Where Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making

Hua Zhang; Kangwei Liu; Qunli Zhang; Ruoyu Chen; Sanyi Zhang; Shangquan Sun; Shiming Liu; Xiaochun Cao; Xiaoqing Guo; Zhangcheng Wang

arxiv: 2602.07008 · v2 · pith:ONJH7JNDnew · submitted 2026-01-30 · 💻 cs.CV · cs.LG

Where Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making

Ruoyu Chen , Shangquan Sun , Xiaoqing Guo , Sanyi Zhang , Kangwei Liu , Shiming Liu , Zhangcheng Wang , Qunli Zhang

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Hua Zhang Xiaochun Cao

This is my paper

Pith reviewed 2026-05-21 13:47 UTC · model grok-4.3

classification 💻 cs.CV cs.LG

keywords human prior alignmentattribution constraintsreliable decision-makingimage classificationGUI agentsshortcut learningattribution methods

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The pith

Training models to align their decision evidence with human prior regions improves both accuracy and decision reasonability.

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

The paper introduces a training approach that encodes human priors as specific input regions, such as bounding boxes, which the model should use as evidence for its decisions. It employs a subset-selection-based attribution technique during training to identify where the model is actually focusing and applies penalties when that focus falls outside the prior regions. This steers the model away from shortcut correlations toward the intended evidence. Experiments on image classification and click-based tasks in GUI agent models show gains in both prediction accuracy and the reasonableness of the justifications provided.

Core claim

By imposing attribution constraints derived from human priors and penalizing reliance on off-prior evidence through a faithful subset-selection attribution method, models achieve higher task accuracy and produce more acceptable decision justifications in both standard classification and autoregressive generation settings.

What carries the argument

Subset-selection-based attribution, used to expose the model's actual decision evidence at training time so that off-prior regions can be penalized and attributions shifted toward the human-specified prior regions.

If this is right

Models become less reliant on unintended shortcut features in classification tasks.
Decision reasonability improves in autoregressive generation settings such as GUI agent click decisions.
The same prior-alignment objective works across both conventional supervised classification and multimodal large language model agents.
Human priors encoded as regions can be directly used to regularize what evidence the model attends to during learning.

Where Pith is reading between the lines

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

The approach may generalize to other domains where human-specified regions of interest can be provided, such as medical image analysis.
If attribution faithfulness holds, similar constraints could be applied to reduce spurious correlations in large-scale pretraining.
Combining this with other forms of human feedback might further improve model trustworthiness without requiring full retraining.

Load-bearing premise

The subset-selection-based attribution method faithfully reveals the model's true decision evidence, so that penalizing off-prior regions actually steers the model to the intended areas.

What would settle it

A direct measurement showing that models trained with the attribution penalty exhibit no greater overlap between their attribution maps and the provided human prior regions than baseline models, or show no accuracy improvement on the target tasks.

read the original abstract

Reliable models should not only predict correctly, but also justify decisions with acceptable evidence. Yet conventional supervised learning typically provides only class-level labels, allowing models to achieve high accuracy through shortcut correlations rather than the intended evidence. Human priors can help constrain such behavior, but aligning models to these priors remains challenging because learned representations often diverge from human perception. To address this challenge, we propose an attribution-based human prior alignment method. We encode human priors as input regions that the model is expected to rely on (e.g., bounding boxes), and leverage a highly faithful subset-selection-based attribution approach to expose the model's decision evidence during training. When the attribution region deviates substantially from the prior regions, we penalize reliance on off-prior evidence, encouraging the model to shift its attribution toward the intended regions. This is achieved through a training objective that imposes attribution constraints induced by the human prior. We validate our method on both image classification and click decision tasks in MLLM-based GUI agent models. Across conventional classification and autoregressive generation settings, human prior alignment consistently improves task accuracy while also enhancing the model's decision reasonability.

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

The paper adds a training penalty that steers subset-based attributions toward human prior regions like bounding boxes, which targets shortcut learning but assumes the attribution is faithful without shown validation.

read the letter

The main thing here is a training objective that penalizes models when their decision evidence, measured by subset-selection attribution, falls outside human-specified prior regions. This is meant to push the model toward intended evidence in both standard image classification and click decisions for MLLM GUI agents. The approach is new in tying subset-based attribution directly into the loss as an explicit constraint rather than using it only for post-training analysis. It does a solid job laying out how class labels alone let models rely on shortcuts and then giving a concrete way to encode priors as regions and enforce alignment during optimization. That dual goal of better accuracy plus improved reasonability is a practical angle for reliable systems. The soft spot is the reliance on the subset-selection attribution being highly faithful. The description treats it as given and highly accurate for exposing true decision evidence, yet offers no comparisons to other attribution methods, no ablations on subset quality, and no checks that the selected subsets match the model's internal computation. If the attribution is only approximate, the penalties risk being noisy or misdirected, and any reported gains could stem from ordinary regularization instead of prior alignment. The abstract also claims consistent improvements across settings without numbers, error bars, or details on how reasonability was scored, which leaves the central results hard to evaluate from the summary alone. This paper is for researchers focused on human-AI alignment and constraint-based training in computer vision and multimodal agents. Someone already working on attribution methods or prior injection would find the objective formulation worth looking at, even if they would want to test the faithfulness assumption themselves. I would send it to peer review. The idea is clear enough and grounded in existing attribution work to merit referee input, though the experiments will need to address the validation gaps to hold up.

