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arxiv: 2606.29894 · v1 · pith:N2RFKWZAnew · submitted 2026-06-29 · 💻 cs.IR · cs.AI· cs.CL· cs.LG

SABER-Math: Automated Benchmark for Information Retrieval Evaluation in Mathematics

Nikolay Georgiev , Maria Drencheva , Kseniia Ibragimova , Ivo Petrov , Dimitar I. Dimitrov , Martin Vechev This is my paper

Pith reviewed 2026-06-30 04:37 UTC · model grok-4.3

classification 💻 cs.IR cs.AIcs.CLcs.LG
keywords mathematical information retrievalautomated benchmarkLLM evaluationreranking tasksembedding modelsmath problemsrelevance rating
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The pith

SABER-Math creates the first automated benchmark for mathematical information retrieval without expert annotators.

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

The paper introduces SABER-Math to solve the problem of evaluating retrieval systems for math tasks where isolating retriever quality is hard and existing benchmarks miss fine-grained relevance. It builds reranking tasks from 283K high-school problems by having LLMs extract solution summaries and topic tags, identifying relevant documents through ontology and lexical similarities, and assigning ratings via Swiss-style preference tournaments. The resulting benchmark reveals that recent embedding models beat classical and math-specific retrievers but still fail in algebra and calculus. It also shows that general-purpose benchmarks like MTEB do not predict performance on mathematical content.

Core claim

SABER-Math is the first fully automated benchmark for mathematical IR evaluation. Starting from 283K high-school problems with solutions, it extracts LLM-generated summaries and topics, discovers relevant documents via topic-based and lexical similarities, and produces fine-grained relevance ratings through Swiss-style LLM preference tournaments, enabling evaluation of lexical, math-specific, and embedding retrievers without human experts.

What carries the argument

The Swiss-style LLM preference tournament that converts pairwise preferences into fine-grained relevance ratings for candidate documents.

If this is right

  • Modern embedding models substantially outperform classical and math-specific baselines on mathematical retrieval tasks.
  • Even the strongest retrievers still underperform in symbol-heavy domains such as Algebra and Calculus.
  • General-purpose IR benchmarks such as MTEB do not reliably predict performance on mathematical content.

Where Pith is reading between the lines

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

  • Teams building math-aware agents may need separate retriever testing on domain-specific data rather than relying on general benchmarks.
  • The automated construction method could be adapted to create similar benchmarks for retrieval in physics or programming domains.
  • Symbol handling in current embeddings may need targeted improvements to close the gap observed in algebra and calculus.

Load-bearing premise

LLM-generated solution summaries, topic tags, and Swiss-style preference tournaments produce accurate fine-grained relevance ratings that reflect true mathematical relevance.

What would settle it

A side-by-side comparison of SABER-Math ratings against ratings assigned by human mathematics experts on the same set of queries and documents.

Figures

Figures reproduced from arXiv: 2606.29894 by Dimitar I. Dimitrov, Ivo Petrov, Kseniia Ibragimova, Maria Drencheva, Martin Vechev, Nikolay Georgiev.

