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REVIEW 4 major objections 5 minor 31 references

ONEBench to Test Them All: Sample-Level Benchmarking Over Open-Ended Capabilities

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read The paper claims that converting all sample-level model measurements into ordinal rankings and aggregating them with the Plackett-Luce model recovers the ground-truth model ranking even when up to 95% of measurements are missing, making…

desk verdict ONEBench is a useful, honest benchmarking framework; the recovery claim is only validated against a mean-score ground truth and random missingness, but the contribution stands. read the letter →

arxiv 2412.06745 v2 pith:6Y6XGD4X submitted 2024-12-09 cs.LG cs.CLcs.CV

Adhiraj Ghosh , Sebastian Dziadzio , Ameya Prabhu , Vishaal Udandarao , Samuel Albanie , Matthias Bethge This is my paper · ORCID
classification cs.LGcs.CLcs.CV
keywords open-endedbenchmarkingsample-levelevaluationPlackett-Lucemodelrankaggregationordinalrankingsmissingdatarobustnesscapabilityprobingfoundation
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

ONEBench proposes replacing fixed task-specific benchmarks with a continuously expanding pool of sample-level tests, where each test sample votes by ranking the models it was used on. The paper argues that aggregating these partial, heterogeneous ordinal rankings with the Plackett-Luce model recovers the underlying model ranking even when most samples are missing for most models. This matters because it would make benchmarking cheaper—up to 20 times fewer measurements—and would let users construct personalized capability benchmarks from a shared pool. The argument combines theoretical identifiability and sample-efficiency guarantees with empirical correlations on real language and vision-language leaderboards.

What carries the argument

The load-bearing mechanism is the Plackett-Luce random utility model: each model has a positive score, and the probability of seeing a particular ranking is the product of sequential choice probabilities. Maximum likelihood estimation over these scores from partial rankings is the aggregation step, and its properties—strict concavity, identifiability up to an additive constant, and minimax-optimal sample efficiency when comparisons form an expander graph—are what make sparse, heterogeneous sample-level data recoverable. Rank-breaking converts partial rankings into near-optimal pairwise estimates in the implementation.

What would settle it

Generate synthetic benchmarks from a non-Plackett-Luce process, such as a Thurstone model where models have unequal score variances or correlated errors, then run the proposed aggregation under 50% to 95% missing data; if the recovered ranking's Kendall tau drops far below the reported values, the recovery guarantee is specific to the assumed model rather than a general property of ordinal sample-level aggregation.

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Extended reading notes

Core claim

The central discovery is that model evaluation can be reframed as preference aggregation at the sample level: each test item is a voter over the models it was evaluated on, and the Plackett-Luce model turns those sparse votes into a global ranking. The paper claims that this aggregation is identifiable, because it asymptotically recovers ground-truth utility scores, and sample-efficient enough that accurate rankings survive up to 95% missing measurements. On real benchmarks, the recovered rankings correlate strongly with mean-score rankings on homogeneous data, and the approach handles heterogeneous metrics by first converting all measurements to ordinal comparisons.

Load-bearing premise

The whole approach rests on assuming that real benchmark measurements are generated by a Plackett-Luce random utility model; if that generative assumption is violated, the theoretical recovery guarantees no longer apply and the empirical ranking is only matching the mean-score ground truth used to validate it.

Editorial extensions

If this is right

  • A single expanding sample pool can serve many custom benchmarks, because the same sample-level rankings are reused across queries.
  • Evaluation cost can drop by up to 20 times, because accurate rankings survive about 95% missing measurements.
  • Model rankings become more comparable across benchmarks that use different metrics, because all measurements are first converted to ordinal comparisons.
  • Capability-specific queries produce rankings that differ systematically from the global ranking, revealing model specialization.
  • The theoretical identifiability and sample-efficiency results justify aggregating incomplete and heterogeneous test sets into one ranking.

