qa_pair_id stringlengths 29 89 | scenario stringclasses 4
values | level stringclasses 3
values | question_subtype stringclasses 9
values | answer_model stringlengths 12 50 | question_text stringlengths 123 589 | question_meta dict | annotations listlengths 5 25 | source_images listlengths 0 4 | answer_raw stringlengths 789 4.45k | question_model stringclasses 6
values | paper_id stringclasses 6
values | paper_pdf_path stringclasses 6
values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
2024_acl-long_533_cross_g2_p5_7_24_results_interpretation | S2 | cross_page | results_interpretation | gemma-4-31b-it | What pattern do Table 2 and Table 11 together show about the generated-context setup relative to the retrieved-context baselines on the same benchmark, and which named rows stand out as the main exceptions or near-ties? | {
"input_types": [
"Table 2",
"Table 11",
"Section 5.2"
],
"question_type": "results_interpretation",
"visual_subtype": "quantitative",
"error_subtype": null,
"scope": null
} | [
{
"sentence": "Together, Table 2 and Table 11 demonstrate a consistent pattern where the generated-context setup, implemented through the MedGENIE framework, generally outperforms retrieved-context baselines across the evaluated benchmarks.",
"label": "contradiction",
"role": "Interpretation",
"rati... | [
"pdfs_jpg/2024_acl-long_533/page_005.jpg",
"pdfs_jpg/2024_acl-long_533/page_007.jpg",
"pdfs_jpg/2024_acl-long_533/page_024.jpg"
] | Together, Table 2 and Table 11 demonstrate a consistent pattern where the generated-context setup, implemented through the MedGENIE framework, generally outperforms retrieved-context baselines across the evaluated benchmarks. In Table 11, which focuses specifically on MedQA, every MedGENIE model—ranging from MedGENIE-P... | gpt-5.4-mini | 2024_acl-long_533 | pdfs/2024_acl-long_533.pdf |
2024_acl-long_533_full_cross_adversarial | S1 | full_paper | results_interpretation | gemma-4-31b-it | I've been examining the subject-specific performance charts in Figure 6 to see how the framework handles different medical specialties. If I'm reading this right, given the abstract's claim that MEDGENIE sets a new state-of-the-art in each testbed, it looks like the FID-Flan-T5 model actually maintains a performance le... | {
"input_types": [
"Abstract",
"Figure 6"
],
"question_type": "cross_adversarial",
"visual_subtype": "comparison",
"error_subtype": "overgeneralized_scope",
"scope": null
} | [
{
"sentence": "The interpretation that the MedGENIE-FID-Flan-T5 model maintains a performance lead over the Meditron baseline across every single subject area in MedMCQA is incorrect.",
"label": "not_a_claim",
"role": "no_rad",
"rationale": "",
"depends_on": [],
"errors": [],
"2d_box": [... | [] | The interpretation that the MedGENIE-FID-Flan-T5 model maintains a performance lead over the Meditron baseline across every single subject area in MedMCQA is incorrect. While the model demonstrates superior performance in many categories, the data in Figure 6 shows that it does not outperform the Meditron baseline in e... | gemini-3-flash-preview | 2024_acl-long_533 | pdfs/2024_acl-long_533.pdf |
2024_acl-long_533_page_001_trend | S4 | single_page | trend | gemini-3-flash-preview | In Figure 1, how does accuracy change over publication date for the model series—does it climb steadily, flatten out, dip at any point, or jump around—and where are the main rises or drops most noticeable? | {
"input_types": [
"Figure 1"
],
"question_type": "trend",
"visual_subtype": null,
"error_subtype": null,
"scope": null
} | [
{
"sentence": "According to Figure 1, the accuracy of the model series on the MedQA-USMLE 4-opt benchmark follows a consistent upward trajectory from January 2021 to February 2024.",
"label": "contradiction",
"role": "Interpretation",
"rationale": "",
"depends_on": [
"S3",
"S5",
... | [
"pdfs_jpg/2024_acl-long_533/page_001.jpg"
] | According to Figure 1, the accuracy of the model series on the MedQA-USMLE 4-opt benchmark follows a consistent upward trajectory from January 2021 to February 2024. The data illustrates a steady climb in performance over this three-year period, with the frontier line connecting the top-performing models showing no ins... | gpt-5.4-mini | 2024_acl-long_533 | pdfs/2024_acl-long_533.pdf |
2024_acl-long_533_page_007_comparison | S4 | single_page | comparison | gemma-4-31b-it | How do LLaMA-2-chat and Zephyr-beta compare across the three grounding conditions (None, Generated, and Retrieved) in Table 2? Describe where the performance gap between the two models is widest, where it narrows, and how the effectiveness of the grounding methods varies for each model across the three benchmarks. | {
"input_types": [
"Table 2",
"Section 5.2"
],
"question_type": "comparison",
"visual_subtype": null,
"error_subtype": null,
"scope": null
} | [
{
"sentence": "Across the three grounding conditions, Zephyr-β consistently outperforms LLaMA-2-chat.",
"label": "supported",
"role": "Interpretation",
"rationale": "",
"depends_on": [
"S2",
"S3",
"S4"
],
"errors": [],
"2d_box": [],
"propagation_type": "none"
... | [
"pdfs_jpg/2024_acl-long_533/page_007.jpg"
] | Across the three grounding conditions, Zephyr-β consistently outperforms LLaMA-2-chat. In the no grounding condition, Zephyr-β achieves an average score of 51.1 compared to 40.4 for LLaMA-2-chat. With retrieved grounding, Zephyr-β maintains its lead with an average score of 54.8, while LLaMA-2-chat scores 42.1. Under g... | gemini-3-flash-preview | 2024_acl-long_533 | pdfs/2024_acl-long_533.pdf |
2024_acl-long_533_page_008_quantitative | S4 | single_page | quantitative | gpt-5.4-mini | Looking at Table 3, how does adding different amounts of artificial contexts (G*) to the baseline knowledge base (KB*) across the different models (llama2, mistral, zephyr) and datasets (MedQA, MedMCQA, MMLU) generally affect the accuracy scores? | {
"input_types": [
"Table 3"
],
"question_type": "quantitative",
"visual_subtype": null,
"error_subtype": null,
"scope": null
} | [
{
"sentence": "Table 3 shows a broadly positive effect from adding artificial contexts G* to the baseline knowledge base KB* across all three models and across the three datasets, although the size of the gain depends on both the model and the dataset.",
"label": "contradiction",
"role": "Interpretation... | [
"pdfs_jpg/2024_acl-long_533/page_008.jpg"
] | Table 3 shows a broadly positive effect from adding artificial contexts G* to the baseline knowledge base KB* across all three models and across the three datasets, although the size of the gain depends on both the model and the dataset. In every setting, moving from KB* alone to KB* plus artificial contexts improves a... | gemma-4-31b-it | 2024_acl-long_533 | pdfs/2024_acl-long_533.pdf |
2024_acl-long_533_page_022_adversarial | S5 | single_page | adversarial | gemma-4-31b-it | If I'm interpreting the charts in Figure 6 correctly, the model that achieves the highest accuracy in every subject for MedMCQA maintains that top position in the MMLU benchmarks as well, specifically outperforming all other models in the Medical Genetics category. | {
