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arxiv: 2604.16392 · v1 · submitted 2026-03-28 · 💻 cs.CY · cs.AI· cs.CL

Recognition: no theorem link

RoMathExam: A Longitudinal Dataset of Romanian Math Exams (1895-2025) with a Seven-Decade Core (1957-2025)

Luca-Ncolae Cuclea , Sabin-Codrut Badea , Adrian-Marius Dumitran
Authors on Pith no claims yet

Pith reviewed 2026-05-14 21:58 UTC · model grok-4.3

classification 💻 cs.CY cs.AIcs.CL
keywords Romanian math examslongitudinal datasetdifficulty metriccurriculum analysisAI in educationsolution complexityexam corpus
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The pith

A longitudinal dataset of Romanian math exams from 1895 to 2025 introduces a solution complexity metric validated as a proxy for difficulty across AI models.

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

The paper introduces RoMathExam, a collection of over 10,000 Romanian high-school mathematics problems spanning from 1895 to 2025, with a standardized core from 1957 onward. It addresses the scarcity of authentic assessment data for AI in education research in non-English contexts by providing curriculum-tagged problems with embeddings. The authors propose a solution complexity metric to estimate difficulty intrinsically and validate it through high agreement among three advanced reasoning models, enabling analysis of curriculum shifts without relying on historical student scores.

Core claim

We introduce RoMathExam as a longitudinal dataset of Romanian high-school mathematics exams and propose a solution complexity metric as a scalable intrinsic proxy for difficulty. Evaluation across GPT-5-mini, DeepSeek-R1, and Qwen3-235B-Thinking shows high cross-model synchronization with r > 0.72, confirming the metric isolates intrinsic mathematical depth. The dataset reveals a regime shift from volatile historical formats to a standardized algebra-dominant modern curriculum.

What carries the argument

The solution complexity metric, which quantifies the intrinsic difficulty of math problems based on solution steps or structure, serving as a proxy independent of student performance data.

If this is right

  • The dataset supports reproducible research in difficulty modeling for low-resource languages.
  • Longitudinal analysis can track curriculum evolution over decades.
  • LLM evaluation can use the metric to test reasoning capabilities on authentic exam problems.
  • Variant detection and deduplication become feasible through embeddings and tags.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Future work could calibrate the metric against modern student performance data to strengthen its validity.
  • The approach might extend to other languages' exam corpora if similar digitization efforts are made.
  • Such metrics could help design more balanced assessments by identifying overly complex problems.

Load-bearing premise

The solution complexity metric accurately reflects true student-experienced difficulty despite the absence of historical psychometric performance data for calibration.

What would settle it

Collecting actual student success rates on a subset of the problems and checking if they correlate with the solution complexity scores.

Figures

Figures reproduced from arXiv: 2604.16392 by Adrian-Marius Dumitran, Luca-Ncolae Cuclea, Sabin-Codrut Badea.

Figure 1
Figure 1. Figure 1: Temporal Similarity Matrices comparing year-to-year consistency. splitting would likely overestimate model performance in such non-stationary environments [30] [PITH_FULL_IMAGE:figures/full_fig_p008_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Relative Share of Major Mathematical Categories (1957–2025). The plot high￾lights the gradual dominance of Algebra in the modern curriculum [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Cross-model complexity trends (2010–2025). High synchronization (r > 0.72) confirms intrinsic difficulty spikes between the models. The analysis, visualized in [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The Stabilization of Difficulty Metrics. Historical exams (pre-2000) display sig￾nificant stochastic variance (a), often driven by isolated, high-complexity outliers. The modern standardized era (2010–2025) demonstrates a convergence toward stability: while individual item complexity has decreased (b), the aggregate cognitive load is maintained through a higher volume of items, resulting in a consistent to… view at source ↗
read the original abstract

AI in Education research increasingly relies on authentic, curriculum-grounded assessment data, yet large, well-structured exam corpora remain scarce for many languages and educational systems. We introduce RoMathExam, a longitudinal dataset of Romanian high-school mathematics exams spanning 1895-2025, with a robust standardized core for 1957-2025. The dataset contains 10,592 mathematics problems organized into 600+ complete exam sets across multiple tracks (M1-M4), covering both official national examination sessions and ministry-published training variants. Beyond high-fidelity digitization and a unified JSON schema with traceable provenance, RoMathExam is enriched with curriculum-aligned topic tags and dense text embeddings, enabling variant detection, deduplication, and similarity-based retrieval. To overcome the lack of historical psychometric data, we propose and validate a solution complexity metric as a scalable intrinsic proxy for difficulty. Our evaluation across three frontier reasoning models (GPT-5-mini, DeepSeek-R1, and Qwen3-235B-Thinking) reveals high cross-model synchronization (r > 0.72), confirming the metric's ability to isolate intrinsic mathematical depth from stochastic generation noise. We demonstrate the dataset's utility through a longitudinal analysis that quantifies a "regime shift" from volatile historical formats to a standardized, algebra-dominant modern curriculum. RoMathExam provides a foundation for reproducible research in difficulty modeling, curriculum analytics, and LLM evaluation in low-resource linguistic contexts.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces RoMathExam, a longitudinal dataset of 10,592 Romanian high-school mathematics problems from 600+ exam sets spanning 1895-2025 (core 1957-2025), with unified JSON schema, curriculum topic tags, and dense embeddings for deduplication and retrieval. It proposes a solution complexity metric as an intrinsic proxy for difficulty and validates it via Pearson r > 0.72 across three frontier LLMs (GPT-5-mini, DeepSeek-R1, Qwen3-235B-Thinking), then applies the metric to quantify a curriculum regime shift toward algebra dominance.

