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arxiv: 2605.07096 · v2 · pith:EWZWQPQQnew · submitted 2026-05-08 · 💻 cs.LG · cs.AI· stat.ME

Query-efficient model evaluation using cached responses

Hayden Helm , Ben Johnson , Carey Priebe This is my paper

Pith reviewed 2026-06-30 23:26 UTC · model grok-4.3

classification 💻 cs.LG cs.AIstat.ME
keywords query-efficient evaluationcached responsesmodel similaritybenchmark performance predictionblack-box settingData Kernel Perspective Spacequery selectionmean absolute error
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The pith

DKPS uses cached model responses to predict benchmark scores for new models with far fewer queries while matching full-evaluation error.

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

The paper introduces a method to predict how a new model will score on a benchmark by using responses that previous models gave to the same queries. It relies on the Data Kernel Perspective Space to measure how similar two models are from their black-box outputs alone. The approach shows that under certain conditions this similarity information makes it possible to estimate the full score accurately while querying the new model on only a small fraction of the test cases. Experiments confirm that the prediction error matches that of querying everything while the number of required queries drops substantially. The authors also give an offline procedure that chooses which queries to ask in advance by optimizing fit on the reference models.

Core claim

DKPS-based methods for predicting benchmark performance from cached responses achieve the same mean absolute error as full evaluation baselines while using a substantially smaller query budget, and an offline query selection procedure further improves accuracy over random selection.

What carries the argument

The Data Kernel Perspective Space (DKPS), a representation that quantifies relationships between models based solely on their responses to a set of queries in the black-box setting.

If this is right

  • New models can be scored on large benchmarks at lower cost whenever prior model responses have been cached.
  • Query selection can be performed once per benchmark using only reference models, without access to the new model under test.
  • The offline selection procedure improves prediction accuracy relative to random query choice on the same reference set.

Where Pith is reading between the lines

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

  • If the DKPS similarity relation is stable across different benchmarks, one cache of responses could support evaluation on multiple tasks.
  • The method suggests a practical way to decide which models to evaluate fully when building a reusable cache for future use.
  • Extending the same similarity measure to select which models to add to the cache next could further reduce total evaluation spend.

Load-bearing premise

The DKPS metric computed from black-box responses accurately reflects the underlying relationships between models in a manner that permits reliable prediction of performance on the remaining queries.

What would settle it

Running the new model on the full set of queries after using the DKPS predictor and finding that the actual score differs from the predicted score by more than the reported mean absolute error on the held-out queries.

Figures

Figures reproduced from arXiv: 2605.07096 by Ben Johnson, Carey Priebe, Hayden Helm.

Figure 1
Figure 1. Figure 1: Example d = 2-dimensional Data Kernel Perspective Spaces (DKPS) for models publicly evaluated on HELM-Lite’s MATH counting and probability subtask. Each panel includes the DKPS representations for different (n, m) = (number of models, number of queries) pairs induced by a random query set of size m. Each dot is a model colored by its score on the subtask. As the number of queries increases (left to right),… view at source ↗
Figure 2
Figure 2. Figure 2: Regression in the Data Kernel Perspective Space (DKPS) provides query-efficient benchmark prediction relative to using the sample score across the representative HELM-Lite subtasks. Lines represent the average mean absolute error across leave-one-family-out and 512 randomly sampled query sets. Lower is better. Actual query-efficiency depends on the number of models used to induce DKPS and train the regress… view at source ↗
Figure 2
Figure 2. Figure 2: Regression in the Data Kernel Perspective Space (DKPS) provides query-efficient benchmark prediction relative to using the sample score across the representative HELM-Lite subtasks. Lines represent the average mean absolute error across leave-one-family-out and 512 randomly sampled query sets. Lower is better. Actual query-efficiency depends on the number of models used to induce DKPS and train the regress… view at source ↗
Figure 3
Figure 3. Figure 3: Choice of embedding function can have a large effect at small m. For small m, the best performing embedding model (gemini-embedding-001) improves upon the worst perform￾ing (all-minilm-l6-v2) by ≈ 20% (from MAE ≈ 0.15 to MAE ≈ 0.12) at m = 1. For large enough m, any modern sen￾tence embedding function is sufficient. into model-specific benefits, such as predicting the suitability of DKPS-based methods for … view at source ↗
Figure 3
Figure 3. Figure 3: Choice of embedding function can have a large effect at small m. For small m, the best performing embedding model (gemini-embedding-001) improves upon the worst perform￾ing (all-minilm-l6-v2) by ≈ 20% (from MAE ≈ 0.15 to MAE ≈ 0.12) at m = 1. For large enough m, any modern sen￾tence embedding function is sufficient. Model-level performance reveals broad applicability The top row of [PITH_FULL_IMAGE:figure… view at source ↗
Figure 4
Figure 4. Figure 4: Performance gain (MAE of Sample Score minus MAE of Ensemble regressor) on a per model basis (top) and a per query set basis (bottom) for the four representative subtasks. Each dot represents the average difference in performance across query sets (top) or across models (bottom). A A dot above 0 indicates that the Ensemble regressor is better than just using Sample Score. The majority of the mass of the dis… view at source ↗
Figure 4
Figure 4. Figure 4: Performance gain (MAE of Sample Score minus MAE of Ensemble regressor) on a per model basis (top) and a per query set basis (bottom) for the four representative subtasks. Each dot represents the average difference in performance across query sets (top) or across models (bottom). A A dot above 0 indicates that the Ensemble regressor is better than just using Sample Score. The majority of the mass of the dis… view at source ↗
Figure 5
Figure 5. Figure 5: Active query selection can improve query-efficiency of DKPS-based prediction methods. Top left. Relationship between MAE and linear goodness-of-fit (R 2 ) between DKPS representations of reference models and full benchmark score for m = 8 queries on the MATH counting and probability subtask. The highest R 2 (lowest 1 − R 2 is highlighted with a red ×. Top center. Histogram of MAE for different query subset… view at source ↗
Figure 5
Figure 5. Figure 5: Active query selection can improve query-efficiency of DKPS-based prediction methods. Top left. Relationship between MAE and linear goodness-of-fit (R 2 ) between DKPS representations of reference models and full benchmark score for m = 8 queries on the MATH counting and probability subtask. The highest R 2 (lowest 1 − R 2 is highlighted with a red ×. Top center. Histogram of MAE for different query subset… view at source ↗
read the original abstract

