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arxiv: 2606.01723 · v1 · pith:D6XRPKVWnew · submitted 2026-06-01 · 💻 cs.LG · cs.AI

Shortcut to Nowhere: Demystifying Deep Spurious Regression

Guanrong Xu , Jessica Li , Hao Wang , Yuzhe Yang This is my paper

Pith reviewed 2026-06-28 15:56 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords deep spurious regressioncontinuous spurious correlationsregression shortcutsdistribution calibrationattribute similarityshortcut learninggeneralization
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The pith

Regression models fail on continuous spurious correlations unless label and feature distributions are calibrated using attribute similarities.

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

The paper defines deep spurious regression as the task of predicting continuous targets when training data contains attributes spuriously correlated with those targets, with the requirement to generalize to every possible attribute-target pairing at test time. Classification shortcuts do not transfer because regression lacks discrete labels and natural group boundaries. The proposed approach therefore measures similarity between spurious attributes in both the label space and the learned feature space to adjust for nearby targets and related groups. This produces calibrated distributions that improve robustness on image, sensor, and language-model regression benchmarks.

Core claim

We define Deep Spurious Regression (DSR) as learning from regression data with attribute-label confounding, addressing continuous spurious correlations, and generalizing to all attribute-label combinations at test time. Motivated by the intrinsic difference between classification and regression shortcuts, we propose to exploit the similarity among spurious attributes in both label and feature spaces, thereby accounting for nearby targets and related groups while calibrating both label and learned feature distributions across attributes.

What carries the argument

Exploitation of similarity among spurious attributes in label and feature spaces to calibrate both label and learned feature distributions across attributes.

If this is right

  • Models achieve superior performance on real-world DSR datasets spanning computer vision, environmental sensing, and LLM regression.
  • Continuous targets and all attribute-label combinations at test time are handled without relying on discrete group definitions.
  • Benchmarks and techniques now exist for studying spurious correlations specifically in continuous prediction tasks.

Where Pith is reading between the lines

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

  • The same calibration idea could apply to other smoothly varying continuous outputs such as time-series forecasting or dose-response modeling.
  • Fairness audits for regression might add explicit checks for distribution shift across continuous attribute values.
  • Synthetic experiments that vary the degree of attribute similarity would directly test how much the method depends on the similarity premise.

Load-bearing premise

Spurious attributes exhibit enough similarity in label and feature spaces for calibration to improve generalization.

What would settle it

A controlled dataset in which spurious attributes show low similarity in either label or feature space, where the calibration method produces no gain or a loss in test generalization.

read the original abstract

Real-world regression often exhibits shortcuts: attributes that are spuriously correlated with continuous targets in training, yet unreliable under deployment shifts; regressing targets using such shortcuts may fail catastrophically at test time. Existing studies on spurious correlations focus primarily on classification, where labels are categorical and groups are naturally defined. However, many real-world tasks require continuous prediction, where hard label boundaries or discrete group-label pairs do not exist. We define Deep Spurious Regression (DSR) as learning from regression data with attribute-label confounding, addressing continuous spurious correlations, and generalizing to all attribute-label combinations at test time. Motivated by the intrinsic difference between classification and regression shortcuts, we propose to exploit the similarity among spurious attributes in both label and feature spaces, thereby accounting for nearby targets and related groups while calibrating both label and learned feature distributions across attributes. Extensive experiments on common real-world DSR datasets that span computer vision, environmental sensing, and large language model (LLM) regression verify the superior performance of our strategies. Our work fills the gap in benchmarks and techniques for studying spurious correlations in continuous prediction.

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 / 0 minor

Summary. The paper defines Deep Spurious Regression (DSR) as regression under attribute-label confounding with continuous targets, requiring generalization to all attribute-label combinations at test time. Motivated by differences from classification shortcuts, it proposes calibration strategies that exploit similarity among spurious attributes in label and feature spaces to account for nearby targets and related groups while calibrating distributions. It claims that extensive experiments across computer vision, environmental sensing, and LLM regression datasets verify superior performance of the proposed strategies.

Significance. If the calibration approach holds, the work would address a genuine gap by extending spurious-correlation analysis to continuous regression, a setting common in deployed systems. The emphasis on intrinsic differences between classification and regression shortcuts, together with the call for new benchmarks, could usefully orient future research.

major comments (2)
  1. [Abstract] Abstract: the central generalization claim rests on the assumption that spurious attributes exhibit sufficient similarity in both label and feature spaces to justify cross-attribute calibration and accounting for nearby targets/related groups. No formal condition, bound, or derivation is supplied establishing that such similarities are guaranteed to exist, are sufficient for the required calibration, or survive the continuous nature of the targets.
  2. [Abstract] Abstract: the claim that 'extensive experiments ... verify the superior performance' is presented without any quantitative results, error bars, dataset statistics, ablation controls, or baseline comparisons, so the soundness of the generalization claim cannot be assessed from the provided text.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on the abstract. We address the two major comments point by point below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central generalization claim rests on the assumption that spurious attributes exhibit sufficient similarity in both label and feature spaces to justify cross-attribute calibration and accounting for nearby targets/related groups. No formal condition, bound, or derivation is supplied establishing that such similarities are guaranteed to exist, are sufficient for the required calibration, or survive the continuous nature of the targets.

    Authors: The calibration approach is motivated by the continuous nature of regression targets, where attributes with nearby label values tend to share related feature representations, enabling similarity-based cross-attribute calibration. We agree that no formal bound or derivation is provided. In revision we will add an explicit discussion of the assumptions (including when similarity may be insufficient) and the empirical conditions under which the method is intended to apply. revision: partial

  2. Referee: [Abstract] Abstract: the claim that 'extensive experiments ... verify the superior performance' is presented without any quantitative results, error bars, dataset statistics, ablation controls, or baseline comparisons, so the soundness of the generalization claim cannot be assessed from the provided text.

    Authors: Abstract length constraints typically preclude detailed quantitative reporting. The full manuscript contains the requested quantitative results, error bars, dataset statistics, ablations, and baseline comparisons. We will revise the abstract to include a concise statement of key performance gains and experimental scope. revision: yes

Circularity Check

0 steps flagged

No circularity: proposal motivated by stated differences without reduction to inputs

full rationale

The paper defines DSR and proposes calibration strategies motivated by intrinsic differences between classification and regression shortcuts. No equations, derivations, or self-citations are presented that reduce the calibration or generalization claims to fitted parameters defined by the same data, or to self-referential definitions. The similarity assumption is invoked as motivation rather than as a load-bearing derivation that collapses by construction. This is a standard non-circular empirical proposal.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

The central claim rests on the existence of exploitable similarities between spurious attributes and on the premise that distribution calibration across attributes will produce generalization; these are domain assumptions rather than derived quantities.

axioms (2)
  • domain assumption Spurious attributes exhibit measurable similarity in both label space and learned feature space that can be exploited for calibration.
    Invoked to motivate the proposed strategies for accounting for nearby targets and related groups.
  • domain assumption Calibrating label and feature distributions across attributes will reduce reliance on shortcuts and improve test-time generalization to all attribute-label combinations.
    Core premise of the method; no formal justification supplied in abstract.
invented entities (1)
  • Deep Spurious Regression (DSR) no independent evidence
    purpose: Named problem setting for regression with continuous spurious correlations.
    Introduced as a definition to fill the gap between classification-focused spurious correlation studies and continuous prediction tasks.

pith-pipeline@v0.9.1-grok · 5724 in / 1385 out tokens · 19934 ms · 2026-06-28T15:56:52.322989+00:00 · methodology

discussion (0)

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