"input_types": [
"Figure 6 (MedMCQA panels)",
"Figure 6 (MMLU panel)"
],
"question_type": "adversarial",
"visual_subtype": "comparison",
"error_subtype": "reversed_relationship",
"scope": null
} | [
{
"sentence": "The interpretation of the charts in Figure 6 is incorrect.",
"label": "contradiction",
"role": "Interpretation",
"rationale": "",
"depends_on": [
"S2"
],
"errors": [
{
"span": "",
"tags": [
"C4"
],
"correction": ""
... | [
"pdfs_jpg/2024_acl-long_533/page_022.jpg"
] | The interpretation of the charts in Figure 6 is incorrect. No single model achieves the highest accuracy across every subject for the MedMCQA benchmarks. While MedGENIE-FID-Flan-T5 holds the top position in many categories, it does not do so in Ophthalmology, where MedGENIE-Zephyr-β achieves the highest accuracy at 70%... | gemini-3-flash-preview | 2024_acl-long_533 | pdfs/2024_acl-long_533.pdf |
Beyond_Outcome_Verification_cross_g3_p5_13_14_implication | S2 | cross_page | implication | google/gemma-4-31b-it | How do the zero-valued categories for the RoBBR datasets in Table 6, when mapped to their definitions in Figure 5, limit the benchmark evaluation of the model's reasoning capabilities across all risk-of-bias domains? | {
"input_types": [
"Table 1",
"Table 6",
"Figure 5",
"Figure 6"
],
"question_type": "implication",
"visual_subtype": "comparison",
"error_subtype": null,
"scope": null
} | [
{
"sentence": "The zero-valued categories for the RoBBR datasets in Table 6 limit the benchmark evaluation by creating gaps where the model's reasoning capabilities cannot be assessed for specific risk-of-bias domains.",
"label": "contradiction",
"role": "Interpretation",
"rationale": "False because... | [
"pdfs_jpg/Beyond_Outcome_Verification/page_005.jpg",
"pdfs_jpg/Beyond_Outcome_Verification/page_013.jpg",
"pdfs_jpg/Beyond_Outcome_Verification/page_014.jpg"
] | The zero-valued categories for the RoBBR datasets in Table 6 limit the benchmark evaluation by creating gaps where the model's reasoning capabilities cannot be assessed for specific risk-of-bias domains. According to Table 6, the RoBBR Cochrane dataset contains zero instances for categories G, H, and I, while the RoBBR... | gemini-3.5-flash | Beyond_Outcome_Verification | pdfs/Beyond_Outcome_Verification.pdf |
Beyond_Outcome_Verification_full_conceptual | S1 | full_paper | conceptual | qwen/qwen3.5-9b | What does Figure 2 show about how the step-by-step outputs in the model's structured reasoning template are verified and scored individually under the process rewarding scheme? | {
"input_types": [
"Figure 2",
"Figure 6"
],
"question_type": "conceptual",
"visual_subtype": "visual_understanding",
"error_subtype": null,
"scope": null
} | [
{
"sentence": "Figure 2 provides a side-by-side comparison of \"Verifiable Outcome Rewarding\" on the left and \"Verifiable Process Rewarding\" on the right.",
"label": "supported",
"role": "Observation",
"rationale": "",
"depends_on": [],
"errors": [],
"2d_box": [
{
"page_... | [] | Figure 2 provides a side-by-side comparison of "Verifiable Outcome Rewarding" on the left and "Verifiable Process Rewarding" on the right. The right side specifically illustrates how the VPRM framework handles step-by-step outputs. It depicts a structured reasoning trace divided into distinct steps (labeled Step 1, Ste... | gemini-3.5-flash | Beyond_Outcome_Verification | pdfs/Beyond_Outcome_Verification.pdf |
Beyond_Outcome_Verification_full_methodological | S1 | full_paper | methodological | qwen/qwen3.5-9b | How does the step-wise verification design in the method connect to the performance patterns in the results, and what seems to explain the improvement it produces? | {
"input_types": [
"Figure 1",
"Table 4",
"Table 5"
],
"question_type": "methodological",
"visual_subtype": "comparison",
"error_subtype": null,
"scope": null
} | [
{
"sentence": "The method’s core design involves Verifiable Process Reward Models (VPRMs), which validate every intermediate reasoning step against deterministic, domain-specific rules rather than relying solely on the final outcome.",
"label": "supported",
"role": "Observation",
"rationale": "",
... | [] | The method’s core design involves Verifiable Process Reward Models (VPRMs), which validate every intermediate reasoning step against deterministic, domain-specific rules rather than relying solely on the final outcome. This step-wise verification design directly connects to the observed performance patterns, specifical... | gpt-5.4-mini | Beyond_Outcome_Verification | pdfs/Beyond_Outcome_Verification.pdf |
Beyond_Outcome_Verification_page_002_visual_understanding | S4 | single_page | visual_understanding | mistralai/mistral-small-2603 | Can you walk me through what Figure 1 is showing — what are the main boxes, arrows, and labels in the top reasoning chain and the bottom decision tree, and how does each step/label lead to the final risk outcome? | {
"input_types": [
"Figure 1"
],
"question_type": "visual_understanding",
"visual_subtype": null,
"error_subtype": null,
"scope": null
} | [
{
"sentence": "Figure 1 illustrates a two-part framework for assessing risk of bias in studies through a structured, verifiable reasoning process.",
"label": "contradiction",
"role": "Observation",
"rationale": "This is not a \"two-part\" framework for assessing risk of bias. It's a depiction of the... | [
"pdfs_jpg/Beyond_Outcome_Verification/page_002.jpg"
] | Figure 1 illustrates a two-part framework for assessing risk of bias in studies through a structured, verifiable reasoning process. The top portion presents a sequential reasoning pipeline applied to an input study x, which involves four distinct assessment steps. Each step corresponds to a guideline-defined question a... | gpt-5.4-mini | Beyond_Outcome_Verification | pdfs/Beyond_Outcome_Verification.pdf |
Beyond_Outcome_Verification_page_007_quantitative | S4 | single_page | quantitative | nvidia/nemotron-3-nano-omni-30b-a3b-reasoning:free | Based on Table 2, how does the performance of the models listed under 'Our Models' change as they move from the CochraneForest benchmark to the RoBBR Cochrane and RoBBR Non-Cochrane benchmarks? | {
"input_types": [
"Table 2"
],
"question_type": "quantitative",
"visual_subtype": null,
"error_subtype": null,
"scope": null
} | [
{
"sentence": "Based on Table 2, the performance of the models listed under \"Our Models\" consistently decreases as they transition from the CochraneForest benchmark to the RoBBR Cochrane benchmark, and continues to decline as they move to the RoBBR Non-Cochrane benchmark.",
"label": "supported",
"role... | [
"pdfs_jpg/Beyond_Outcome_Verification/page_007.jpg"
] | Based on Table 2, the performance of the models listed under "Our Models" consistently decreases as they transition from the CochraneForest benchmark to the RoBBR Cochrane benchmark, and continues to decline as they move to the RoBBR Non-Cochrane benchmark.
