Paper Citation Record · LEDGER

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

As of 7 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2608.04554.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2608.04554 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:14:43.862454Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

34 of 34 outbound references displayed

verified exact5
verified fuzzy13
unresolved16
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4042e9c9-f6d4-4c71-b957-e8d44ce87d35 · outbound

This paper cites LLMs Struggle to Measure What Distinguishes Students of Different Proficiency Levels: A Study of Item Discrimination in Reading Comprehension Assessment.

Representing Visual Evidence for Item Difficulty Prediction: Visual Textualization and Image-Native Modeling LLMs Struggle to Measure What Distinguishes Students of Different Proficiency Levels: A Study of Item Discrimination in Reading Comprehension Assessment

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T22:14:41.784243Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a6332e64-bdbf-4d17-8ae2-44d2873e786b · outbound

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

Representing Visual Evidence for Item Difficulty Prediction: Visual Textualization and Image-Native Modeling Blank-image predictions are constant within each image-only seed, so their Spearman correlation is undefined

Reference 3

Resolution: verified exact; raw_fallback, observed 2026-08-06T22:14:44.621129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 933109e8-e99b-4bb6-be81-0ab93f9e4ec1 · outbound

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

Representing Visual Evidence for Item Difficulty Prediction: Visual Textualization and Image-Native Modeling Qwen-VL and PaliGemma use their packaged image preprocessing; InternVL uses a448× 448image transform and the model’s image-context tokens

Reference 4

Resolution: verified exact; raw_fallback, observed 2026-08-06T22:14:44.833055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 50655e06-b89e-497d-ae9a-f9873f088bc8 · outbound

This paper cites The Llama 3 Herd of Models.

Representing Visual Evidence for Item Difficulty Prediction: Visual Textualization and Image-Native Modeling The Llama 3 Herd of Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T22:14:42.056734Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e9bb733-7e0b-43f3-b23e-d9678e638fca · outbound

This paper cites Jump-starting item parameters for adaptive language tests.

Representing Visual Evidence for Item Difficulty Prediction: Visual Textualization and Image-Native Modeling Jump-starting item parameters for adaptive language tests

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:14:46.571597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4a692e18-eb14-4d07-a3ab-9257d176d774 · outbound

This paper cites Qwen2.5 Technical Report.

Representing Visual Evidence for Item Difficulty Prediction: Visual Textualization and Image-Native Modeling Qwen2.5 Technical Report

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T22:14:42.480552Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74348b13-8884-4c25-a219-332c13d771fb · outbound

This paper cites Qwen2.5 Technical Report.

Representing Visual Evidence for Item Difficulty Prediction: Visual Textualization and Image-Native Modeling Qwen2.5 Technical Report

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T22:14:42.532662Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a63e776-51f4-48fc-a5b2-628d25d4e0b1 · outbound

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

Representing Visual Evidence for Item Difficulty Prediction: Visual Textualization and Image-Native Modeling Unibucllm: Harnessing llms for automated prediction of item difficulty and response time for multiple-choice questions

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:14:46.240931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ab5fa2c9-f0ff-4b99-92b8-b77ee8609d1a · outbound

This paper cites an unresolved cited work.

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

Reference 17

Resolution: unresolved; raw_fallback, observed 2026-08-06T22:14:46.092650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dedf64f9-f21f-4dbb-8a3d-57ea7704004f · outbound

This paper cites PaliGemma 2: A Family of Versatile VLMs for Transfer.

Representing Visual Evidence for Item Difficulty Prediction: Visual Textualization and Image-Native Modeling PaliGemma 2: A Family of Versatile VLMs for Transfer

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T22:14:42.703573Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29481127-f033-41f9-8ccf-824b126e6213 · outbound

This paper cites Large language model-based pipeline for item difficulty and response time estimation for educational assessments.

Representing Visual Evidence for Item Difficulty Prediction: Visual Textualization and Image-Native Modeling Large language model-based pipeline for item difficulty and response time estimation for educational assessments

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:14:45.840147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 61b6b596-afe7-4864-9bab-5ccfeca145cf · outbound

This paper cites Cognitive Episodes in LLM Reasoning Traces Enable Interpretable Human Item Difficulty Prediction.

Representing Visual Evidence for Item Difficulty Prediction: Visual Textualization and Image-Native Modeling Cognitive Episodes in LLM Reasoning Traces Enable Interpretable Human Item Difficulty Prediction

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:14:45.108666Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6ddbd1a3-fae9-4e1e-8107-e828b428a4f4 · outbound

This paper cites InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency.

Representing Visual Evidence for Item Difficulty Prediction: Visual Textualization and Image-Native Modeling InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T22:14:42.928682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4859f4c9-044b-4f07-9c06-408af74ef0c7 · outbound

This paper cites Qwen3 Technical Report.

Representing Visual Evidence for Item Difficulty Prediction: Visual Textualization and Image-Native Modeling Qwen3 Technical Report

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T22:14:43.143417Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ec5f49b-827a-41e8-b580-090a63ba1648 · outbound

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

Representing Visual Evidence for Item Difficulty Prediction: Visual Textualization and Image-Native Modeling Towards valid student simulation with large language models.arXiv preprint arXiv:2601.05473,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T22:14:43.204466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95409a96-ec0c-46c7-9479-43cf4cc02bda · outbound

This paper cites InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models.

Representing Visual Evidence for Item Difficulty Prediction: Visual Textualization and Image-Native Modeling InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T22:14:43.288809Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc2df163-b325-4164-b994-8d17164bcfbb · outbound

This paper cites 3 3.2 Difficulty Labels.

Representing Visual Evidence for Item Difficulty Prediction: Visual Textualization and Image-Native Modeling 3 3.2 Difficulty Labels

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:14:45.629862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation aa9a3b01-bb03-450a-99af-9d8a6774b9fb · outbound

This paper cites Visual textualization and image-native modeling impose different representational bottlenecks.

