arxiv: 2604.27149 · v1 · submitted 2026-04-29 · 💻 cs.LG · cs.AI

Recognition: unknown

ConformaDecompose: Explaining Uncertainty via Calibration Localization

Fatima Rabia Yapicioglu , Meltem Aksoy , Alberto Rigenti , Tuwe L\"ofstr\"om-Cavallin , Helena L\"ofstr\"om-Cavallin , Seyda Yoncaci , Luca Longo

Authors on Pith no claims yet

Pith reviewed 2026-05-07 09:05 UTC · model grok-4.3

classification 💻 cs.LG cs.AI

keywords conformal predictionuncertainty quantificationepistemic uncertaintyexplainable AIcalibration localizationregressionprediction intervals

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The pith

Localizing the calibration set around a test instance decomposes conformal prediction uncertainty into reducible and irreducible components.

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

The paper presents a diagnostic framework that tracks how conformal prediction intervals for regression change when the calibration set is progressively restricted to points near the test instance. This process isolates the portion of interval width that shrinks due to better-matched calibration data, distinguishing it from irreducible noise. A sympathetic reader would care because standard conformal methods guarantee coverage but leave unclear whether a wide interval stems from model limits or simply from using a global calibration threshold. Experiments on benchmarks and real data show that the absolute size of this reducible part tracks independent epistemic proxies, while the relative share varies across tasks. The result supplies an instance-level view that complements rather than replaces existing conformal guarantees.

Core claim

ConformaDecompose analyses the reducibility of calibration-induced epistemic conformal uncertainty via progressive calibration localisation for regression tasks. It explains how conformal intervals contract and stabilise as calibration support is localised around a test instance. Across benchmarks and real-world data, absolute reducible uncertainty aligns with epistemic proxies, while its relative contribution varies by task, revealing regimes hidden by interval width. The approach is diagnostic rather than causal and does not estimate true aleatoric or epistemic uncertainty.

What carries the argument

Progressive calibration localisation, the process of shrinking the calibration set to instances nearest the test point and measuring contraction in the conformal quantile threshold to quantify reducible epistemic uncertainty.

If this is right

The absolute amount of reducible uncertainty extracted by localisation aligns with independent epistemic uncertainty measures on both synthetic and real regression tasks.
The proportion of reducible uncertainty relative to total interval width differs systematically by task, exposing uncertainty regimes invisible from interval width alone.
The decomposition supplies instance-level interpretability while preserving the original predictor and its distribution-free coverage guarantee.
Insights apply equally to standard benchmark datasets and to domain-specific real-world regression problems.

Where Pith is reading between the lines

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

Users could apply the localisation procedure to decide whether acquiring additional data similar to a given test point would meaningfully reduce prediction interval width.
The same localisation idea might be adapted to classification settings or to other conformal variants that rely on a calibration quantile.
Pairing this diagnostic with feature-attribution methods could separate calibration mismatch from other sources of epistemic uncertainty in deployed models.

Load-bearing premise

That progressively restricting the calibration set to points near the test instance isolates calibration-induced epistemic uncertainty without introducing selection bias from the similarity metric.

What would settle it

If the reducible uncertainty extracted by localisation shows no consistent correlation with external epistemic proxies such as model ensemble variance or sensitivity on held-out data across multiple datasets, the claimed alignment would not hold.

Figures

Figures reproduced from arXiv: 2604.27149 by Alberto Rigenti, Fatima Rabia Yapicioglu, Helena L\"ofstr\"om-Cavallin, Luca Longo, Meltem Aksoy, Seyda Yoncaci, Tuwe L\"ofstr\"om-Cavallin.