Figure 1
Figure 1. Figure 1: Overview of SABER-MATH construction. First, we source a large mathematical corpus of problems and their solutions 1 . Using LLM annotation, we compute two separate relevance signals for every document pair based on topics and solution summaries 2 . We then select 1000 target query documents, each with 150 relevant documents split evenly across relevance signals 3 . Finally, we rank the candidates per query… view at source ↗
Figure 2
Figure 2. Figure 2: Distribution of retriever preferences for mathematical vs. textual content across domains, as well as the domains avg. token distribution. 4.2. Comparison of Mathematical and Textual Content in Embedding Space We next examine what content IR methods focus on when aligning relevant pieces of mathematics. For any problem, we construct math-only and word-only variants by segment￾ing mathematical expressions u… view at source ↗
Figure 3
Figure 3. Figure 3: Average number of inversions between orderings pro￾duced by factoring all pairs compared to a Swiss tournament. 1 20 40 60 80 100 120 140 Sorted rank 0.0 0.2 0.4 0.6 0.8 1.0 Cumulative Proportion Topic-only Summary-only Both [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Cumulative rank distribution of candidates selected by different relevance signals. Higher curves indicate that candidates from that group appear earlier in the final LLM-derived ranking. pairs. In §D.1, we further confirm through human evalua￾tion that this setting nearly matches the exhaustive ranking in terms of human preference. 4.4. Relative Performance of Relevance Signals In §3.4, we use two proxy s… view at source ↗
Figure 6
Figure 6. Figure 6: Domain and subdomain distribution of the sampling corpus described in §3.1. A. Limitations While SABER-MATH provides a scalable framework for evaluating mathematical information retrieval, several limitations remain. First, the benchmark is built primarily from high-school, olympiad, and early undergraduate-style problems, so performance may not fully transfer to research-level mathematics, formal proof li… view at source ↗
Figure 7
Figure 7. Figure 7: Domain and subdomain distribution of the documents retrieved for a representative query from SABER-MATH. Shaded subdomains indicate the subdomains associated with the query, while black lines mark documents ranked in the top 10 for the query. Because the AoPS subset is nearly five times larger than the official-source subset, relying too heavily on AoPS could reduce the overall quality of the corpus. We th… view at source ↗
Figure 8
Figure 8. Figure 8: Percentage of query problems for which the mathematical component receives a higher average relevance score than the textual component, across retrieval methods [PITH_FULL_IMAGE:figures/full_fig_p020_8.png] view at source ↗
read the original abstract

As agentic AI systems tackle more complex mathematical tasks, they increasingly rely on information retrieval (IR) to search problem databases, theorem libraries, and educational resources. However, choosing the right retriever remains difficult, as it is infeasible to directly isolate its effect on downstream performance. On the other hand, existing retrieval-specific benchmarks often fail to capture fine-grained mathematical relevance, penalizing relevant documents. We address this gap by introducing SABER-Math, the first fully automated benchmark for evaluating mathematical IR without expert annotation. Starting from 283K high-school-level math problems with solutions, SABER-Math builds challenging reranking tasks in three steps: (i) first, LLMs extract concise solution summaries and mathematical topics for each problem; (ii) then, per-query relevant documents are discovered using ontology topic-based and lexical solutions-summary-based similarities, and (iii) finally, a Swiss-style LLM preference tournament produces fine-grained relevance ratings for the documents. We evaluate lexical retrievers, specialized mathematical retrieval systems, and recent embedding models. We find that while modern embedding models substantially outperform classical and math-specific baselines, even the strongest systems struggle in symbol-heavy domains like Algebra and Calculus. Importantly, we show that general-purpose IR benchmarks such as MTEB do not reliably predict mathematical performance, especially for recent embedding models, highlighting the need for math-specific retrieval benchmarks.

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 / 1 minor

Summary. The manuscript introduces SABER-Math as the first fully automated benchmark for mathematical IR evaluation. Starting from 283K high-school math problems, it constructs reranking tasks via three LLM-driven steps: extraction of solution summaries and topic tags, discovery of relevant documents through ontology-based topic and lexical summary similarities, and Swiss-style LLM preference tournaments to generate fine-grained relevance ratings. Evaluations of lexical retrievers, math-specific systems, and embedding models show modern embeddings outperforming baselines yet struggling in Algebra and Calculus, while demonstrating that MTEB scores do not reliably predict mathematical IR performance.

Significance. If the automated pipeline produces ratings that align with expert mathematical relevance, the work would provide a scalable, expert-annotation-free benchmark for a domain where manual labeling is costly. The large corpus size, explicit three-step construction, and direct comparison to MTEB constitute concrete strengths that could enable reproducible progress in math-specific retrieval.

