Pith. sign in

REVIEW 2 major objections 5 minor 34 references

Representing Visual Evidence for Item Difficulty Prediction: Visual Textualization and Image-Native Modeling

T0 review · 2 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read How visual evidence is represented—as generated text or as the original image—is a consequential choice for predicting item difficulty, and the two visual interfaces are competitive with no reliable ordering between them.

desk verdict A careful, honest comparison of text, visual textualization, and image-native interfaces for difficulty prediction; the main caveat is missing IRT fit diagnostics for the Rasch labels, but the relative claims are well-hedged and hold up conditional on that target. read the letter →

arxiv 2608.04554 v1 pith:JZLC5KJR submitted 2026-08-05 cs.CL cs.CV

Han Chen , Ming Li , Hong Jiao , Tianyi Zhou This is my paper
classification cs.CLcs.CV
keywords itemdifficultypredictionvisualtextualizationimage-nativemodelingvision-languagemodelsRaschmodelparameter-efficientfine-tuningEedidatasetregression
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

When a math question contains a diagram, a model predicting how hard students will find it needs to use that visual evidence somehow. This paper compares three interfaces for representing that evidence: the question text alone, the text plus a machine-generated verbal description of the visual component, and the original image fed directly to a vision-language model. On 725 Eedi mathematics items with Rasch-calibrated difficulty targets, the two visual interfaces achieve the lowest error point estimates, and the paper finds no reliable ordering between them. The practical conclusion is that textualizing images is not the only workable pipeline: image-native modeling is competitive, but only when the vision-language model is adapted with a sufficiently broad parameter-efficient update. Because the choice affects both error patterns and computational cost, visual-evidence representation itself is a modeling decision that should be evaluated explicitly.

What carries the argument

The central objects are the three item representations: question text $Q$ (a manually verified transcription of stem and choices), the generated visual description $D$ (produced once by a VLM textualizer), and the original item image $I$. The mechanism that carries the argument is supervised task adaptation to the continuous Rasch difficulty target $\beta_i$: text LLMs use attention-only LoRA with a regression head, and image-native VLMs use attention+MLP LoRA with a regression head on pooled hidden states. Matched experiments vary only one factor at a time — input ($Q$ vs. $Q{+}D$) with model and recipe fixed, or adaptation scope (attention vs. attention+MLP) with input fixed — so the differences are attributable to representation and adaptation breadth rather than to model choice.

What would settle it

Run the same three interfaces on a fresh set of, say, 200 newly administered math items with diagrams, fit Rasch difficulties from the new student responses, and compare seed-ensemble RMSE with paired bootstrap intervals. If the image-native system's RMSE reliably exceeds the visual-textualization system's (interval excluding zero), the paper's no-domination claim is refuted; if the ordering flips under an alternative item split of the same data, the conclusions are split-specific rather than general.

Watch

Extended reading notes

Core claim

The paper's central claim is that how visual evidence is represented is a consequential modeling choice for response-calibrated item difficulty prediction, and that neither of the two visual interfaces dominates. With a fixed split of 580 training and 145 test items, the strongest systems using question text alone, visual textualization ($Q$ plus a generated description $D$), and image-native modeling ($I$ plus $Q$) reach RMSE values of $0.517$, $0.506$, and $0.497$, respectively, but the paired bootstrap intervals among these leaders include zero. Matched comparisons hold the downstream model fixed: adding the Qwen2.5-VL-7B description $D_V$ lowers RMSE point estimates relative to text alone for all five text LLMs, and extending LoRA adaptation from attention projections to attention+MLP lowers seed-ensemble RMSE for all ten vision-language models. Test-time interventions that blank or shuffle the paired image sharply degrade the image-native predictor, showing it depends on the full item image, though this does not isolate the additional visual component. The paper concludes that visual textualization should not be treated as the only practical interface.

Load-bearing premise

The comparison inherits its ground truth from a one-parameter Rasch model fit to student responses on the Eedi items; if those difficulty estimates are systematically biased or unstable, every reported advantage could be an artifact of the labels, even though the relative model comparisons remain internally consistent.