Specifically, examining the Accuracy (Acc) values for each mo... | google/gemma-4-31b-it | Beyond_Outcome_Verification | pdfs/Beyond_Outcome_Verification.pdf |
Beyond_Outcome_Verification_page_013_adversarial | S5 | single_page | adversarial | qwen/qwen3.6-35b-a3b | If I'm reading this right, Algorithm 1 provides the core prediction logic applied to generate labels for all the risk domains (A through I) listed in Figure 5—is that correct? | {
"input_types": [
"Algorithm 1",
"Figure 5"
],
"question_type": "adversarial",
"visual_subtype": "visual_understanding",
"error_subtype": "overgeneralized_scope",
"scope": null
} | [
{
"sentence": "Based on the provided document, it is not correct to assume that Algorithm 1 provides the core prediction logic for all risk domains listed from A through I.",
"label": "not_a_claim",
"role": "no_rad",
"rationale": "",
"depends_on": [],
"errors": [],
"2d_box": [],
"pro... | [
"pdfs_jpg/Beyond_Outcome_Verification/page_013.jpg"
] | Based on the provided document, it is not correct to assume that Algorithm 1 provides the core prediction logic for all risk domains listed from A through I.
Algorithm 1 is explicitly titled "RoB A Macro" and defines a procedure named `PREDICTLABEL-A`. This naming convention suggests the algorithm is specific to Domai... | qwen/qwen3.5-27b | Beyond_Outcome_Verification | pdfs/Beyond_Outcome_Verification.pdf |
Multi_lexsum_real_world_summaries_of_civil_rights_lawsuits_cross_g1_p4_6_8_methodological | S2 | cross_page | methodological | qwen/qwen3.5-27b | What does Table 4 show about using a longer summary as input versus a predicted one when generating a shorter summary, and how does the pipeline in Figure 4b help explain that gap? | {
"input_types": [
"Figure 4b",
"Table 4"
],
"question_type": "methodological",
"visual_subtype": "comparison",
"error_subtype": null,
"scope": null
} | [
{
"sentence": "Table 4 indicates that model performance is significantly higher when a ground truth (Gold) longer summary is provided as input compared to using a predicted (Predicted L') summary.",
"label": "supported",
"role": "Interpretation",
"rationale": "",
"depends_on": [
"S3",
... | [
"pdfs_jpg/Multi_lexsum_real_world_summaries_of_civil_rights_lawsuits/page_004.jpg",
"pdfs_jpg/Multi_lexsum_real_world_summaries_of_civil_rights_lawsuits/page_006.jpg",
"pdfs_jpg/Multi_lexsum_real_world_summaries_of_civil_rights_lawsuits/page_008.jpg"
] | Table 4 indicates that model performance is significantly higher when a ground truth (Gold) longer summary is provided as input compared to using a predicted (Predicted L') summary. In the progressive summarization results shown in Table 4(b), there is a notable drop in ROUGE scores when the input shifts from a Gold su... | gpt-5.4-mini | Multi_lexsum_real_world_summaries_of_civil_rights_lawsuits | pdfs/Multi_lexsum_real_world_summaries_of_civil_rights_lawsuits.pdf |
Multi_lexsum_real_world_summaries_of_civil_rights_lawsuits_cross_g4_p5_7_9_implication | S2 | cross_page | implication | nvidia/nemotron-3-nano-omni-30b-a3b-reasoning:free | Looking at Figure 1 and Table 3, what does the relationship between the target length for the shortest summaries and the actual word counts produced reveal about where the approach breaks down? | {
"input_types": [
"Figure 1",
"Table 3"
],
"question_type": "implication",
"visual_subtype": "quantitative",
"error_subtype": null,
"scope": null
} | [
{
"sentence": "Based on Figure 1,the target length for the shortest summaries, which are labeled as \"Tiny Summaries,\" is specified as \"One sentence, ~25 words.\"",
"label": "supported",
"role": "Observation",
"rationale": "",
"depends_on": [],
"errors": [],
"2d_box": [
{
... | [
"pdfs_jpg/Multi_lexsum_real_world_summaries_of_civil_rights_lawsuits/page_005.jpg",
"pdfs_jpg/Multi_lexsum_real_world_summaries_of_civil_rights_lawsuits/page_007.jpg",
"pdfs_jpg/Multi_lexsum_real_world_summaries_of_civil_rights_lawsuits/page_009.jpg"
] | Based on Figure 1,the target length for the shortest summaries, which are labeled as "Tiny Summaries," is specified as "One sentence, ~25 words."