major comments (2)
  1. [Section 3.3] Section 3.3: The Swiss-style LLM preference tournament is presented as yielding fine-grained relevance ratings that substitute for expert annotation and reflect true mathematical relevance, yet the manuscript reports no human-expert correlation study, inter-rater agreement baseline, or ablation against gold labels on any held-out set. This directly undermines the central claim that the benchmark evaluates retrievers on accurate mathematical relevance without expert input.
  2. [Section 4] Section 4 and abstract: Claims that embedding models 'substantially outperform' baselines and that MTEB does not predict math performance rest on the unvalidated tournament ratings; without a validation subsection, these comparative results cannot be interpreted as evidence about mathematical relevance.
minor comments (1)
  1. [Section 3] The similarity thresholds and prompt templates used in steps (i) and (ii) of the pipeline are described at a high level but lack explicit values or sensitivity analysis, which would aid reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and constructive feedback, particularly the emphasis on validating the automated relevance ratings. We address each major comment below and commit to revisions that directly respond to the concerns raised.

read point-by-point responses

  1. Referee: [Section 3.3] Section 3.3: The Swiss-style LLM preference tournament is presented as yielding fine-grained relevance ratings that substitute for expert annotation and reflect true mathematical relevance, yet the manuscript reports no human-expert correlation study, inter-rater agreement baseline, or ablation against gold labels on any held-out set. This directly undermines the central claim that the benchmark evaluates retrievers on accurate mathematical relevance without expert input.

    Authors: We agree that the absence of a human-expert correlation study is a substantive limitation. In the revised version we will add a dedicated validation subsection to Section 3.3. This subsection will describe a human evaluation on a held-out sample of 100 queries (with their candidate documents), where two mathematics experts independently assign relevance grades. We will report (i) inter-rater agreement (Cohen’s kappa), (ii) correlation between the LLM tournament scores and the expert grades (Spearman’s rho and Kendall’s tau), and (iii) an ablation comparing retrieval metrics obtained with LLM ratings versus expert ratings. The results will be used to qualify the benchmark’s reliability. revision: yes

  2. Referee: [Section 4] Section 4 and abstract: Claims that embedding models 'substantially outperform' baselines and that MTEB does not predict math performance rest on the unvalidated tournament ratings; without a validation subsection, these comparative results cannot be interpreted as evidence about mathematical relevance.

    Authors: We accept that the comparative claims in Section 4 and the abstract cannot be presented as definitive evidence of mathematical relevance until the ratings are validated. After completing the human study described above, we will revise the abstract and Section 4 to (a) state the observed correlations explicitly, (b) condition the performance claims on those correlations, and (c) add a limitations paragraph discussing the degree to which the automated pipeline approximates expert judgment. If correlations prove modest, we will tone down the language accordingly. revision: yes

Circularity Check

0 steps flagged

No circularity in benchmark construction or claims

full rationale

The paper presents SABER-Math as a constructed benchmark using LLM summaries, topic tags, similarity discovery, and Swiss-style tournaments to generate relevance ratings. No equations, fitted parameters, predictions, or derivations are described that could reduce to inputs by construction. No self-citations are invoked as load-bearing for any uniqueness theorem or ansatz. The process is presented as an independent automated pipeline whose outputs enable downstream evaluations, with no reduction of the central claim to its own fitted values or prior self-referential results.

Axiom & Free-Parameter Ledger

0 free parameters · 3 axioms · 0 invented entities

The approach rests on unverified assumptions about LLM accuracy for mathematical relevance labeling; no free parameters or invented entities are mentioned in the abstract.

axioms (3)
  • domain assumption LLMs can reliably extract concise solution summaries and mathematical topics from high-school problems
    Invoked in the first construction step.
  • domain assumption Ontology topic-based and lexical solution-summary similarities discover per-query relevant documents
    Invoked in the second construction step.
  • domain assumption Swiss-style LLM preference tournament produces accurate fine-grained relevance ratings
    Invoked in the third construction step.

pith-pipeline@v0.9.1-grok · 5803 in / 1402 out tokens · 43360 ms · 2026-06-30T04:37:48.952333+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

29 extracted references · 6 canonical work pages · 3 internal anchors

  1. [1]

    Agarwal, S., Ahmad, L., Ai, J., Altman, S., Applebaum, A., Arbus, E., et al

    URL https://ceur-ws.org/Vol-385 4/emtcir-2.pdf. Agarwal, S., Ahmad, L., Ai, J., Altman, S., Applebaum, A., Arbus, E., et al. gpt-oss-120b & gpt-oss-20b model card.arXiv preprint arXiv:2508.10925, 2025. URL https://arxiv.org/abs/2508.10925. Akram, M. K., Sturua, S., Havriushenko, N., Herreros, Q., Günther, M., Werk, M., and Xiao, H. jina-embeddings-v5- tex...