Editorial extensions

If this is right

  • Practitioners gain a validated second interface: image-native VLM regression with attention+MLP LoRA is a competitive alternative to textualization, not a fallback.
  • The textualizer is part of the modeling choice: Qwen2.5-VL-7B descriptions beat GPT-5.5 descriptions for four of five downstream models.
  • Adaptation breadth matters for image-native prediction: attention+MLP LoRA improves all ten VLMs, suggesting attention-only updates leave part of the task mapping fixed.
  • The two visual interfaces make complementary item-level errors (74 image-native wins vs. 71 textualization wins), so a fixed average reaches a lower point estimate (0.478 RMSE) even though its intervals include zero.
  • Workflow cost differs sharply: textualization caches descriptions and averages 191 seconds per run versus 1,602 seconds for image-native processing, so the choice has operational consequences.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because the study covers only 725 English-language math items from one source, the natural out-of-sample test is whether the no-domination conclusion replicates on other curricula, languages, and item types; the paper's own limitations section acknowledges this scope.
  • The image-native system's largest aggregate gain occurs on items without an identified visual component, hinting that the image contributes through rendered equations and layout, not only explicit diagrams; cropping experiments could test this directly.
  • The observed complementarity suggests a learned fusion of textualized and image-native signals might exceed either interface, but the paper only evaluates a fixed average, leaving trained fusion as an open extension.
  • A practical decision rule implied (but not stated) by the data is to evaluate both interfaces on a small validation set before committing to a pipeline, since the accuracy gap is statistically uncertain and the cost gap is large.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 5 minor

Summary. The paper asks how visual evidence should be represented for content-based item difficulty prediction, comparing three interfaces: question text alone (Q), visual textualization (Q+D, where D is a generated language description of the additional visual component), and image-native modeling (I+Q, where the original item image is retained). Using 725 Eedi mathematics items with difficulty targets estimated by a one-parameter Rasch model from student responses, the authors train and evaluate 22 text and vision models with 2B–8B parameters under task-adapted regression, frozen-feature regression, scalar generation, and late fusion. Headline results show the strongest Q, Q+D, and I+Q systems reach 0.517, 0.506, and 0.497 RMSE, respectively, with the two visual interfaces having the lowest point estimates but not reliably orderable. Matched analyses find that adding a Qwen2.5-VL-7B description lowers RMSE point estimates for all five text LLMs, and that attention+MLP LoRA adaptation outperforms attention-only LoRA for all ten VLMs. Test-time image interventions show the image-native predictor depends on the paired full-item image but do not isolate the additional visual component. The authors conclude that visual textualization should not be treated as the only practical interface and that image-native modeling is a competitive alternative whose effectiveness depends on VLM adaptation.

Significance. If the results hold, the paper provides a systematic, head-to-head comparison of two practical visual interfaces for a real educational task, supported by a broad model sweep and careful uncertainty quantification. The experimental design is a clear strength: matched comparisons hold models and recipes fixed, paired bootstrap confidence intervals are reported, the authors honestly state that the interface leaders cannot be reliably ordered, and a label-standard-error sensitivity analysis is included. The paper also ships a project page with code and gives a nuanced interpretation of its test-time interventions. The main reservation concerns the external validity of the Rasch-based difficulty labels, which form the common target for all compared systems; this issue is discussable and fixable but currently leaves the central claim slightly undersupported.