Referee Report

2 major / 2 minor

Summary. The paper proposes an attribution-based human prior alignment method for reliable decision-making. Human priors are encoded as input regions (e.g., bounding boxes); a subset-selection-based attribution technique exposes the model's decision evidence during training, and a penalty is applied when attributions deviate from the prior regions. The training objective imposes these attribution constraints. The method is validated on image classification and click decision tasks for MLLM-based GUI agents, with the claim that prior alignment improves both task accuracy and decision reasonability across classification and autoregressive generation settings.

Significance. If the central claims hold, the work offers a concrete mechanism to constrain models away from shortcut correlations toward human-intended evidence regions, which could improve reliability in vision and multimodal agent settings. The dual benefit of accuracy gains plus enhanced reasonability, if robustly shown, would be a useful contribution for applications requiring justifiable decisions.

major comments (2)

The central claim requires that the subset-selection attribution accurately exposes the model's true decision evidence so the penalty can reliably shift attributions onto human prior regions. The manuscript treats this attribution as 'highly faithful' and uses it to induce constraints, yet provides no derivation, comparison to established attribution techniques (e.g., integrated gradients or occlusion), or ablation showing that selected subsets match the model's actual internal computation. If faithfulness is only partial, the training signal is noisy and accuracy gains could arise from generic regularization rather than prior alignment. This assumption is load-bearing for both the method and the reported improvements.
The abstract and claims state that human prior alignment 'consistently improves task accuracy while also enhancing the model's decision reasonability' across settings. However, the provided text contains no quantitative results, error bars, statistical significance tests, or details on how attribution faithfulness or reasonability was measured and verified in the experiments. Without these, the evidence supporting the strongest claim remains indirect.

minor comments (2)

Clarify the precise mathematical form of the attribution constraint and penalty term, including any weighting hyperparameters or thresholds for 'substantial deviation' from prior regions.
Specify the exact datasets, number of human prior annotations (bounding boxes), and evaluation metrics for reasonability in the GUI agent click tasks.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. The comments highlight important aspects of our method's justification and experimental presentation. We address each major comment point-by-point below and will incorporate revisions to strengthen the manuscript.

read point-by-point responses

Referee: The central claim requires that the subset-selection attribution accurately exposes the model's true decision evidence so the penalty can reliably shift attributions onto human prior regions. The manuscript treats this attribution as 'highly faithful' and uses it to induce constraints, yet provides no derivation, comparison to established attribution techniques (e.g., integrated gradients or occlusion), or ablation showing that selected subsets match the model's actual internal computation. If faithfulness is only partial, the training signal is noisy and accuracy gains could arise from generic regularization rather than prior alignment. This assumption is load-bearing for both the method and the reported improvements.

Authors: We agree that the faithfulness of the subset-selection attribution is central and load-bearing. The original manuscript describes it as highly faithful due to its explicit subset-selection mechanism that directly identifies decision evidence, but we acknowledge the absence of explicit derivations and comparisons. In the revised manuscript, we will add a new subsection providing a formal derivation of its faithfulness properties, direct comparisons against integrated gradients and occlusion methods on the same tasks, and ablation studies that correlate selected subsets with internal model activations and decision boundaries. These additions will clarify that the training signal targets prior alignment rather than acting as generic regularization. revision: yes
Referee: The abstract and claims state that human prior alignment 'consistently improves task accuracy while also enhancing the model's decision reasonability' across settings. However, the provided text contains no quantitative results, error bars, statistical significance tests, or details on how attribution faithfulness or reasonability was measured and verified in the experiments. Without these, the evidence supporting the strongest claim remains indirect.

Authors: We note that the full manuscript contains experimental validation sections reporting accuracy gains and reasonability improvements on both image classification and MLLM-based GUI agent click tasks. However, we agree that the presentation would benefit from greater quantitative rigor. In the revision, we will expand the results section to include all numerical values with error bars, statistical significance tests (including p-values from appropriate tests), and explicit details on the measurement protocols for attribution faithfulness (e.g., overlap metrics with ground-truth regions) and decision reasonability (e.g., human evaluation or proxy metrics). This will make the evidence for the dual benefits direct and verifiable. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation relies on external priors and treated-as-given attribution

full rationale

The paper proposes encoding human priors as input regions (e.g., bounding boxes) and applying a penalty via a subset-selection-based attribution method when the model's decision evidence deviates from those regions. The training objective is explicitly defined in terms of these external priors and the attribution procedure, which is presented as an existing, highly faithful tool rather than derived from the model's own outputs or fitted parameters. No self-definitional loops, fitted inputs renamed as predictions, load-bearing self-citations, or ansatzes smuggled via prior work appear in the abstract or described method. The central claims of improved accuracy and decision reasonability are framed as empirical validation outcomes on classification and GUI agent tasks, not as results forced by construction from the inputs. The faithfulness of the attribution method is an external assumption (a potential correctness risk) but does not reduce the derivation chain to tautology.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The approach rests on the unverified assumption that the chosen attribution method faithfully reflects model decisions and that human-provided regions are the correct target evidence. No free parameters or invented entities are explicitly listed in the abstract.

axioms (1)

domain assumption Subset-selection-based attribution is highly faithful for exposing decision evidence
Invoked when the method uses attribution deviation to apply penalties during training.

pith-pipeline@v0.9.0 · 5760 in / 978 out tokens · 28955 ms · 2026-05-21T13:47:43.723368+00:00 · methodology

discussion (0)

Forward citations

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