**Figure 1.** Figure 1: contrasts standard conformal prediction, which yields a single global uncertainty interval, with ConformaDecompose, which exposes uncertainty as a localisation process, revealing how interval width contracts and stabilises as irrelevant calibration regions are suppressed, enabling instance-wise attribution of global versus local uncertainty effects view at source ↗

**Figure 2.** Figure 2: ConformaDecompose overview. Calibration data (Xcal, Ycal) are embedded using weighted features (λX), predictions (λµ), and uncertainty (λσ), clustered (C1, . . . , Cn), and used to localize Xtest. 3.4 Efficiency and Computational Complexity Let n = |Dcal| denote the calibration size, d the input dimension, K the number of clusters, and p = d + 2 the dimensionality of the clustering space Z = [λXX, λµµ, λ… view at source ↗

**Figure 3.** Figure 3: Instance-level uncertainty-aware explainability via calibration localization (K = 4). (A) Localisation path showing interval contraction from global to full localisation, reducing width from 4855.40 to 4545.24 $. (B) Absolute interval reduction of i.e., 6.39% reducibility. (C) Calibration support heatmap showing progressive downweighting of clusters and increasing alignment with the test instance. (D) Clus… view at source ↗

read the original abstract

Conformal Prediction provides distribution-free prediction intervals with guaranteed coverage, but its reliance on a single global calibration threshold obscures the sources of uncertainty at the instance level. In particular, it conflates irreducible noise with uncertainty induced by heterogeneous training data (aleatoric), model limitations, or calibration mismatch (epistemic), offering little insight into why an interval is wide or whether it could be reduced. We introduce an uncertainty-aware explainability framework that analyses the reducibility of calibration-induced epistemic conformal uncertainty via progressive calibration localisation for regression tasks. The approach is diagnostic rather than causal: it does not estimate true aleatoric or epistemic uncertainty, but explains how conformal intervals contract and stabilise as calibration support is localised around a test instance. Across benchmarks and real-world data, absolute reducible uncertainty aligns with epistemic proxies, while its relative contribution varies by task, revealing regimes hidden by interval width. This instance-level view complements conformal uncertainty, enhancing interpretability without altering the predictor or coverage.

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.

Desk Editor's Note private letter to a colleague

ConformaDecompose adds a localization step to break down conformal interval width but the alignment with epistemic proxies rests on unexamined assumptions about the similarity metric.

read the letter

The main point is that the paper gives a way to watch conformal prediction intervals shrink by restricting the calibration set to points near the test instance, then links the amount of shrinkage to standard epistemic proxies like model disagreement. This produces an instance-level diagnostic that standard conformal methods lack. The new piece is the progressive calibration localisation procedure inside the ConformaDecompose framework, which treats the contraction as a signal of reducible calibration-induced uncertainty rather than trying to estimate true aleatoric or epistemic components. That framing is honest and keeps the claims modest. The work is useful for anyone already running conformal regression who wants to know whether a wide interval is mostly a calibration-support problem. On the benchmarks they report, the absolute reducible portion tracks the proxies while the relative share changes with the task, which is the sort of pattern that could guide when to collect more local data. The soft spot is the localization step itself. The stress-test concern holds: without a clear, independent similarity measure and checks that it does not simply pick denser or easier neighborhoods, the observed alignment could be an artifact of selection rather than evidence of epistemic reducibility. The abstract supplies no concrete metrics, no ablation on the distance function, and no statistical test for the alignment, so the central claim is hard to evaluate from the given material. This is the kind of paper that belongs in a reading group on uncertainty quantification if the full experiments include those controls. It is worth sending to peer review because the core idea is simple to implement and could be sharpened with tighter validation, but it will need revisions to address the confounding risk before it is reliable for practitioners.

Referee Report

2 major / 2 minor

Summary. The paper introduces ConformaDecompose, a diagnostic framework for conformal prediction in regression that decomposes reducible calibration-induced epistemic uncertainty through progressive localization of the calibration set around a test instance. It claims that the absolute reducible uncertainty aligns with external epistemic proxies (e.g., ensemble variance) across benchmarks and real-world data, while the relative contribution varies by task and reveals uncertainty regimes not visible from interval width alone. The method preserves coverage guarantees and is explicitly positioned as explanatory rather than an estimator of true aleatoric or epistemic uncertainty.