Looking at Table 3, the task corresponding to Tiny Summaries is "D -> T" (Document to Tiny). In the "Words" column for this task, the actual word counts produced by the vari... | google/gemma-4-31b-it | Multi_lexsum_real_world_summaries_of_civil_rights_lawsuits | pdfs/Multi_lexsum_real_world_summaries_of_civil_rights_lawsuits.pdf |
Multi_lexsum_real_world_summaries_of_civil_rights_lawsuits_page_006_quantitative | S4 | single_page | quantitative | mistralai/ministral-14b-2512 | Looking at Table 2, describe the variation in target words and target sentences among the datasets in the 'Short' category. | {
"input_types": [
"Table 2"
],
"question_type": "quantitative",
"visual_subtype": null,
"error_subtype": null,
"scope": null
} | [
{
"sentence": "In the 'Short' category of Table 2, there is notable variation in the number of target words and sentences among the different datasets.",
"label": "contradiction",
"role": "Interpretation",
"rationale": "Correct interpretation but stemming from a flawed premise. There is a hidden rea... | [
"pdfs_jpg/Multi_lexsum_real_world_summaries_of_civil_rights_lawsuits/page_006.jpg"
] | In the 'Short' category of Table 2, there is notable variation in the number of target words and sentences among the different datasets. The BigPatent dataset has a target summary consisting of 116.7 words and 3.5 sentences, which is relatively concise. In contrast, the MS^2 dataset has a significantly larger target su... | google/gemma-4-31b-it | Multi_lexsum_real_world_summaries_of_civil_rights_lawsuits | pdfs/Multi_lexsum_real_world_summaries_of_civil_rights_lawsuits.pdf |
Multi_lexsum_real_world_summaries_of_civil_rights_lawsuits_page_009_adversarial | S5 | single_page | adversarial | z-ai/glm-4.6v | If I'm reading this right, the multitask training approach always outperforms the single-task baseline in terms of R-1f1 across all the different summary length configurations mentioned in the text and Table 5. Is that right? | {
"input_types": [
"Table 5",
"the paragraph above Section 5"
],
"question_type": "adversarial",
"visual_subtype": "comparison",
"error_subtype": "overgeneralized_scope",
"scope": null
} | [
{
"sentence": "To determine whether the multitask training approach always outperforms the single-task baseline in terms of R-1f1 across all summary length configurations, we analyze Table 5 and the accompanying text.",
"label": "not_a_claim",
"role": "no_rad",
"rationale": "",
"depends_on": [],... | [
"pdfs_jpg/Multi_lexsum_real_world_summaries_of_civil_rights_lawsuits/page_009.jpg"
] | To determine whether the multitask training approach always outperforms the single-task baseline in terms of R-1f1 across all summary length configurations, we analyze Table 5 and the accompanying text. The table compares two multitask models (one with three tasks: D → {L,S,T}; one with two tasks: L → {S,T}) against th... | google/gemma-4-31b-it | Multi_lexsum_real_world_summaries_of_civil_rights_lawsuits | pdfs/Multi_lexsum_real_world_summaries_of_civil_rights_lawsuits.pdf |
Rogue_scores_cross_g2_p3_6_19_conceptual | S2 | cross_page | conceptual | google/gemma-4-26b-a4b-it | Looking at Figure 3 and Table 2, how does the error distribution of Nonstandard-Implementations explain the growth of the red bars in recent years? | {
"input_types": [
"Figure 3",
"Table 2",
"Section G"
],
"question_type": "conceptual",
"visual_subtype": "trend",
"error_subtype": null,
"scope": null
} | [
{
"sentence": "In Figure 3, the red bars represent the number of papers that cite an incorrect ROUGE package.",
"label": "supported",
"role": "Observation",
"rationale": "Legend: red = Cites Incorrect Package (n = 755).",
"depends_on": [],
"errors": [],
"2d_box": [
{
"page_... | [
"pdfs_jpg/Rogue_scores/page_003.jpg",
"pdfs_jpg/Rogue_scores/page_006.jpg",
"pdfs_jpg/Rogue_scores/page_019.jpg"
] | In Figure 3, the red bars represent the number of papers that cite an incorrect ROUGE package. These bars show a significant increase in recent years, particularly from 2018 through 2022. This visual trend indicates that a growing number of research papers are utilizing ROUGE evaluation packages that result in incorrec... | google/gemma-4-31b-it | Rogue_scores | pdfs/Rogue_scores.pdf |
Rogue_scores_cross_g3_p5_17_18_implication | S2 | cross_page | implication | google/gemma-4-26b-a4b-it | How do Table 1 and Table C1 together show that the approach can break down as a fair way to compare models, even when the surrounding text says the score differences are often just configuration noise? Please explain what the tables reveal about the size of those hidden shifts and which kinds of settings make them swin... | {
"input_types": [
"Table 1",
"Table C1"
],
"question_type": "implication",
"visual_subtype": "comparison",
"error_subtype": null,
"scope": null
} | [
{
"sentence": "Table C1 defines the specific experimental configurations used in the study, while Table 1 quantifies the resulting changes in ROUGE scores compared to a baseline configuration.",
"label": "supported",
"role": "Observation",
"rationale": "The Appendix C table lists the experimental co... | [
"pdfs_jpg/Rogue_scores/page_005.jpg",
"pdfs_jpg/Rogue_scores/page_017.jpg",
"pdfs_jpg/Rogue_scores/page_018.jpg"
] | Table C1 defines the specific experimental configurations used in the study, while Table 1 quantifies the resulting changes in ROUGE scores compared to a baseline configuration. Together, they demonstrate that the methodology for comparing models can break down because the differences in scores caused by these configur... | gpt-5.4-mini | Rogue_scores | pdfs/Rogue_scores.pdf |
Rogue_scores_full_cross_adversarial | S1 | full_paper | results_interpretation | z-ai/glm-4.6v | I think the paper’s main takeaway is that ROUGE is broadly unreliable in three ways: many papers don’t report enough details to reproduce scores, evaluation choices can shift scores enough to hurt comparability, and a lot of papers rely on packages that can produce wrong results. Looking at the abstract and then Table ... | {
"input_types": [
"Abstract",
"Table 2",
"Figure 1"
],
"question_type": "cross_adversarial",
"visual_subtype": "comparison",
"error_subtype": "reversed_relationship",
"scope": null
} | [
{
"sentence": "Based strictly on the provided document, your reading is correct.",
"label": "not_a_claim",
"role": "no_rad",
"rationale": "",
"depends_on": [],
"errors": [],
"2d_box": [],
"propagation_type": "none"
},
{
"sentence": "The paper’s main takeaway is that ROUGE sco... | [] | Based strictly on the provided document, your reading is correct. The paper’s main takeaway is that ROUGE scores are broadly unreliable, and the analysis of Table 2 supports the conclusion that major wrapper packages are generally more error-prone than reimplementations.