Reading between the lines

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

  • Beyond the paper, the same ordinal aggregation could be applied to other open-ended evaluation settings, such as text-to-image generation or code synthesis, where metrics are heterogeneous and coverage is sparse.
  • Beyond the paper, an adaptive version that chooses which sample-model measurements to collect next could reach the same ranking accuracy with even less compute than random subsampling.
  • Beyond the paper, the claimed ground-truth recovery would be strengthened by validating against an independent held-out sample set, rather than only against mean-score rankings from the same leaderboards.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper introduces ONEBench, a sample-level benchmarking paradigm that pools heterogeneous measurement data across multiple benchmarks and aggregates them with a Plackett-Luce (PL) rank model. The authors argue that converting all model measurements into ordinal rankings and fitting PL parameters via maximum likelihood yields an identifiable, sample-efficient aggregation that recovers ground-truth model scores, remains accurate with up to 95% randomly missing data, and supports open-ended capability queries through semantic and metadata retrieval. The manuscript includes four real-world benchmark suites (HELM, Open LLM Leaderboard, VHELM, LMMs-Eval), comparisons against Elo, Bradley-Terry, Borda, and Dowdall aggregation, synthetic data experiments, and a capability-probing study with manual annotation of retrieval quality.

Significance. If the central claims held as stated, the work would be a valuable contribution to foundation-model evaluation: it provides an open, expanding infrastructure, a unified treatment of heterogeneous metrics, and a concrete demonstration that ordinal aggregation can be more robust than Elo-style ratings. The paper ships two benchmark pools (ONEBench-LLM, ONEBench-LMM), compares several aggregation methods on shared data, and includes capability-probing evaluations with human-annotated retrieval quality, which are useful practical artifacts. The significance of the theoretical claims, however, is currently limited by validation issues: the real-data ground truth is constructed from the same scores used to form the PL input rankings, and the synthetic validation uses the PL noise model itself. These issues do not destroy the framework's practical potential, but they prevent the paper from substantiating its headline guarantee of asymptotically recovering ground-truth scores on real, misspecified data.

major comments (4)
  1. [Section 3.2.1 and Tables 1-2] The ground truth for real-data evaluation is defined as the mean min-max normalized score computed from the same per-sample measurements that are then converted into the ordinal rankings fed to the PL estimator. The reported Kendall's tau therefore measures agreement between two summaries of the same input data, not recovery of an independently defined latent quality. Under the PL random-utility model, MLE is expected to rank models according to their mean latent scores, so a nontrivial correlation with mean scores is partly built in by construction. The Limitations section concedes that the statistical modeling assumptions 'may not always hold', but no experiment quantifies the effect of this concession. I request an external-validity check: for example, fit PL on a subset of benchmarks or samples and compare against leaderboard rankings from a held-out benchmark, or compare against an independent preference signal such as human arena win rates that were not used in fitting. Without such a check, the real-data results do not test the load-bearing claim of recovering an underlying score independent of the defined ground truth.
  2. [Section 3.2.3 and Figure 3] The robustness experiments simulate missingness by uniformly random removal of samples or model measurements. ONEBench's motivating incompleteness, however, is structural: different benchmarks evaluate largely disjoint model subsets, and models enter the pool over time, so the observed measurement matrix contains blocks of missing entries rather than uniform random holes. Uniform subsampling from a fully observed matrix cannot reproduce the sparsity pattern that the framework is designed to handle. The claim of maintaining accurate rankings at 95% missing data should be tested under structured missingness, such as holding out entire benchmarks for subsets of models, or withholding all measurements for models added in a later time period, and reporting Kendall's tau under those patterns.
  3. [Appendix D, Synthetic Data Experiments] The synthetic experiments generate scores from Gumbel-distributed random variables, which is precisely the noise model underlying the Plackett-Luce distribution assumed in Section 3.1. Table 7 therefore verifies that the MLE recovers the parameters of the assumed generative model; it cannot reveal behavior when real measurements are generated differently (for example, by binary correctness with sample-dependent difficulty, log-normal continuous scores, or heterogeneous per-benchmark noise). Since the real-data validation is circular in the sense described above, these synthetic experiments are the only currently available test of recovery, and they only cover the favorable same-model case. I ask for a misspecification experiment with non-PL generative distributions to quantify the rank-recovery error when the PL assumption is violated.
  4. [Section 3.1, Property 1] The identifiability guarantee is stated for data where all models are comparable through a directed path. The paper does not report whether the real measurement graph in ONEBench-LLM or ONEBench-LMM is connected, nor how many samples each model appears in. If the comparison graph has disconnected components, MLE cannot identify relative scores across components, making a global Kendall's tau ill-defined. Please report the connectivity of the measurement graph, the largest connected component, and per-model sample counts, or restrict global ranking claims to the connected component containing the reference model.
minor comments (5)
  1. [Title, Abstract, and throughout] Please fix typographical errors: 'ONEBENCHto' in the title, 'Placket-Luce' instead of 'Plackett-Luce' in the main text, 'continuosly' in Section 2.1, 'practioners' in Appendix F.2, and 'over-ended' in Appendix G.
  2. [Section 3.1, Eq. (1)] The statement that PL parameters are identifiable 'up to an arbitrary additive constant' is imprecise: the gamma parameters in Eq. (1) are identifiable only up to a multiplicative constant, or equivalently additive in log-score space. The strict concavity claim for the likelihood also holds only after fixing this gauge, e.g., by setting the baseline model's log-score to zero. Please state this more carefully.
  3. [Section 3.2.1 and Appendix A] The min-max normalization used to construct the ground truth is not precisely specified: it should be stated whether normalization is applied per benchmark, per metric, or across the entire pooled data, since different choices produce different ground-truth rankings and affect reproducibility.
  4. [Section 4.3 and Table 3] The number of concepts is reported inconsistently: Section 4.3 mentions 'a curated set of 50 concepts', Table 3 reports 40 for LLM and 50 for LMM, and Appendix F.2 says 'ONEBench-LMM(50 vs 40)', which appears reversed. Please align these counts and clarify which concepts were used for which analysis.
  5. [Section 3.2.1, Metrics] For the comparison in Table 1, the paper reports mean and variance over three runs but does not state the source of stochasticity (e.g., random subsampling, rank-breaking randomness, or initialization). Please specify the randomness that produces the variance.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the PL aggregation is validated against an explicitly acknowledged self-defined ground truth, but no fitted parameter is renamed as a prediction and the theoretical guarantees are external.