Significance. If the central claims hold, the dataset would constitute a valuable, reproducible resource for curriculum analytics and difficulty modeling in low-resource linguistic settings. The reported cross-model synchronization indicates the metric captures consistent structural features of problems, offering a scalable alternative where psychometric data are absent. However, its significance as a proxy for student-experienced difficulty remains limited by the lack of external calibration.

major comments (2)
  1. [Abstract] Abstract: the solution complexity metric is presented as a validated 'scalable intrinsic proxy for difficulty' with r > 0.72, yet no exact definition, formula, error analysis, or controls for model-specific biases are supplied, leaving the central validation claim only partially verifiable.
  2. [Metric evaluation section] The section describing the metric evaluation: cross-model agreement (GPT-5-mini, DeepSeek-R1, Qwen3-235B-Thinking) is reported but supplies no comparison to historical pass-rate data, teacher difficulty ratings, or student performance on the same items, so the claim that the metric isolates 'intrinsic mathematical depth' from stochastic noise lacks an external anchor and risks capturing shared model artifacts.
minor comments (2)
  1. [Dataset description] The exact number of complete exam sets should be stated precisely rather than '600+', and a table summarizing distribution by track (M1-M4) and decade would improve clarity.
  2. [Data collection section] Provenance and digitization details for the pre-1957 portion of the corpus should be expanded to allow readers to assess data quality and potential transcription biases.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment point by point below, indicating where revisions have been made to strengthen the presentation of the solution complexity metric and its evaluation.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the solution complexity metric is presented as a validated 'scalable intrinsic proxy for difficulty' with r > 0.72, yet no exact definition, formula, error analysis, or controls for model-specific biases are supplied, leaving the central validation claim only partially verifiable.

    Authors: We agree that the abstract should make the metric fully verifiable on first reading. In the revised manuscript we have added a concise definition of the solution complexity metric together with its exact formula, a brief description of the error analysis (standard deviation across repeated inferences), and explicit controls for model-specific biases via pairwise correlations and variance reporting. These additions keep the abstract within length limits while rendering the validation claim directly checkable. revision: yes

  2. Referee: [Metric evaluation section] The section describing the metric evaluation: cross-model agreement (GPT-5-mini, DeepSeek-R1, Qwen3-235B-Thinking) is reported but supplies no comparison to historical pass-rate data, teacher difficulty ratings, or student performance on the same items, so the claim that the metric isolates 'intrinsic mathematical depth' from stochastic noise lacks an external anchor and risks capturing shared model artifacts.

    Authors: We acknowledge the absence of direct external anchors. The manuscript already notes that historical psychometric data are unavailable for the full longitudinal span, which motivated the intrinsic approach. The reported r > 0.72 is obtained across three architecturally and training-distinct frontier models, which reduces the likelihood of shared artifacts. To address the referee's concern we have added a new limitations subsection that (i) explicitly states the lack of historical pass-rate or teacher-rating data, (ii) reports a limited modern-era comparison with available national exam statistics, and (iii) discusses the rationale for treating cross-model consistency as an internal validation of structural depth. We do not claim the metric equals student-experienced difficulty; we position it as a reproducible proxy when external calibration is impossible. revision: partial

standing simulated objections not resolved
  • Comprehensive external calibration against historical student performance or teacher ratings for the entire 1895-2025 period, which simply does not exist in accessible form.

Circularity Check

0 steps flagged

No significant circularity in metric definition or validation

full rationale

The solution complexity metric is introduced as an intrinsic proxy derived from problem structure, with validation performed via independent cross-model Pearson correlation (r > 0.72) on three separate frontier LLMs. No equations or steps reduce the metric to a fitted parameter on the same data, a self-citation chain, or a definitional tautology. Dataset construction, tagging, and longitudinal analysis remain independent of this validation step. This is the expected non-circular outcome for an intrinsically defined quantity checked against external model behavior.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claims rest on the assumption that cross-model agreement on the proposed metric reflects intrinsic mathematical depth rather than shared model training artifacts, and that the collected exams are representative of the Romanian curriculum.

axioms (1)
  • domain assumption Cross-model synchronization on solution complexity isolates intrinsic mathematical depth from stochastic generation noise
    Invoked in the validation paragraph to interpret r > 0.72 as evidence for the metric's validity.

pith-pipeline@v0.9.0 · 5590 in / 1482 out tokens · 71737 ms · 2026-05-14T21:58:03.023138+00:00 · methodology

discussion (0)

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