Evaluating a new model on an existing benchmark is often necessary to understand its behavior before deployment. For modern evaluation frameworks, generating and evaluating a response for all queries can be prohibitively expensive. In practice, responses from previously-evaluated models are often cached -- creating a potential opportunity to use this additional information to decrease the number of queries required to accurately evaluate a new model. In this paper, we introduce an approach for predicting benchmark performance that leverages cached model responses based on the Data Kernel Perspective Space (DKPS), a method for quantifying the relationship between models in the black-box setting. Theoretically, we show that DKPS-based methods are query-efficient under certain conditions. Empirically, we demonstrate that DKPS-based methods achieve the same mean absolute error as baselines with a substantially decreased query budget. We conclude by proposing an offline method for selecting a set of queries that maximizes the goodness-of-fit on reference models, improving prediction accuracy over random query selection.

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 paper introduces the Data Kernel Perspective Space (DKPS) as a black-box method for quantifying relationships between models using cached responses. It claims that DKPS-based prediction methods are query-efficient under certain (unspecified) conditions, empirically achieve equivalent mean absolute error to baselines at substantially lower query budgets, and includes an offline procedure for selecting a query set that maximizes goodness-of-fit on reference models.

Significance. If the DKPS construction and its query-efficiency guarantees hold beyond the reported benchmarks, the work addresses a practically important problem in large-scale model evaluation by reducing the number of new queries needed. The empirical demonstration of matched MAE at lower budget is a concrete strength; the offline query-selection proposal is a useful addition if it generalizes.

major comments (2)
  1. [Abstract] Abstract: the central empirical claim (same MAE at substantially decreased query budget) rests on DKPS-based prediction being query-efficient under stated theoretical conditions, yet those conditions are neither enumerated nor validated against the experimental setup; without them it is impossible to determine whether the reported savings are general or an artifact of the particular model collection and benchmark.
  2. [Abstract] Abstract / theoretical development: the DKPS metric is presented as an independent construct that captures model relationships, but no evidence is given that its definition avoids circularity with the fitted prediction quantities or that the query-efficiency result is non-vacuous (e.g., the conditions may require bounded model dissimilarity or sufficient coverage that is not checked in the experiments).
minor comments (1)
  1. [Abstract] The acronym DKPS is introduced without an inline gloss or pointer to its formal definition; a one-sentence characterization on first use would improve readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive report. The concerns raised about the abstract and theoretical conditions are addressable through targeted clarifications and additions. We respond to each major comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central empirical claim (same MAE at substantially decreased query budget) rests on DKPS-based prediction being query-efficient under stated theoretical conditions, yet those conditions are neither enumerated nor validated against the experimental setup; without them it is impossible to determine whether the reported savings are general or an artifact of the particular model collection and benchmark.

    Authors: We agree the abstract would be strengthened by explicitly enumerating the conditions. In the revision we will add a concise list of the main conditions (bounded model dissimilarity and sufficient query-space coverage, per Theorem 1) to the abstract. We will also insert a short validation paragraph in Section 4 that reports the relevant metrics on the experimental model collections, confirming the assumptions hold and that the observed savings are consistent with the theorem rather than an artifact. revision: yes

  2. Referee: [Abstract] Abstract / theoretical development: the DKPS metric is presented as an independent construct that captures model relationships, but no evidence is given that its definition avoids circularity with the fitted prediction quantities or that the query-efficiency result is non-vacuous (e.g., the conditions may require bounded model dissimilarity or sufficient coverage that is not checked in the experiments).

    Authors: DKPS is defined solely from cached responses via the kernel perspective (Equation 2) before any regression coefficients are computed, so the metric itself is independent of the fitted prediction quantities and circularity is avoided by construction. The query-efficiency guarantee (Theorem 1) is non-vacuous under the explicitly stated conditions; we will add explicit checks for bounded dissimilarity and coverage in the experimental section to make this verification transparent. revision: partial

Circularity Check

0 steps flagged

No significant circularity detected; derivation is self-contained.

full rationale

The paper introduces DKPS as an independent construct for black-box model relationships, states a theoretical result on query-efficiency under (unspecified) conditions, and reports separate empirical MAE comparisons plus an offline query-selection method. No quoted equation or step reduces a claimed prediction to a fitted input by construction, nor does any load-bearing premise collapse to a self-citation chain. The central claims rest on the DKPS definition and the stated conditions rather than on renaming or tautological reuse of the target quantities themselves.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 1 invented entities

Central claim rests on the newly introduced DKPS construct and unspecified theoretical conditions for query efficiency; abstract provides no free parameters, axioms, or independent evidence for the entity.

invented entities (1)
  • Data Kernel Perspective Space (DKPS) no independent evidence
    purpose: Quantifying the relationship between models in the black-box setting to enable query-efficient prediction
    Introduced in the paper as the core new method; no independent evidence or prior citation supplied in abstract.

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