The abstract explicitly states that the systema... | gpt-5.4-mini | Rogue_scores | pdfs/Rogue_scores.pdf |
Rogue_scores_full_implication | S1 | full_paper | implication | nvidia/nemotron-nano-12b-v2-vl:free | Where do ROUGE scores experience the most significant drops compared to the baseline under different evaluation configurations, and what does the corresponding experiment setup reveal about why those variations occur? | {
"input_types": [
"Table 1",
"Appendix C"
],
"question_type": "implication",
"visual_subtype": "comparison",
"error_subtype": null,
"scope": null
} | [
{
"sentence": "The most significant drops in ROUGE scores compared to the baseline occur under preprocessing configurations, particularly stemming, as well as tokenization configurations like period sentence splitting and NLTK sentence splitting.",
"label": "contradiction",
"role": "Interpretation",
... | [] |
The most significant drops in ROUGE scores compared to the baseline occur under preprocessing configurations, particularly stemming, as well as tokenization configurations like period sentence splitting and NLTK sentence splitting. The sensitivity analysis reveals that when Porter stemming is applied, ROUGE scores d... | gemini-3.5-flash | Rogue_scores | pdfs/Rogue_scores.pdf |
Rogue_scores_page_001_comparison | S4 | single_page | comparison | google/gemma-4-26b-a4b-it | In Figure 1, how do the percentages compare across the different study groups in panel A versus the paper practices in panel B — where are the gaps between items largest, where are they smallest, and how does the spread change overall within each panel? | {
"input_types": [
"Figure 1"
],
"question_type": "comparison",
"visual_subtype": null,
"error_subtype": null,
"scope": null
} | [
{
"sentence": "In Panel A, which displays the reproducibility of different study groups, the data is provided in percentages: 20% for language model evaluations, 39% for psychology studies, 46% for cancer biology studies, 61% for economics studies, and 62% for social science studies.",
"label": "supported",... | [
"pdfs_jpg/Rogue_scores/page_001.jpg"
] | In Panel A, which displays the reproducibility of different study groups, the data is provided in percentages: 20% for language model evaluations, 39% for psychology studies, 46% for cancer biology studies, 61% for economics studies, and 62% for social science studies. The largest gap between items in this panel is 19 ... | gpt-5.4-mini | Rogue_scores | pdfs/Rogue_scores.pdf |
Rogue_scores_page_006_adversarial | S5 | single_page | adversarial | nvidia/nemotron-3-nano-omni-30b-a3b-reasoning:free | I think Table 2 shows that most of the nonstandard ROUGE packages differ from ROUGE-1.5.5 across every score variant and both stemming settings, so the validation mostly finds broad, across-the-board scoring errors rather than anything localized. Is that right, or am I missing a nuance here? | {
"input_types": [
"Table 2",
"Section 4.2"
],
"question_type": "adversarial",
"visual_subtype": "comparison",
"error_subtype": "overgeneralized_scope",
"scope": null
} | [
{
"sentence": "Based strictly on the provided document and Table 2, the statement is incorrect.",
"label": "not_a_claim",
"role": "no_rad",
"rationale": "",
"depends_on": [],
"errors": [],
"2d_box": [],
"propagation_type": "none"
},
{
"sentence": "While the text notes that \"... | [
"pdfs_jpg/Rogue_scores/page_006.jpg"
] | Based strictly on the provided document and Table 2, the statement is incorrect. While the text notes that "all but one package we test has scoring errors," indicating that 18 out of 19 packages have non-zero values in at least one column, only 6 packages differ across *every* score variant (R1, R2, RL) and *both* stem... | gpt-5.4-mini | Rogue_scores | pdfs/Rogue_scores.pdf |
cancers-17-02861-v3_cross_g0_p5_6_9_11_methodological | S2 | cross_page | methodological | gemini-3-flash-preview | How does the clustering scheme shown in the schematic shape the survival patterns seen later, and what does the outcome figure suggest about which cluster-related pattern remains most influential after accounting for the other variables? | {
"input_types": [
"Figure 1",
"Figure 2",
"Section 2.5",
"Section 2.7.1"
],
"question_type": "methodological",
"visual_subtype": "visual_understanding",
"error_subtype": null,
"scope": null
} | [
{
"sentence": "The clustering scheme presented in the document categorizes patients into four distinct transcriptomic groups—C1, C2, C3, and C4—based on their molecular signatures and underlying biological pathways.",
"label": "supported",
"role": "Observation",
"rationale": "Correct. Evidence suppo... | [
"pdfs_jpg/cancers-17-02861-v3/page_005.jpg",
"pdfs_jpg/cancers-17-02861-v3/page_006.jpg",
"pdfs_jpg/cancers-17-02861-v3/page_009.jpg",
"pdfs_jpg/cancers-17-02861-v3/page_011.jpg"
] | The clustering scheme presented in the document categorizes patients into four distinct transcriptomic groups—C1, C2, C3, and C4—based on their molecular signatures and underlying biological pathways. This classification directly shapes subsequent survival patterns by identifying specific molecular drivers that correla... | gpt-5.4-mini | cancers-17-02861-v3-6 | pdfs/cancers-17-02861-v3-6.pdf |
cancers-17-02861-v3_cross_g1_p12_13_16_results_interpretation | S2 | cross_page | results_interpretation | gemma-4-31b-it | What do Figure 3, Figure 4, and the analysis described in Section 3.5 together show about the comparative performance of the various prognostic models and signatures when evaluating different survival metrics? | {
"input_types": [
"Section 3.5",
"Figure 3",
"Figure 4"
],
"question_type": "results_interpretation",
"visual_subtype": "comparison",
"error_subtype": null,
"scope": null
} | [
{
"sentence": "The provided data from Section 3.5, Figure 3, and Figure 4 collectively demonstrate that transcriptomic cluster-based classification provides superior prognostic value for overall survival (OS) compared to both traditional clinical nomograms and other molecular signatures.",
"label": "support... | [
"pdfs_jpg/cancers-17-02861-v3/page_012.jpg",
"pdfs_jpg/cancers-17-02861-v3/page_013.jpg",
"pdfs_jpg/cancers-17-02861-v3/page_016.jpg"
] | The provided data from Section 3.5, Figure 3, and Figure 4 collectively demonstrate that transcriptomic cluster-based classification provides superior prognostic value for overall survival (OS) compared to both traditional clinical nomograms and other molecular signatures. According to the analysis in Section 3.5 and t... | gemini-3-flash-preview | cancers-17-02861-v3-6 | pdfs/cancers-17-02861-v3-6.pdf |
cancers-17-02861-v3_cross_g2_p4_5_17_conceptual | S2 | cross_page | conceptual | gpt-5.4-mini | How does the number of samples that successfully passed quality control for RNA-seq expression analysis determine the population size used for the transcriptomic cluster and survival analyses? | {
"input_types": [
"Section 2.4",
"Section 2.5",
"Section 2.7.1"
],