major comments (2)
  1. [3.2, Eq. (1), B.8, Table 9] The prediction target is the Rasch difficulty parameter beta_i estimated from student responses, and every headline RMSE and matched comparison is evaluated against this target. The reported standard errors (mean 0.061) and the trimming sensitivity analysis in Table 9 address the noisiness of the estimates but do not address potential systematic misspecification of the one-parameter logistic model. Because the central claim is about 'response-calibrated item difficulty prediction,' the validity of beta_i as a measure of difficulty is load-bearing. I ask the authors to add item-level fit diagnostics (e.g., infit/outfit statistics or a likelihood-ratio test against a two-parameter model) and a robustness check using an alternative IRT specification (e.g., 2PL or 3PL on a subset of items) to confirm that the interface ordering is not an artifact of the 1PL target. Without this, the abstract and conclusion should be limited to prediction of a specific 1PL estimate rather than item difficulty per se.
  2. [5.5, C.7, C.9] The test-time image interventions show that the image-native predictor depends on the paired full-item image, but they do not isolate the additional visual component. The appendix evidence (Table 16) further indicates that the image-native system's largest aggregate gain over Q occurs on the 69 items without an identified visual component (RMSE 0.4478 vs. 0.5034 for Q), whereas on the 76 items with a visual component the image-native point estimate (0.5266) is worse than visual textualization (0.4942). This dissociation is only discussed in the appendix and complicates the interpretation of the image-native advantage as evidence about visual-evidence representation. I recommend that the main text explicitly acknowledge this pattern and, if possible, include an analysis that separates the contribution of the additional visual component from the contribution of having the full rendered image (e.g., by comparing performance on items with and without identified visual components while controlling for text rendering). Without this, the claim that the two visual interfaces are 'partially complementary' in a way that informs visual-evidence representation remains underspecified.
minor comments (5)
  1. [Table 1] Several entries in Table 1 contain rendering artifacts such as 'T ext encoder', 'Q+DF rozen', and 'Q+D V'; these should be cleaned up before publication.
  2. [3.3] The sentence about the 18 items whose answer choices are images is dense; consider clarifying that Q includes brief image descriptions for these items and that this is an intentional, acknowledged exception to the text-only definition.
  3. [B.8] The sentence 'Absolute RMSE increases after trimming because the high-SE items are not the items with the largest model errors; this does not indicate worse labels after trimming' is potentially confusing; rephrasing it to explain that removing the noisiest labels changes the test composition would improve readability.
  4. [Abstract] The abstract uses 'Open-VLM textualization' whereas the body uses 'Qwen2.5-VL-7B textualization' or 'DVL7B'; use consistent terminology throughout.
  5. [5.4] When stating the confidence interval [−0.0314, 0.0214] for the text control, make explicit that this is the seed-ensemble RMSE difference for the Llama-3.1-8B Q-only control, not for the VLM comparisons reported just above.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the difficulty targets are Rasch parameters estimated from external student responses, descriptions are generated without label access, and the central comparisons hold models fixed.

full rationale

The paper's central claim is an empirical comparison of three representations for supervised difficulty regression. The targets β_i come from Eq. 1, a Rasch model fit to Eedi student response records, which is external to all compared systems and not defined in terms of any model output. The visual descriptions D are generated by GPT-5.5 or Qwen2.5-VL-7B once, and Section 4.3 explicitly states they "are generated once and are not optimized using difficulty labels." The matched comparisons in Sections 5.3 and 5.4 hold the downstream text model or VLM and training recipe fixed and vary only the input representation or adaptation scope, so the measured RMSE differences are not forced by construction. The image-intervention experiments in Section 5.5 are empirical checks, not derivations, and the paper itself notes they do not isolate the additional visual component. Self-citations (e.g., Li et al., 2025b; Chen et al., 2026) appear only in related work and do not carry the load of any central result; there is no imported uniqueness theorem or ansatz justified solely by authorial prior work. The reported bootstrap intervals and sensitivity analysis explicitly qualify the inferential strength of the point estimates. Accordingly, no step in the claimed derivation chain reduces to its own inputs.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The ledger is light because this is an empirical benchmark paper: the prediction target (Rasch difficulty) is estimated from student responses and is external to all compared systems. The free parameters are disclosed experimental choices: the 200-response retention filter, the cross-validated family recipes (Table 5), the two LoRA scopes, and the description-generation prompt and token cap. None of these encodes the central claim. The axioms are the psychometric validity of the 1PL labels, the GPT-5.5-based visual-component partition, the faithfulness of the manually inspected Q transcripts, and the transferability of the fixed split. No invented entities are introduced; the blank and shuffled images are perturbations of existing inputs rather than new objects.