    Pith/arXiv arXiv 2025

  2. [2]

    MathNet: a Global Multimodal Benchmark for Mathematical Reasoning and Retrieval

    doi: 10.48550/ARXIV.2604.18584. URL https: //doi.org/10.48550/arXiv.2604.18584. Babakhin, Y ., Osmulski, R., Ak, R., Moreira, G., Xu, M., Schifferer, B., Liu, B., and Oldridge, E. Llama- embed-nemotron-8b: A universal text embedding model for multilingual and cross-lingual tasks, 2025. URL https://arxiv.org/abs/2511.07025. Bansal, K., Loos, S. M., Rabe, M...

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2604.18584 2025

  3. [4]

    URL https: //doi.org/10.48550/arXiv.2505.22846

    doi: 10.48550/ARXIV.2505.22846. URL https: //doi.org/10.48550/arXiv.2505.22846. Kwon, W., Li, Z., Zhuang, S., Sheng, Y ., Zheng, L., Yu, C. H., Gonzalez, J. E., Zhang, H., and Stoica, I. Efficient memory management for large language model serving with pagedattention. InProceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles, 2023. Le...

    work page doi:10.48550/arxiv.2505.22846 2023

  4. [5]

    Generative Agents: Interactive Simulacra of Human Behavior

    doi: 10.48550/ARXIV.2304.03442. URL https: //doi.org/10.48550/arXiv.2304.03442. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V ., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V ., VanderPlas, J., Passos, A., Cour- napeau, D., Brucher, M., Perrot, M., and Duchesnay, E. Scikit-learn: Machine learning in python.J. Mach. ...

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2304.03442 2011

  5. [6]

    Foundations and Trends in Information Retrieval3(4), 333–389 (2009) https://doi.org/10.1561/1500000019

    URL https://dl.acm.org/doi/10.5555 /1953048.2078195. Penedo, G., Kydlíˇcek, H., Cappelli, A., Sasko, M., and Wolf, T. Datatrove: large scale data processing, 2024. URL https://github.com/huggingface/datat rove. Petrov, I., Dekoninck, J., Dimitrov, D. I., and Vechev, M. Not all proofs are equal: Evaluating llm proof quality beyond correctness, 2026. URL ht...

    work page doi:10.1561/1500000019 2024

  6. [7]

    URL https: //doi.org/10.1145/3626772.3657707

    doi: 10.1145/3626772.3657707. URL https: //doi.org/10.1145/3626772.3657707. Upadhyay, S., Kamalloo, E., and Lin, J. Llms can patch up missing relevance judgments in evaluation.CoRR, abs/2405.04727, 2024. doi: 10.48550/ARXIV.2405.04

    work page doi:10.1145/3626772.3657707 2024

  7. [8]

    URL https://doi.org/10.48550/arXiv .2405.04727. Wang, J. Z., Du, Z., Payattakool, R., Yu, P. S., and Chen, C.-F. A new method to measure the semantic similarity of go terms.Bioinformatics, 23(10):1274–1281, 05 2007. ISSN 1367-4803. doi: 10.1093/bioinformatics/btm087. URL https://doi.org/10.1093/bioinfor matics/btm087. Wang, L., Yang, N., Huang, X., Yang, ...

    work page internal anchor Pith review doi:10.48550/arxiv 2007

  8. [9]

    <tagName>

    A comma-separated list of tags # TASK For each of the provided tags assign a real number from the interval [0.0, 1.0]. The most relevant tag to the text must have a relevance score of 1.0 and the most irrelevant must have a score of 0.0. Align the remaining tags with respect to those. # OUTPUT SCHEMA Constrain your output to a pure JSON with no explanatio...