"question_type": "conceptual",
"visual_subtype": "quantitative",
"error_subtype": null,
"scope": null
} | [
{
"sentence": "The document shows that 102 soft tissue sarcoma samples were initially sent for molecular analysis, but only 75 samples passed the F1RNA quality control and were sufficient for RNA-seq expression analysis.",
"label": "supported",
"role": "Observation",
"rationale": "Evidence supported... | [
"pdfs_jpg/cancers-17-02861-v3/page_004.jpg",
"pdfs_jpg/cancers-17-02861-v3/page_005.jpg",
"pdfs_jpg/cancers-17-02861-v3/page_017.jpg"
] | The document shows that 102 soft tissue sarcoma samples were initially sent for molecular analysis, but only 75 samples passed the F1RNA quality control and were sufficient for RNA-seq expression analysis. One additional sample was then excluded from the RNA-seq expression analysis because it was identified as an outli... | gemma-4-31b-it | cancers-17-02861-v3-6 | pdfs/cancers-17-02861-v3-6.pdf |
cancers-17-02861-v3_full_cross_adversarial | S1 | full_paper | results_interpretation | gemma-4-31b-it | I think I'm starting to appreciate why the authors emphasize that this transcriptomic classification is one of the first molecular-based systems to predict overall survival in STS, as mentioned in the abstract. It's especially interesting to see how it compares to established signatures like CINSARC; if I'm interpretin... | {
"input_types": [
"Abstract",
"Figure 4"
],
"question_type": "cross_adversarial",
"visual_subtype": "comparison",
"error_subtype": "reversed_relationship",
"scope": null
} | [
{
"sentence": "The research study utilized a cohort of 102 high-grade soft tissue sarcoma (STS) samples from 101 patients, comprising the three most common histotypes: dedifferentiated liposarcoma (DDLPS), high-grade leiomyosarcoma (LMS), and undifferentiated pleomorphic sarcoma (UPS).",
"label": "supported... | [] | The research study utilized a cohort of 102 high-grade soft tissue sarcoma (STS) samples from 101 patients, comprising the three most common histotypes: dedifferentiated liposarcoma (DDLPS), high-grade leiomyosarcoma (LMS), and undifferentiated pleomorphic sarcoma (UPS). By employing DNA sequencing (DNA-seq) and RNA se... | gemini-3-flash-preview | cancers-17-02861-v3-6 | pdfs/cancers-17-02861-v3-6.pdf |
cancers-17-02861-v3_full_implication | S1 | full_paper | implication | gemini-3-flash-preview | Where does the new transcriptomic classification fall behind the comparison models, and what do the more detailed subtype breakdowns suggest about why those weaker cases happen? | {
"input_types": [
"Figure 3",
"Figure 5"
],
"question_type": "implication",
"visual_subtype": "comparison",
"error_subtype": null,
"scope": null
} | [
{
"sentence": "The new transcriptomic classification system falls slightly behind the comparison models specifically regarding the estimation of disease-free survival (DFS) within the validation cohort.",
"label": "supported",
"role": "Interpretation",
"rationale": "Correct. This arrives from the de... | [] | The new transcriptomic classification system falls slightly behind the comparison models specifically regarding the estimation of disease-free survival (DFS) within the validation cohort. While the transcriptomic approach consistently outperforms the molecular-based CINSARC model, it is marginally surpassed by the clin... | gpt-5.4-mini | cancers-17-02861-v3-6 | pdfs/cancers-17-02861-v3-6.pdf |
cancers-17-02861-v3_full_methodological | S1 | full_paper | methodological | gemma-4-31b-it | How does the clustering and validation setup in the methods connect to the survival patterns reported later, and what in the results suggests that this design choice is what drives the prognostic signal? | {
"input_types": [
"Figure 1",
"Figure 3",
"Figure 4"
],
"question_type": "methodological",
"visual_subtype": "comparison",
"error_subtype": null,
"scope": null
} | [
{
"sentence": "The connection between the methods and the reported survival patterns begins with the use of unsupervised machine learning on RNA-seq data from a study cohort of 74 samples.",
"label": "supported",
"role": "Observation",
"rationale": "The specific connection between RNA-seq data and m... | [] | The connection between the methods and the reported survival patterns begins with the use of unsupervised machine learning on RNA-seq data from a study cohort of 74 samples. By employing consensus clustering and the Elbow method, the researchers identified four distinct transcriptomic clusters (TCs). These clusters wer... | gpt-5.4-mini | cancers-17-02861-v3-6 | pdfs/cancers-17-02861-v3-6.pdf |
cancers-17-02861-v3_full_results_interpretation | S1 | full_paper | results_interpretation | gpt-5.4-mini | What do the survival curves in Figure 4 and the detailed gene distributions in Table A1 jointly reveal about the molecular drivers behind the performance of this new classification system across different patient subgroups? | {
"input_types": [
"Figure 4",
"Table A1",
"Section 3.8",
"Appendix A"
],
"question_type": "results_interpretation",
"visual_subtype": "comparison",
"error_subtype": null,
"scope": null
} | [
{
"sentence": "Taken together, Figure 4 and Table A1 suggest that the new transcriptomic classification performs well because it captures biologically distinct disease programs that translate into different survival outcomes, rather than merely reproducing the conventional histopathological labels.",
"label... | [] | Taken together, Figure 4 and Table A1 suggest that the new transcriptomic classification performs well because it captures biologically distinct disease programs that translate into different survival outcomes, rather than merely reproducing the conventional histopathological labels. Figure 4 shows that, in the TCGA-SA... | gemini-3-flash-preview | cancers-17-02861-v3-6 | pdfs/cancers-17-02861-v3-6.pdf |
cancers-17-02861-v3_page_013_quantitative | S4 | single_page | quantitative | gemma-4-31b-it | In Figure 3, how do the C-index values spread out across the different prognostic models in panels a and b — which models are highest and lowest in each panel, and what overall pattern do the values show when you compare the standalone models versus the combinations? | {
"input_types": [
"Figure 3"
],
"question_type": "quantitative",
"visual_subtype": null,
"error_subtype": null,
"scope": null
} | [
{
"sentence": "In panel a, the C-index values range from a low of 0.62 to a high of 0.7.",
"label": "supported",
"role": "Observation",
"rationale": "Correct the C-index range from a low of 0.62 to 0.7",
"depends_on": [],
"errors": [],
"2d_box": [
{
"page_id": 13,
"... | [
"pdfs_jpg/cancers-17-02861-v3/page_013.jpg"
] | In panel a, the C-index values range from a low of 0.62 to a high of 0.7. The highest performing model in this panel is TC + Age, which reaches a C-index of 0.7, followed by TC + SARCULATOR at 0.69. The lowest performing model in panel a is SARCULATOR, with a value of 0.62, while TC alone shows a C-index of 0.63.