free parameters (4)
  • Minimum response count for item retention = 200 student responses per item
    Section 3.1 filter reducing the Eedi pool to 725 items; disclosed and tied to label reliability, but it defines the benchmark and is a hand-chosen threshold.
  • Family-level training recipes = LoRA rank 8 or 16; learning rate 2e-4 or 5e-5; 5-10 epochs (Table 5)
    Selected by cross-validation on training folds (Appendix B.1); fixed per model family before final evaluation. They influence all reported numbers but do not encode the representation comparison.
  • LoRA adaptation scope = attention-only versus attention+MLP projections
    The manipulated variable in Section 5.4; chosen by hand as two discrete scopes, not fitted. The +MLP gain is the paper's main image-native finding.
  • Description generation limits = 128 new tokens; 1-3 sentence instruction for DVL7B
    Appendix B.2; hand-chosen prompt and length cap constrain what D can express, hence partially determine textualization's ceiling.
assumptions (4)
  • domain assumption A one-parameter logistic (Rasch) model applied to Eedi student correctness records yields item difficulty estimates that are valid, comparable prediction targets.
    Section 3.2 defines beta_i via Eq. 1 from student response records; the paper's ground truth presupposes that a 1PL IRT model fits the diagnostic question data. No model-fit or local-independence diagnostics are reported for the filtered 725-item subset.
  • ad hoc to paper GPT-5.5 correctly identifies which items carry an additional visual component, and the generated descriptions D preserve the solution-relevant visual relations.
    Section 3.3 and Appendix B.2 rely on gpt-5.5 extraction for the has_figure decision and figure_description; 55.3% of items are labeled as having visual components, and no independent human audit of these labels is reported. The visual-textualization comparison depends on this partition because D is empty for the other items.
  • ad hoc to paper The manually verified transcription Q is faithful to the original item images.
    Section 3.3 treats Q as manually verified, but Appendix B.2 states no edit-level audit trail was retained, so the human correction rate is unquantified. All three interfaces receive Q, so errors in Q would propagate to every comparison.
  • domain assumption Supervised tuning on the 580-item training split transfers to the 145 held-out items, and the fixed split's results represent the task's difficulty-prediction performance.
    Section 5.1 fixes one item-level split and selects recipes only from training folds; the paper does not quantify variance across alternative splits or hyperparameter-selection procedures, as acknowledged in Appendix B.7.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Representing Visual Evidence for Item Difficulty Prediction: Visual Textualization and Image-Native Modeling." pith.science (2026). https://pith.science/paper/JZLC5KJR

@misc{pith2026260804554,
  author       = {Pith},
  title        = {Pith review of: Representing Visual Evidence for Item Difficulty Prediction: Visual Textualization and Image-Native Modeling},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JZLC5KJR}},
  note         = {Machine review of arXiv:2608.04554}
}
read the original abstract

Predicting item difficulty from content can provide an initial estimate for newly developed questions before sufficient student responses are available. Existing approaches typically represent the question stem and answer choices as text. When mathematics items contain visual components, a common pipeline first textualizes that evidence and then applies a text predictor. We ask: how should visual evidence be represented for item difficulty prediction? We compare question text alone, visual textualization, which expresses visual evidence in language, and image-native modeling, which retains the original image. Using Eedi items with difficulty calibrated from student responses, we train large language models (LLMs) and vision-language models (VLMs) directly for difficulty regression. Both visual interfaces achieve the lowest point estimates, although the leading systems cannot be reliably ordered. Open-VLM textualization yields lower RMSE point estimates for all evaluated LLMs, while broader adaptation does so for all image-native VLMs. Test-time interventions show dependence on the paired full-item image, but do not isolate the additional visual component. The two visual interfaces also make partially complementary item-level errors and differ substantially in computational workflow. Thus, textualization should not be treated as the only practical interface: image-native modeling is a competitive alternative whose effectiveness depends on how the VLM is adapted.