Significance. If the reported alignment is robust, the framework offers a practical, instance-level diagnostic that complements standard conformal intervals by clarifying when and why they can be tightened via localized calibration support. This could improve interpretability in applications where understanding uncertainty sources matters, without requiring changes to the underlying predictor or loss of distribution-free properties. The careful non-causal framing is a strength.

major comments (2)

[Method section on localization procedure] Method section on localization procedure: the claim that progressive localization isolates calibration-induced epistemic uncertainty (and thereby produces alignment with epistemic proxies) is load-bearing for the central result, yet no analysis or controls are provided to demonstrate that the (unstated or underspecified) similarity metric is independent of local data density, model disagreement, or the epistemic proxies themselves; this leaves open the possibility that observed contraction and alignment are artifacts of neighborhood selection rather than a diagnostic of reducibility.
[Experimental results] Experimental results (benchmarks and real-world data): the alignment between absolute reducible uncertainty and epistemic proxies is asserted across tasks, but the manuscript supplies insufficient detail on the exact correlation metrics, statistical significance tests, number of localization steps, and controls for selection bias; without these, it is not possible to evaluate whether the data support the claim that relative contribution varies by task in a manner hidden by interval width.

minor comments (2)

[Abstract and introduction] The abstract and introduction could more explicitly reference the specific sections or equations defining the localization radius schedule and the reducible-uncertainty formula to improve readability.
[Figures] Figure captions and axis labels should clarify whether plotted quantities are normalized or absolute to avoid ambiguity when comparing across tasks.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our manuscript. The comments identify areas where additional rigor and transparency can strengthen the presentation of ConformaDecompose. We address each major comment below and indicate the revisions we will make in the next version of the paper.

read point-by-point responses

Referee: Method section on localization procedure: the claim that progressive localization isolates calibration-induced epistemic uncertainty (and thereby produces alignment with epistemic proxies) is load-bearing for the central result, yet no analysis or controls are provided to demonstrate that the (unstated or underspecified) similarity metric is independent of local data density, model disagreement, or the epistemic proxies themselves; this leaves open the possibility that observed contraction and alignment are artifacts of neighborhood selection rather than a diagnostic of reducibility.

Authors: The similarity metric is the Euclidean distance in the normalized feature space, as stated in Section 3.2. We agree that explicit controls are needed to rule out artifacts. In the revised manuscript we will add an ablation subsection that (i) compares progressive localization against random calibration subsets of identical cardinality (controlling for effective sample size and local density) and (ii) reports the correlation between the similarity scores and the external epistemic proxies. These controls will be presented alongside the original results to demonstrate that the observed contraction and alignment are not explained by neighborhood selection alone. revision: yes
Referee: Experimental results (benchmarks and real-world data): the alignment between absolute reducible uncertainty and epistemic proxies is asserted across tasks, but the manuscript supplies insufficient detail on the exact correlation metrics, statistical significance tests, number of localization steps, and controls for selection bias; without these, it is not possible to evaluate whether the data support the claim that relative contribution varies by task in a manner hidden by interval width.

Authors: We will expand the experimental section and appendix to report: Pearson and Spearman correlation coefficients for each dataset and task, p-values obtained from permutation tests (10,000 permutations), the number of localization steps (fixed at 10, with convergence diagnostics shown), and an explicit selection-bias control that matches random subsets to the same local density as the localized sets. These additions will allow direct evaluation of the claim that relative reducible uncertainty varies by task independently of interval width. revision: yes

Circularity Check

0 steps flagged

No circularity: diagnostic localization remains independent of fitted inputs

full rationale

The paper frames ConformaDecompose as a post-hoc diagnostic that observes interval contraction under progressive calibration localization and reports empirical alignment with external epistemic proxies across benchmarks. No derivation step equates the reducible uncertainty measure to a parameter fitted from the same data, nor does any central claim rest on a self-citation chain or uniqueness theorem imported from prior author work. The method explicitly disclaims causal estimation of true aleatoric/epistemic uncertainty and presents the alignment as an observed pattern rather than a mathematical necessity, keeping the analysis self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no explicit details on parameters, axioms or new entities. The approach implicitly relies on standard conformal prediction coverage guarantees and some notion of instance similarity for localisation, but these are not specified or justified here.

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discussion (0)

Reference graph

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