Paper Citation Record · LEDGER

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons

As of 9 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 1 inbound Pith citation observation for arXiv:2502.03391.

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pith.paper-citation-record.v1
2502.03391 v3

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

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measured 54 of 54 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T16:52:02.948457Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

53 of 53 outbound references displayed

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External citation measurements

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arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

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This paper cites Appendix E contains technical specifications, related to the models, and training.

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons Appendix E contains technical specifications, related to the models, and training

Reference 1

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Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons Unresolved cited work

Reference 2

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This paper cites We will now begin by introducing the reduction for binary-input-output MLPs from the Shortest- Implicant-Core problem (Barceló et al.

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons We will now begin by introducing the reduction for binary-input-output MLPs from the Shortest- Implicant-Core problem (Barceló et al

Reference 3

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This paper cites missingness.

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons missingness

Reference 4

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This paper cites A large annotated corpus for learning natural language inference.

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons A large annotated corpus for learning natural language inference

Reference 5

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This paper cites We first note a known inapproximability result for the Shortest-Implicant-Core prob- lem (Umans (1999)) which will be used to prove the inapproximability result for our case: Lemma.

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons We first note a known inapproximability result for the Shortest-Implicant-Core prob- lem (Umans (1999)) which will be used to prove the inapproximability result for our case: Lemma

Reference 6

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This paper cites We acknowledge, however, that despite the difficulty of theMSR query being established through a reduction from the Shortest Implicant Core problem (as proven in Barceló et al.

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons We acknowledge, however, that despite the difficulty of theMSR query being established through a reduction from the Shortest Implicant Core problem (as proven in Barceló et al

Reference 7

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This paper cites On the Reasons Behind Decisions.

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons On the Reasons Behind Decisions

Reference 8

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Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons Unresolved cited work

Reference 9

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This paper cites ERASER: A Benchmark to Evaluate Rationalized NLP Models.

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons ERASER: A Benchmark to Evaluate Rationalized NLP Models

Reference 10

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This paper cites Concept Embedding Models: Beyond the Accuracy-Explainability Trade-off.

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons Concept Embedding Models: Beyond the Accuracy-Explainability Trade-off

Reference 11

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This paper cites On Efficiently Explaining Graph-Based Classifiers.

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons On Efficiently Explaining Graph-Based Classifiers

Reference 12

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This paper cites On Explaining Decision Trees.

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons On Explaining Decision Trees

Reference 13

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This paper cites Distance-Restricted Explanations: Theoretical Underpinnings & Efficient Implementation.

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons Distance-Restricted Explanations: Theoretical Underpinnings & Efficient Implementation

Reference 14

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This paper cites A Comprehensive Survey on Self-Interpretable Neural Networks.

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons A Comprehensive Survey on Self-Interpretable Neural Networks

Reference 16

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This paper cites Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks.

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks

Reference 17

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This paper cites Logic-based Explainability in Machine Learning.

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons Logic-based Explainability in Machine Learning

Reference 21

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This paper cites On the Computational Tractability of the (Many) Shapley Values.

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons On the Computational Tractability of the (Many) Shapley Values

Reference 22

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This paper cites SELFEXPLAIN: A Self-Explaining Architecture for Neural Text Classifiers.

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons SELFEXPLAIN: A Self-Explaining Architecture for Neural Text Classifiers

Reference 23

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This paper cites Reachability is NP-Complete Even for the Simplest Neural Networks.

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons Reachability is NP-Complete Even for the Simplest Neural Networks

Reference 24

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This paper cites A Symbolic Approach to Explaining Bayesian Network Classifiers.

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons A Symbolic Approach to Explaining Bayesian Network Classifiers

Reference 25

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This paper cites Unsupervised Commonsense Question Answering with Self-Talk.

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons Unsupervised Commonsense Question Answering with Self-Talk

Reference 26

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This paper cites Probabilistic Explanations for Linear Models.

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons Probabilistic Explanations for Linear Models

Reference 27

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This paper cites The many Shapley Values for Model Explanation.

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons The many Shapley Values for Model Explanation

Reference 28

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This paper cites Comprehensive Attribution: Inherently Ex- plainable Vision Model with Feature Detector.