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

34 extracted references · 19 canonical work pages

  1. [2]

    Han Chen, Ming Li, Chenguang Wang, Yijun Liang, Dawei Zhou, Hong Jiao, and Tianyi Zhou. LLMs struggle to measure what distinguishes students of different proficiency levels: A study of item discrimination in reading comprehension assessment.arXiv preprint arXiv:2606.18709,

    arXiv

  2. [3]

    Blank-image predictions are constant within each image-only seed, so their Spearman correlation is undefined

    Trained input Test image RMSE↓Spearman↑ I+Q, attention Original0.5233±0.0077 0.7649±0.0049 Blank1.0746±0.1299 0.6492±0.0080 Shuffled0.9552±0.0044 0.1121±0.0093 I+Q, attention+MLP Original0.4966±0.0062 0.7853±0.0079 Blank0.9447±0.0475 0.6788±0.0069 Shuffled0.9643±0.0059 0.1118±0.0065 I, attention Original0.5210±0.0030 0.7667±0.0063 Blank1.2629±0.2093undefi...

    work page arXiv

  3. [4]

    Qwen-VL and PaliGemma use their packaged image preprocessing; InternVL uses a448× 448image transform and the model’s image-context tokens

    Images are processed with each model family’s native processor. Qwen-VL and PaliGemma use their packaged image preprocessing; InternVL uses a448× 448image transform and the model’s image-context tokens. The pooled representation is taken from the language backbone’s final hidden state. The final image-native adaptation targets the language backbone’s atte...

    work page arXiv 2021

  4. [7]

    The llama 3 herd of models.arXiv preprint arXiv:2407.21783,

    Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, et al. The llama 3 herd of models.arXiv preprint arXiv:2407.21783,

    arXiv

  5. [10]

    Jump-starting item parameters for adaptive language tests

    Arya D McCarthy, Kevin P Yancey, Geoffrey T LaFlair, Jesse Egbert, Manqian Liao, and Burr Settles. Jump-starting item parameters for adaptive language tests. InProceedings of the 2021 conference on empirical methods in natural language processing, pages 883–899,

    work page 2021

  6. [14]

    Qwen2.5 technical report.arXiv preprint arXiv:2412.15115,

    11 Qwen Team. Qwen2.5 technical report.arXiv preprint arXiv:2412.15115,

    arXiv

  7. [15]

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al

    doi: 10.48550/arXiv.2412.15115. Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. InInternational conference on machine learning, pages 8748–8763. PmLR,

    arXiv doi:10.48550/arxiv.2412.15115

  8. [16]

    Unibucllm: Harnessing llms for automated prediction of item difficulty and response time for multiple-choice questions

    Ana-Cristina Rogoz and Radu Tudor Ionescu. Unibucllm: Harnessing llms for automated prediction of item difficulty and response time for multiple-choice questions. InProceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024), pages 493–502,

    work page 2024

Show all 34 references
  1. [17]

    Andreas Säuberli, Diego Frassinelli, and Barbara Plank. Do llms give psychometrically plausible responses in educational assessments? InProceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025), pages 266–278,

    2025

  2. [18]

    Paligemma 2: A family of versatile vlms for transfer.arXiv preprint arXiv:2412.03555,

    Andreas Steiner, André Susano Pinto, Michael Tschannen, Daniel Keysers, Xiao Wang, Yonatan Bitton, Alexey Gritsenko, Matthias Minderer, Anthony Sherbondy, Shangbang Long, et al. Paligemma 2: A family of versatile vlms for transfer.arXiv preprint arXiv:2412.03555,

    arXiv

  3. [20]

    Large language model-based pipeline for item difficulty and response time estimation for educational assessments

    Hariram Veeramani, Surendrabikram Thapa, Natarajan Balaji Shankar, and Abeer Alwan. Large language model-based pipeline for item difficulty and response time estimation for educational assessments. InProceedings of the 19th Workshop on Innovative Use of NLP for Building Educat...

    2024

  4. [21]

    Cognitive episodes in llm reasoning traces enable interpretable human item difficulty prediction.arXiv preprint arXiv:2606.28186,

    Chenguang Wang, Ming Li, Xinyue Zeng, Zhuochun Li, Hong Jiao, Tianyi Zhou, and Dawei Zhou. Cognitive episodes in llm reasoning traces enable interpretable human item difficulty prediction.arXiv preprint arXiv:2606.28186,

    arXiv

  5. [22]

    Internvl3

    Weiyun Wang, Zhangwei Gao, Lixin Gu, Hengjun Pu, Long Cui, Xingguang Wei, Zhaoyang Liu, Linglin Jing, Shenglong Ye, Jie Shao, et al. Internvl3. 5: Advancing open-source multimodal models in versatility, reasoning, and efficiency. arXiv preprint arXiv:2508.18265, 2025a. Z Wang,...