Representing Visual Evidence for Item Difficulty Prediction: Visual Textualization and Image-Native Modeling Visual textualization and image-native modeling impose different representational bottlenecks

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:14:45.530690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ab27c39d-b6aa-4ebe-b7ec-8fa2a3cfa6c9 · outbound

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

Representing Visual Evidence for Item Difficulty Prediction: Visual Textualization and Image-Native Modeling The first pass extracts the question and identifies any additional visual component; the second checks the extraction against the same source image

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:14:45.409774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 90b2eeb5-5ce5-454c-b667-171a4c3629ab · outbound

This paper cites 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.

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

Reference 71

Resolution: verified exact; raw_fallback, observed 2026-08-06T22:14:44.413204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 50e53fcb-2e65-4ccd-a2bd-21fdb7002b07 · outbound

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

Representing Visual Evidence for Item Difficulty Prediction: Visual Textualization and Image-Native Modeling The complete group is shown in Table 21; withn= 4, its aggregate ordering is not a stable estimate of a population effect

Reference 227

Resolution: verified exact; raw_fallback, observed 2026-08-06T22:14:44.168609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bada4227-35fd-45f3-8f01-b32ab37598a2 · outbound

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

Representing Visual Evidence for Item Difficulty Prediction: Visual Textualization and Image-Native Modeling Text-based approaches to item difficulty modeling in large-scale assessments: A systematic review.arXiv preprint arXiv:2509.23486,

Reference 1995

Resolution: unresolved; no resolver link, observed 2026-08-06T22:14:42.425904Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db0ccafa-0121-4b8c-8271-50e31ff37395 · outbound

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

Representing Visual Evidence for Item Difficulty Prediction: Visual Textualization and Image-Native Modeling Upn-icc at bea 2024 shared task: Leveraging llms for multiple-choice questions difficulty prediction

Reference 2010

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:14:46.984440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 06cfef41-6ed2-4ae9-a3f9-8a8918e138c0 · outbound

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

Representing Visual Evidence for Item Difficulty Prediction: Visual Textualization and Image-Native Modeling Itec at bea 2024 shared task: Predicting difficulty and response time of medical exam questions with statistical, machine learning, and language models

Reference 2011

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:14:45.969488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dbe14f68-7146-471a-bdf7-6beb39fd66b5 · outbound

This paper cites Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts.

Representing Visual Evidence for Item Difficulty Prediction: Visual Textualization and Image-Native Modeling Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts

Reference 2016

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:14:46.752576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1e35b22c-56f3-4ced-8e4e-2d405770b86d · outbound

This paper cites Can LLMs Estimate Student Struggles? Human-AI Difficulty Alignment with Proficiency Simulation for Item Difficulty Prediction.

Representing Visual Evidence for Item Difficulty Prediction: Visual Textualization and Image-Native Modeling Can LLMs Estimate Student Struggles? Human-AI Difficulty Alignment with Proficiency Simulation for Item Difficulty Prediction

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-06T22:14:42.098999Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50f12ec2-a5d3-4be4-8314-d03d8652eedf · outbound

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

Representing Visual Evidence for Item Difficulty Prediction: Visual Textualization and Image-Native Modeling Findings from the first shared task on automated prediction of difficulty and response time for multiple-choice questions

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:14:45.723412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6c60d004-d4e4-40e4-ba33-741b9556fb0e · outbound

This paper cites Benchmarking Multimodal Mathematical Reasoning with Explicit Visual Dependency.

Representing Visual Evidence for Item Difficulty Prediction: Visual Textualization and Image-Native Modeling Benchmarking Multimodal Mathematical Reasoning with Explicit Visual Dependency

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-06T22:14:43.010301Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation edefa4ec-cb67-4ec7-8919-ee6a1ab7dcae · outbound

This paper cites Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling.

Representing Visual Evidence for Item Difficulty Prediction: Visual Textualization and Image-Native Modeling Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-06T22:14:41.888312Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f79b8d6-8391-4e01-a64b-78c0565ce311 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Representing Visual Evidence for Item Difficulty Prediction: Visual Textualization and Image-Native Modeling DINOv2: Learning Robust Visual Features without Supervision

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-06T22:14:42.314157Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f775a61b-6148-4ee8-8ec8-5558b4a5a38d · outbound

This paper cites Large language models are students at various levels: Zero-shot question difficulty estimation.

Representing Visual Evidence for Item Difficulty Prediction: Visual Textualization and Image-Native Modeling Large language models are students at various levels: Zero-shot question difficulty estimation

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:14:46.391100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9d261d2a-9061-44d3-a16c-074588c9455d · outbound

This paper cites Qwen3-VL Technical Report.

Representing Visual Evidence for Item Difficulty Prediction: Visual Textualization and Image-Native Modeling Qwen3-VL Technical Report

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T22:14:41.746790Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd1f35c3-6685-44ca-b59a-31fc5afdaa51 · outbound

This paper cites Utilizing machine learning to predict question difficulty and response time for enhanced test construction.

Representing Visual Evidence for Item Difficulty Prediction: Visual Textualization and Image-Native Modeling Utilizing machine learning to predict question difficulty and response time for enhanced test construction

Reference 2025

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:14:46.861974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 357b56c4-fdb2-418b-90ed-5656ae20e6ba · outbound

This paper cites Geoqa: A geometric question answering benchmark towards multimodal numerical reasoning.

Representing Visual Evidence for Item Difficulty Prediction: Visual Textualization and Image-Native Modeling Geoqa: A geometric question answering benchmark towards multimodal numerical reasoning

Reference 2026

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:14:47.064968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

No inbound Pith citation observations are available.