In p... | gpt-5.4-mini | cancers-17-02861-v3-6 | pdfs/cancers-17-02861-v3-6.pdf |
cancers-17-02861-v3_page_017_adversarial | S5 | single_page | adversarial | gemma-4-31b-it | I think Figure 5 shows that Cluster 2 has the highest number of tier 2 actionable variants, with Cluster 3 next, and that Cluster 3 is the one with the strongest enrichment at tiers 3 and 4. Also, it looks like the actionability evidence breakdown is mostly dominated by curated variants across the clusters. Correct me ... | {
"input_types": [
"Figure 5",
"Figure 5 caption"
],
"question_type": "adversarial",
"visual_subtype": "comparison",
"error_subtype": "reversed_relationship",
"scope": null
} | [
{
"sentence": "Your interpretation of the provided document is correct.",
"label": "not_a_claim",
"role": null,
"rationale": "",
"depends_on": [],
"errors": [],
"2d_box": [],
"propagation_type": "none"
},
{
"sentence": "According to the text, Cluster 2 displayed the highest n... | [
"pdfs_jpg/cancers-17-02861-v3/page_017.jpg"
] | Your interpretation of the provided document is correct. According to the text, Cluster 2 displayed the highest number of gene variants classified with an ESCAT evidence tier 2, totaling 15 variants. Cluster 3 followed as the second highest for this tier, with 14 variants.
The document further confirms that Cluster 3 ... | gpt-5.4-mini | cancers-17-02861-v3-6 | pdfs/cancers-17-02861-v3-6.pdf |
prismm-bench_cross_g0_p2_8_10_results_interpretation | S2 | cross_page | results_interpretation | gemma-4-31b-it | What do Table 1 and Table 3 together show about how model performance changes when the answer format becomes more structured, and how that effect differs between easier and harder context settings? | {
"input_types": [
"Table 1",
"Table 3",
"Section 4.2",
"Section 4.3"
],
"question_type": "results_interpretation",
"visual_subtype": "comparison",
"error_subtype": null,
"scope": "multi"
} | [
{
"sentence": "Table 3 demonstrates that the choice of answer format significantly impacts model performance, particularly when models are evaluated without context for the Ident task.",
"label": "supported",
"role": "Interpretation",
"rationale": "Correct interpretation of the table",
"depends_... | [
"pdfs_jpg/prismm-bench/page_002.jpg",
"pdfs_jpg/prismm-bench/page_008.jpg",
"pdfs_jpg/prismm-bench/page_010.jpg"
] | Table 3 demonstrates that the choice of answer format significantly impacts model performance, particularly when models are evaluated without context for the Ident task. When using natural language, models achieve inflated accuracies by exploiting linguistic regularities and surface cues. For example, Gemini 2.5 Pro ac... | gpt-5.4-mini | prismm-bench_final | pdfs/prismm-bench_final.pdf |
prismm-bench_cross_g2_p6_31_35_conceptual | S2 | cross_page | conceptual | gemini-3-flash-preview | How does converting the answer choices into a structured format help determine what needs to be changed to resolve an inconsistency, instead of letting the model rely on surface linguistic cues? | {
"input_types": [
"Section 3.3",
"Figure 12",
"Figure 17"
],
"question_type": "conceptual",
"visual_subtype": "visual_understanding",
"error_subtype": null,
"scope": "multi"
} | [
{
"sentence": "Converting natural language answer choices into a structured format, specifically the Target-Action JSON format, helps identify necessary changes for resolving inconsistencies by stripping away superficial linguistic cues that models often use as shortcuts.",
"label": "contradiction",
"ro... | [
"pdfs_jpg/prismm-bench/page_006.jpg",
"pdfs_jpg/prismm-bench/page_031.jpg",
"pdfs_jpg/prismm-bench/page_035.jpg"
] | Converting natural language answer choices into a structured format, specifically the Target-Action JSON format, helps identify necessary changes for resolving inconsistencies by stripping away superficial linguistic cues that models often use as shortcuts. During pilot testing, researchers observed that models could a... | gpt-5.4-mini | prismm-bench_final | pdfs/prismm-bench_final.pdf |
prismm-bench_cross_g3_p9_25_39_implication | S2 | cross_page | implication | gpt-5.4-mini | How does the performance of the models break down when switching from natural language to a structured output format in the absence of context? | {
"input_types": [
"Table 2",
"Section E.1",
"Figure 21"
],
"question_type": "implication",
"visual_subtype": "quantitative",
"error_subtype": null,
"scope": "multi"
} | [
{
"sentence": "The document shows that switching from natural language to a structured JSON-style output format substantially reduces apparent performance when there is no context.",
"label": "supported",
"role": "Interpretation",
"rationale": "Can be directly inferred from the table.",
"depends... | [
"pdfs_jpg/prismm-bench/page_009.jpg",
"pdfs_jpg/prismm-bench/page_025.jpg",
"pdfs_jpg/prismm-bench/page_039.jpg"
] | The document shows that switching from natural language to a structured JSON-style output format substantially reduces apparent performance when there is no context. In the user study, the natural language condition without context was much stronger than the JSON condition without context for every model listed. For ex... | gemma-4-31b-it | prismm-bench_final | pdfs/prismm-bench_final.pdf |
prismm-bench_full_implication | S1 | full_paper | implication | gpt-5.4-mini | Which tasks within the benchmark show the most significant performance gaps for LMMs, and what does the overall distribution of inconsistency categories suggest about the focus of the evaluation? | {
"input_types": [
"Table 1",
"Figure 9"
],
"question_type": "implication",
"visual_subtype": "comparison",
"error_subtype": null,
"scope": "full"
} | [
{
"sentence": "The benchmark shows the largest performance gaps in the Inconsistency Remedy task and in the broader long-context settings.",
"label": "nei",
"role": "Observation",
"rationale": "Rest is too wide, given that there are multiple context settings.",
"depends_on": [],
"errors": [
... | [] | The benchmark shows the largest performance gaps in the Inconsistency Remedy task and in the broader long-context settings. The paper states that Remedy scores are consistently lower than Ident, because proposing a correction requires deeper reasoning than simply spotting the inconsistency. It also reports that perform... | gemini-3-flash-preview | prismm-bench_final | pdfs/prismm-bench_final.pdf |