    2020 arXiv

  6. [25]

    Qwen3 technical report.arXiv preprint arXiv:2505.09388,

    An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, et al. Qwen3 technical report.arXiv preprint arXiv:2505.09388,

    arXiv

  7. [26]

    Towards valid student simulation with large language models.arXiv preprint arXiv:2601.05473,

    12 Zhihao Yuan, Yunze Xiao, Ming Li, Weihao Xuan, Richard Tong, Mona Diab, and Tom Mitchell. Towards valid student simulation with large language models.arXiv preprint arXiv:2601.05473,

  8. [27]

    Internvl3: Exploring advanced training and test-time recipes for open-source multimodal models.arXiv preprint arXiv:2504.10479,

    Jinguo Zhu, Weiyun Wang, Zhe Chen, Zhaoyang Liu, Shenglong Ye, Lixin Gu, Hao Tian, Yuchen Duan, Weijie Su, Jie Shao, et al. Internvl3: Exploring advanced training and test-time recipes for open-source multimodal models.arXiv preprint arXiv:2504.10479,

    arXiv

  9. [28]

    3 3.2 Difficulty Labels

    13 Contents 1 Introduction 1 2 Related Work 3 3 Task and Data 3 3.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 3.2 Difficulty Labels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ....

    1995

  10. [29]

    Visual textualization and image-native modeling impose different representational bottlenecks

    and Qwen2.5-VL (Bai et al., 2025b) serve as textualizers. Visual textualization and image-native modeling impose different representational bottlenecks. Textualization produces a fixed, inspectable, and reusable linguistic representation, but information omitted or misexpresse...

    2024

  11. [30]

    The first pass extracts the question and identifies any additional visual component; the second checks the extraction against the same source image

    B.2 Item Parsing and Visual Textualization The initial parser uses the OpenAI GPT-5.5 API aliasgpt-5.5, accessed in June 2026, and operates in two passes. The first pass extracts the question and identifies any additional visual component; the second checks the extraction agai...

    2026

  12. [71]

    Their fixed equal-weight average requires no fitted fusion parameters and reaches 0.4780 RMSE, but its paired intervals relative to either component cross zero

    Across their three matched seeds, the preference is unanimous for 37 image-native wins and 40 textualization wins; the other 68 items change winner across seeds. Their fixed equal-weight average requires no fitted fusion parameters and reaches 0.4780 RMSE, but its paired inter...

  13. [227]

    The complete group is shown in Table 21; withn= 4, its aggregate ordering is not a stable estimate of a population effect

    This makesQ a comparatively rich reduced interface and helps explain why adding another description is redundant on three of four items. The complete group is shown in Table 21; withn= 4, its aggregate ordering is not a stable estimate of a population effect. Item Visual task ...

  14. [1995]

    Text-based approaches to item difficulty modeling in large-scale assessments: A systematic review.arXiv preprint arXiv:2509.23486,

    Sydney Peters, Nan Zhang, Hong Jiao, Ming Li, Tianyi Zhou, and Robert Lissitz. Text-based approaches to item difficulty modeling in large-scale assessments: A systematic review.arXiv preprint arXiv:2509.23486,

  15. [2010]

    Upn-icc at bea 2024 shared task: Leveraging llms for multiple-choice questions difficulty prediction

    George Dueñas, Sergio Jimenez, and Geral Mateus Ferro. Upn-icc at bea 2024 shared task: Leveraging llms for multiple-choice questions difficulty prediction. InProceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024), pages 542–550,

    2024

  16. [2011]

    Itec at bea 2024 shared task: Predicting difficulty and response time of medical exam questions with statistical, machine learning, and language models

    Anaïs Tack, Siem Buseyne, Changsheng Chen, Robbe D’hondt, Michiel De Vrindt, Alireza Gharahighehi, Sameh Metwaly, Felipe Kenji Nakano, and Ann-Sophie Noreillie. Itec at bea 2024 shared task: Predicting difficulty and response time of medical exam questions with statistical, ma...