  9. [10]

    Capture the untrial step or idea that is the greatest hint for the solution

    Identify what makes the solution work conceptually, not how to carry it out. Capture the untrial step or idea that is the greatest hint for the solution

  10. [11]

    Don't include any annotations that are in the solution but not in the original problem statement

    Never include any multi-step reasoning, equations, or numeric computations. Don't include any annotations that are in the solution but not in the original problem statement

  11. [12]

    Never try to solve the problem on your own, and don't include your reasoning or thoughts

  12. [13]

    Output a single valid JSON object matching the schema below

  13. [14]

    Structure the ideas imperatively so they look like you are giving a hint to someone

  14. [15]

    noCoreIdea

    If the problem seems too easy or straightforward, or you can't identify a core idea, store its value as 'null' and set the 'noCoreIdea' to 'true'. # SCHEMA ```json { "noCoreIdea": <true|false>, "coreIdea": "<string - one short sentence (up to 30 words) naming the main insight to the problem>", "supportingIdeas": ["<strings - 0-3 short technique phrases>"]...

  15. [16]

    symmetry/invariants vs

    Technique overlap: - Are the same kinds of tools central (not merely mentioned)? - Are the same transformations used (e.g., factoring vs. symmetry/invariants vs. counting)?

  16. [17]

    Problem structure alignment: - Are the same intermediate objects introduced (auxiliary point, substitution, generating function, invariant quantity, etc.)? - Do both require the same "shape" of argument (e.g., construction + chase, or setup of recurrence + induction, or extremal argument + contradiction)? This criterion should be weighted lower than the t...

  17. [18]

    hard/easy

    Difficulty is NOT the criterion: - Prefer shared method and structure over "hard/easy"

  18. [19]

    Further, algebraic computations do not imply similarity if the core method is different, or if the algebra is just a technical detail rather than the main driver

    Penalize superficial similarity: - Do NOT reward matching variable names, story context, or domain language if the underlying method differs. Further, algebraic computations do not imply similarity if the core method is different, or if the algebra is just a technical detail rather than the main driver. Produce two subsections: - Sample 1 vs Target -- Sim...

  19. [20]

    The sample whose *main* technique is the same as the target's main technique

  20. [21]

    The sample whose *sequence of moves* (setup -> transformation -> key lemma -> finish) matches more closely

  21. [22]

    modular arithmetic

    The sample that relies on the same representation (e.g., algebraic manipulation vs. geometric configuration vs. combinatorial counting vs. graph reasoning). -------- OUTPUT FORMAT (must follow) - You may include your extracted profiles and comparisons above. - Output your final decision on the last line, formatted as either: $\\boxed{{1}}$ -> correspondin...

  22. [23]

    Technique overlap - Which techniques are shared? - Which shared techniques are central rather than incidental? - Are the same theorems, transformations, or proof strategies doing the main work?

  23. [24]

    Technique differences - Which important techniques appear in one but not the other? - Do the problems rely on different core ideas even if they are in the same broad subject?

  24. [25]

    Important rules: - Focus on the methods actually used in the solutions

    Structural alignment - Do the two solutions have a similar shape, such as: - setup -> substitution -> simplification -> conclusion - construction -> theorem application -> chase -> finish - recurrence -> induction - extremal argument -> contradiction - counting representation -> double count -> algebraic cleanup - This matters, but it should be weighted l...

  25. [26]

    \) and \\(

    DELIMITER CONVERSION: Replace all instances of \( ... \) and \\( ... \\) with standard dollar signs. - Use $...$ for inline text. - Use $$...$$ for display equations

  26. [27]

    UNIVERSAL MATH TAGGING: Apply math mode ($...$) to every single mathematical element without exception

  27. [28]

    CONTENT INTEGRITY: Do not solve the problem or edit the prose

  28. [29]

    FINAL WRAPPING: The entire output must be contained within \boxed{{ <your_formatted_text_here> }}

  29. [30]

    Finished Tasks

    NO VERBOSITY: Provide ONLY the \boxed{{...}} block. Example Transformation: Input: If the radius r is 5, find the area. Use \( \pi \). Output: \boxed{{If the radius $r$ is $5$, find the area. Use $\pi$.}} F.6. Human Annotator Instructions Human Annotator Instructions Goal: Compare Candidate 1 and Candidate 2 against the Target problem. Candidate 1: Choose...