full rationale

The paper's derivation chain is: postulate a Plackett-Luce random utility model (Sec. 3.1), invoke external identifiability and sample-complexity results (Xia 2019; Han and Xu 2023; Maystre and Grossglauser 2015), fit the model by MLE to sample-level ordinal rankings, and then compare the resulting ranking to leaderboard mean-score rankings (Sec. 3.2.1). The real-data ground truth is computed from the same raw measurements, so the validation is an in-sample consistency check rather than recovery of an independent latent quality. However, the paper explicitly states "there is no other ground truth available for real data" and does not feed the ground-truth ranking into the PL estimation; the comparison is between two different aggregations of the same measurements, and the missing-data experiments compare full-data versus randomly subsampled rankings, which is a legitimate robustness check. The synthetic experiments in Appendix D generate data from the PL/Gumbel model, so they test the estimator on its own generative model; this is a self-consistency check, not circularity, because the ground truth there is known independently and is not used in fitting. Self-citations (Prabhu et al. 2024, Udandarao et al. 2024a) are contextual comparisons, not load-bearing. The Limitations section candidly concedes that the PL distributional assumptions "may not always hold". No equation reduces to another by construction, no fitted parameter is renamed as a prediction, and no load-bearing step depends on an unverified self-citation.

Assumptions & free parameters 2 free parameters · 5 assumptions · 0 invented entities

The central method relies on the PL model assumption and the random missingness assumption. The paper introduces no new physical or conceptual entities beyond the benchmark framework itself.

free parameters (2)
  • PL model score gamma_k for each model = MLE estimates
    Latent ability parameters inferred by the aggregation algorithm; they are the output rather than tuned inputs, but they are free parameters of the model.
  • Semantic retrieval thresholds = 0.3 for LLM, 0.7 for LMM
    Hand-chosen thresholds for kNN cosine similarity to retrieve relevant samples; affect capability probing only, not the aggregation method.
assumptions (5)
  • domain assumption The Plackett-Luce model correctly describes the generation of sample-level rankings in real benchmarks.
    Section 3.1: "We begin by postulating a ground-truth statistical model generating the data." The theoretical guarantees rely on this assumption.
  • domain assumption Missing model measurements are missing at random.
    Section 3.2.3 simulates missing data by random removal; the claimed robustness to 95% missing data depends on this assumption.
  • domain assumption Ordinal rankings derived from cardinal scores preserve enough information for accurate ranking.
    Appendix C.2 discusses information loss but argues ordinal measurements are more robust; the method's validity depends on this.
  • ad hoc to paper Ground truth model rankings can be approximated by mean min-max normalized scores across benchmarks.
    Section 3.2.1: "We consider these scores as the ground truth measurement and generate a ground truth model ranking from these scores." This is used as the evaluation target.
  • domain assumption The comparison graph is connected, or the baseline model connects all components, so PL parameters are identifiable.
    Property 1 cites identifiability via a directed path; the paper does not verify connectivity of the real comparison graph.