    2024

  17. [2016]

    Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts

    Pan Lu, Hritik Bansal, Tony Xia, Jiacheng Liu, Chunyuan Li, Hannaneh Hajishirzi, Hao Cheng, Kai-Wei Chang, Michel Galley, and Jianfeng Gao. Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts. InInternational Conference on Learning Representati...

    2024

  18. [2018]

    Can llms estimate student struggles? human- ai difficulty alignment with proficiency simulation for item difficulty prediction.arXiv preprint arXiv:2512.18880, 2025a

    Ming Li, Han Chen, Yunze Xiao, Jian Chen, Hong Jiao, and Tianyi Zhou. Can llms estimate student struggles? human- ai difficulty alignment with proficiency simulation for item difficulty prediction.arXiv preprint arXiv:2512.18880, 2025a. Ming Li, Hong Jiao, Tianyi Zhou, Nan Zha...

    2016 arXiv

  19. [2019]

    Findings from the first shared task on automated prediction of difficulty and response time for multiple-choice questions

    Victoria Yaneva, Kai North, Peter Baldwin, Le An Ha, Saed Rezayi, Yiyun Zhou, Sagnik Ray Choudhury, Polina Harik, and Brian Clauser. Findings from the first shared task on automated prediction of difficulty and response time for multiple-choice questions. InProceedings of the ...

    2024

  20. [2020]

    Benchmarking multimodal mathematical reasoning with explicit visual dependency.arXiv preprint arXiv:2504.18589, 2025b

    Zhikai Wang, Jiashuo Sun, Wenqi Zhang, Zhiqiang Hu, Xin Li, Fan Wang, and Deli Zhao. Benchmarking multimodal mathematical reasoning with explicit visual dependency.arXiv preprint arXiv:2504.18589, 2025b. Kang Xue, Victoria Yaneva, Christopher Runyon, and Peter Baldwin. Predict...

    arXiv

  21. [2021]

    Expanding performance boundaries of open-source multimodal models with model, data, and test-time scaling.arXiv preprint arXiv:2412.05271,

    Zhe Chen, Weiyun Wang, Yue Cao, Yangzhou Liu, Zhangwei Gao, Erfei Cui, Jinguo Zhu, Shenglong Ye, Hao Tian, Zhaoyang Liu, et al. Expanding performance boundaries of open-source multimodal models with model, data, and test-time scaling.arXiv preprint arXiv:2412.05271,

    arXiv

  22. [2022]

    GPT-5.5 System Card, 2026.https://openai.com/index/gpt-5-5-system-card/

    OpenAI. GPT-5.5 System Card, 2026.https://openai.com/index/gpt-5-5-system-card/. Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual...

    2026 arXiv

  23. [2023]

    Large language models are students at various levels: Zero-shot question difficulty estimation

    Jae-Woo Park, Seong-Jin Park, Hyun-Sik Won, and Kang-Min Kim. Large language models are students at various levels: Zero-shot question difficulty estimation. InFindings of the association for computational linguistics: Emnlp 2024, pages 8157–8177,

    2024

  24. [2024]

    Qwen3-vl technical report.arXiv preprint arXiv:2511.21631, 2025a

    Shuai Bai, Yuxuan Cai, Ruizhe Chen, Keqin Chen, Xionghui Chen, Zesen Cheng, Lianghao Deng, Wei Ding, Chang Gao, Chunjiang Ge, et al. Qwen3-vl technical report.arXiv preprint arXiv:2511.21631, 2025a. Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dan...

    arXiv

  25. [2025]

    Utilizing machine learning to predict question difficulty and response time for enhanced test construction

    10 Rishikesh Fulari and Jonathan Rusert. Utilizing machine learning to predict question difficulty and response time for enhanced test construction. InProceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024), pages 528–533,

    2024

  26. [2026]

    Geoqa: A geometric question answering benchmark towards multimodal numerical reasoning

    Jiaqi Chen, Jianheng Tang, Jinghui Qin, Xiaodan Liang, Lingbo Liu, Eric Xing, and Liang Lin. Geoqa: A geometric question answering benchmark towards multimodal numerical reasoning. InFindings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 513–523,

    2021

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.