.jpg",
"b9/b94ae4b0f8c8a757490657080992934b8e757e8bb63198b71621d4770575b3f6.jpg",
"44/444eb67fcd821a6695acc9dcae96802ef44b9dd67fa15aa51f30745dcdcdfccd.jpg",
"d9/d991... | np_d2_01_001__en_all_first | np_d2_01_001 | en_all_first | all_first | json | {} | np_d2_01 | scientific | en | np_d2_01_001 | [
"images/SciDocBench/f9/f9ae61614c8705153c27f727a6247a3b438e5896ec3454f68bb7fcd46051cbd1.jpg",
"images/SciDocBench/c1/c19c11f814fa446e92a9b83e5053b6138782b08b59aa0b3f05ebcd9dd20e2d7b.jpg",
"images/SciDocBench/b9/b94ae4b0f8c8a757490657080992934b8e757e8bb63198b71621d4770575b3f6.jpg",
"images/SciDocBench/44/444eb... | ||
97 | 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 | [
"a9/a9681eb57ca7dd3e746316e96ee2dee21574dfc51b840f6c73e2636162a03db4.jpg",
"cf/cf73e3a7398fe594feafeb9d84d85a07cb072739110c6237d1f74970640d5f65.jpg",
"bc/bcaf1f284a8d83056a2ba951679a9d86a1f35dc93c237b0f74a278c01a44ac6d.jpg",
"b0/b05f786288b34c51a93472ac2db18e38135d46fb3f60b837fd9bdc57cecd455b.jpg",
"81/8117... | np_d2_02_001__en_all_first | np_d2_02_001 | en_all_first | all_first | json | {} | np_d2_02 | scientific | en | np_d2_02_001 | [
"images/SciDocBench/a9/a9681eb57ca7dd3e746316e96ee2dee21574dfc51b840f6c73e2636162a03db4.jpg",
"images/SciDocBench/cf/cf73e3a7398fe594feafeb9d84d85a07cb072739110c6237d1f74970640d5f65.jpg",
"images/SciDocBench/bc/bcaf1f284a8d83056a2ba951679a9d86a1f35dc93c237b0f74a278c01a44ac6d.jpg",
"images/SciDocBench/b0/b05f7... | ||
98 | 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 | [
"d5/d5f6862e6baf1735fe956d4d908e94f4abacaeba8c781dfbc170ecb95d8c2473.jpg",
"2c/2c97680f92f3814a81b7b48f929c77cfa93887b353cea8743c1f7f554b8ece69.jpg"
] | np_d3_01_001__en_all_first | np_d3_01_001 | en_all_first | all_first | json | {} | np_d3_01 | scientific | en | np_d3_01_001 | [
"images/SciDocBench/d5/d5f6862e6baf1735fe956d4d908e94f4abacaeba8c781dfbc170ecb95d8c2473.jpg",
"images/SciDocBench/2c/2c97680f92f3814a81b7b48f929c77cfa93887b353cea8743c1f7f554b8ece69.jpg"
] | ||
99 | 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 | [
"b1/b1f25586b73974c730953b4864cc161fb686f5ff12a3817e69f645e59946e897.jpg",
"9f/9f302c12dba7af268f2a823212e0dab04ea6006308c43764c853adcb4179bb06.jpg",
"a2/a2a1114410327bd05c35fa5900ae98d6eedfc99db1cc242bff716d9311c0275f.jpg",
"93/93fe75ca4980ed2e8bcc61e7e92165abc6f2ec68f4d226794b7c18cf1c8d5b31.jpg",
"81/8162... | np_d3_02_001__en_all_first | np_d3_02_001 | en_all_first | all_first | json | {} | np_d3_02 | scientific | en | np_d3_02_001 | [
"images/SciDocBench/b1/b1f25586b73974c730953b4864cc161fb686f5ff12a3817e69f645e59946e897.jpg",
"images/SciDocBench/9f/9f302c12dba7af268f2a823212e0dab04ea6006308c43764c853adcb4179bb06.jpg",
"images/SciDocBench/a2/a2a1114410327bd05c35fa5900ae98d6eedfc99db1cc242bff716d9311c0275f.jpg",
"images/SciDocBench/93/93fe7... |
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