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Cite this review

Pith. "Pith review of ONEBench to Test Them All: Sample-Level Benchmarking Over Open-Ended Capabilities." pith.science (2026). https://pith.science/paper/6Y6XGD4X

@misc{pith2026241206745,
  author       = {Pith},
  title        = {Pith review of: ONEBench to Test Them All: Sample-Level Benchmarking Over Open-Ended Capabilities},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6Y6XGD4X}},
  note         = {Machine review of arXiv:2412.06745}
}
read the original abstract

Traditional fixed test sets fall short in evaluating open-ended capabilities of foundation models. To address this, we propose ONEBench(OpeN-Ended Benchmarking), a new testing paradigm that consolidates individual evaluation datasets into a unified, ever-expanding sample pool. ONEBench allows users to generate custom, open-ended evaluation benchmarks from this pool, corresponding to specific capabilities of interest. By aggregating samples across test sets, ONEBench enables the assessment of diverse capabilities beyond those covered by the original test sets, while mitigating overfitting and dataset bias. Most importantly, it frames model evaluation as a collective process of selecting and aggregating sample-level tests. The shift from task-specific benchmarks to ONEBench introduces two challenges: (1)heterogeneity and (2)incompleteness. Heterogeneity refers to the aggregation over diverse metrics, while incompleteness describes comparing models evaluated on different data subsets. To address these challenges, we explore algorithms to aggregate sparse measurements into reliable model scores. Our aggregation algorithm ensures identifiability(asymptotically recovering ground-truth scores) and rapid convergence, enabling accurate model ranking with less data. On homogenous datasets, we show our aggregation algorithm provides rankings that highly correlate with those produced by average scores. We also demonstrate robustness to ~95% of measurements missing, reducing evaluation cost by up to 20x with little-to-no change in model rankings. We introduce ONEBench-LLM for language models and ONEBench-LMM for vision-language models, unifying evaluations across these domains. Overall, we present a technique for open-ended evaluation, which can aggregate over incomplete, heterogeneous sample-level measurements to continually grow a benchmark alongside the rapidly developing foundation models.

Figures

Figures reproduced from arXiv: 2412.06745 by the authors.

Figure 1
Figure 1. The ONEBench Framework. Left: ONEBench comprises a set of models, a pool of data samples spanning multiple test sets, metadata describing models and data samples, and a collection of sample-level measurements. Right: the user formulates a query to capture the desired model capability, using a mix of structured metadata filters and semantic search. Selected models are then ranked on a subset of data samples that meet… view at source ↗
Figure 2
Figure 2. Top-10 model ranking changes across different aggregation methods. Plackett-Luce (Ours) shows the most similarity to the Ground Truth model rankings (GT). However, there is a progressive degradation in ranking accuracy for LMArena (LMArena) and Elo (ELO). Dataset Elo LMArena Ours HELM 0.35 ± 0.13 0.85 ± 0.00 0.88 ± 0.00 Leaderboard 0.21 ± 0.07 0.97 ± 0.00 0.99 ± 0.00 VHELM 0.63 ± 0.02 0.69 ± 0.00 0.80 ± 0.00 LMMs-Ev… view at source ↗
Figure 3
Figure 3. Sample-efficient convergence and robustness to sparsity. Kendall rank correlation coefficient between ground-truth ranking and different ranking methods as we randomly remove individual data samples (top) and model measurements (bottom). Methods typically remain robust to missing data, with Plackett-Luce consistently achieving higher correlation, even with 95% measurements missing. The strongly convex Plackett-Luce … view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Capability Probing (Qualitative): we provide six sample retrieval results for a set of queries covering a diverse set of topics and report the top-5 models for each query. random retrievals of n samples from the full pool and compute their average correlation. Results …
Figure 5
Figure 5. Figure 5: Constituent datasets of ONEBench-LLM (left) and OneBench-LMM (right). We provide task type, metric, and license about each dataset in table 5 and table 6. Dataset Source Task Size Metric License Cardinal LegalBench (Guha et al., 2024) HELM Legal 1K QEM Unknown MATH (He…
Figure 6
Figure 6. Figure 6: Additional qualitative analysis for ONEBench’s capability probing for selected queries. [PITH_FULL_IMAGE:figures/full_fig_p033_6.png]

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