prismm-bench_full_results_interpretation | S1 | full_paper | results_interpretation | gemini-3-flash-preview | Looking at Figure 2 and Figure 5 together, what do the results and the later analysis suggest about where the benchmark is hardest and what kind of reasoning or context actually helps models cope with it? | {
"input_types": [
"Figure 2",
"Figure 5",
"Section 4.3",
"Appendix C.1"
],
"question_type": "results_interpretation",
"visual_subtype": "comparison",
"error_subtype": null,
"scope": "full"
} | [
{
"sentence": "The results provided in Table 1 and the accompanying analysis in the text suggest that the PRISMM-Bench benchmark is hardest for models when they must navigate expanded context levels, such as the Page and Document settings.",
"label": "supported",
"role": "Interpretation",
"rationale... | [] | The results provided in Table 1 and the accompanying analysis in the text suggest that the PRISMM-Bench benchmark is hardest for models when they must navigate expanded context levels, such as the Page and Document settings. Accuracy consistently peaks in the Focused setting but often degrades toward random chance as t... | gpt-5.4-mini | prismm-bench_final | pdfs/prismm-bench_final.pdf |
prismm-bench_page_001_visual_understanding | S4 | single_page | visual_understanding | gpt-5.4-mini | Can you describe the layout and components of Figure 1? Identify the different sections such as the boxes on the left and right and the callouts from the central paper graphic, explaining what each represents according to its labels and how they are visually linked together. | {
"input_types": [
"Figure 1"
],
"question_type": "visual_understanding",
"visual_subtype": null,
"error_subtype": null,
"scope": "single"
} | [
{
"sentence": "Figure 1 is arranged as a three-part visual: a large annotated panel on the left, a separate question-and-answer box on the right, and a central paper-style graphic inside the left panel that is connected to several callouts and zoomed highlights.",
"label": "contradiction",
"role": "Obse... | [
"pdfs_jpg/prismm-bench/page_001.jpg"
] | Figure 1 is arranged as a three-part visual: a large annotated panel on the left, a separate question-and-answer box on the right, and a central paper-style graphic inside the left panel that is connected to several callouts and zoomed highlights. The overall caption beneath the figure explains the purpose of the figur... | gemini-3-flash-preview | prismm-bench_final | pdfs/prismm-bench_final.pdf |
prismm-bench_page_005_visual_understanding | S4 | single_page | visual_understanding | gemma-4-31b-it | Can you walk me through the full pipeline shown in Figure 2 — what are the six steps, what icons or boxes represent each stage, and how do the arrows connect the review sourcing, filtering, manual annotation, task generation, verification, and debiasing parts? | {
"input_types": [
"Figure 2"
],
"question_type": "visual_understanding",
"visual_subtype": null,
"error_subtype": null,
"scope": "single"
} | [
{
"sentence": "The pipeline of PRISMM-Bench consists of six sequential steps designed to create benchmark tasks for identifying inconsistencies in scientific papers.",
"label": "supported",
"role": "Interpretation",
"rationale": "6 steps can be directly seen in the figure",
"depends_on": [
... | [
"pdfs_jpg/prismm-bench/page_005.jpg"
] | The pipeline of PRISMM-Bench consists of six sequential steps designed to create benchmark tasks for identifying inconsistencies in scientific papers. The process begins with Step 1, Review Sourcing via OpenReview API, which is represented by a sequence of icons including a world wide web globe, a cloud download symbol... | gpt-5.4-mini | prismm-bench_final | pdfs/prismm-bench_final.pdf |
prismm-bench_page_008_quantitative | S4 | single_page | quantitative | gpt-5.4-mini | Looking at the 'Average' column for the different categories of open-weight models (Small, Medium, and Large), how does the overall performance vary across these size categories? | {
"input_types": [
"Table 1"
],
"question_type": "quantitative",
"visual_subtype": null,
"error_subtype": null,
"scope": "single"
} | [
{
"sentence": "The document shows a clear upward trend in the Average column as model size increases from Small to Medium to Large open-weight models, though the improvement is not perfectly uniform across every individual model.",
"label": "supported",
"role": "Interpretation",
"rationale": "Can be... | [
"pdfs_jpg/prismm-bench/page_008.jpg"
] | The document shows a clear upward trend in the Average column as model size increases from Small to Medium to Large open-weight models, though the improvement is not perfectly uniform across every individual model. In the Small category, the Average values range from 27.8 to 37.7, with most models clustering in the hig... | gemma-4-31b-it | prismm-bench_final | pdfs/prismm-bench_final.pdf |
prismm-bench_page_027_adversarial | S5 | single_page | adversarial | gpt-5.4-mini | I noticed a naming mismatch between the sections; the textual element describes gamma as the hyperparameter for balancing the trade-off between fitting data and distribution adherence, whereas Table 10 lists this as 'Alpha, alpha'. Looking at the settings, it appears the researchers prioritized this trade-off more heav... | {
"input_types": [
"Textual element",
"Table 10: Hyperparameters"
],
"question_type": "adversarial",
"visual_subtype": "quantitative",
"error_subtype": "reversed_relationship",
"scope": "single"
} | [
{
"sentence": "The document does not support the conclusion that the researchers prioritized that trade-off more heavily for the Energy dataset than for the Stocks dataset.",
"label": "supported",
"role": "Interpretation",
"rationale": "",
"depends_on": [
"S9"
],
"errors": [],
... | [
"pdfs_jpg/prismm-bench/page_027.jpg"
] | The document does not support the conclusion that the researchers prioritized that trade-off more heavily for the Energy dataset than for the Stocks dataset. The textual element states that γ is a hyperparameter controlling the strength of the gradient guidance and that it balances the trade-off between fitting the obs... | gemini-3-flash-preview | prismm-bench_final | pdfs/prismm-bench_final.pdf |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.