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons Comprehensive Attribution: Inherently Ex- plainable Vision Model with Feature Detector

Reference 29

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This paper cites Appendix B contains background for the computational complexity proofs.

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons Appendix B contains background for the computational complexity proofs

Reference 30

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This paper cites concepts.

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons concepts

Reference 33

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Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons prototypes

Reference 34

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Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons Unresolved cited work

Reference 35

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Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons Unresolved cited work

Reference 36

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This paper cites An implicant C for ϕ is a partial assignment of the variables of ϕ such that any assignment to the remaining variables evaluates to true.

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons An implicant C for ϕ is a partial assignment of the variables of ϕ such that any assignment to the remaining variables evaluates to true

Reference 38

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This paper cites Conversely, if o2,2 = 0, then o2,1 = o2,2, and thus f ′ is classified under o2,1 by virtue of o2,1 ≻ o2,2.

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons Conversely, if o2,2 = 0, then o2,1 = o2,2, and thus f ′ is classified under o2,1 by virtue of o2,1 ≻ o2,2

Reference 40

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Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons Unresolved cited work

Reference 41

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Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons Unresolved cited work

Reference 42

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This paper cites (2021)) established that finding a cardinally minimal probabilistic sufficient reason for a CNF classifier, given a discrete uniform distribution over{0, 1}n, is NPPP-Hard.

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons (2021)) established that finding a cardinally minimal probabilistic sufficient reason for a CNF classifier, given a discrete uniform distribution over{0, 1}n, is NPPP-Hard

Reference 43

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Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons Unresolved cited work

Reference 46

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Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons This reduction is approximation preserving, as both k′ := k and n′ := n, indicating that the same approximation ratio is preserved

Reference 47

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Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons Unresolved cited work

Reference 48

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This paper cites From the previous claim, it follows directly that a sufficient reason S is of minimal cardinality if and only if the cardinality of E \ C is minimal (when C is a clique in G).

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons From the previous claim, it follows directly that a sufficient reason S is of minimal cardinality if and only if the cardinality of E \ C is minimal (when C is a clique in G)

Reference 49

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This paper cites I A DDITIONAL ABLATION EXPERIMENTS In this section, we provide additional ablation results for our experiments.

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons I A DDITIONAL ABLATION EXPERIMENTS In this section, we provide additional ablation results for our experiments

Reference 51

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This paper cites However, excessively high or lowτ values tend to be more sensitive and may negatively impact certain key metrics.

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons However, excessively high or lowτ values tend to be more sensitive and may negatively impact certain key metrics

Reference 52

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This paper cites On Guaranteed Optimal Robust Explanations for NLP Models.

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons On Guaranteed Optimal Robust Explanations for NLP Models

Reference 2009

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This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 2011

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This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 2012

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This paper cites Furthermore, the robust class of sufficient explanations studied in this work also connects to research in formal logic (Marques-Silva, 2023; Darwiche & Marquis, 2021).

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons Furthermore, the robust class of sufficient explanations studied in this work also connects to research in formal logic (Marques-Silva, 2023; Darwiche & Marquis, 2021)

Reference 2016

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Observation ac1cc37b-acb7-4964-adf3-16597a16c133 · outbound

This paper cites You Only Explain Once.

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons You Only Explain Once

Reference 2017

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Observation 09108dc4-4422-4780-bcbf-63c989f00a68 · outbound

This paper cites Hard to Explain: On the Computational Hardness of In-Distribution Model Interpretation.

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons Hard to Explain: On the Computational Hardness of In-Distribution Model Interpretation

Reference 2018

Resolution: verified exact; local_arxiv, observed 2026-08-09T04:59:51.850035Z

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Observation 3611229a-ed6a-4c48-8387-61fc8f7f3bb5 · outbound

This paper cites Explaining Image Classifiers.

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons Explaining Image Classifiers

Reference 2019

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Observation 42fb757b-b2d8-4b93-a2f0-6c41fbb8fe89 · outbound

This paper cites Explaining k-Nearest Neighbors: Abductive and Counterfactual Explanations.

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons Explaining k-Nearest Neighbors: Abductive and Counterfactual Explanations

Reference 2020

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Observation 23aa1ac7-8ee9-48f7-bb22-ce34d19852d2 · outbound

This paper cites Causal Explanations for Image Classifiers.

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons Causal Explanations for Image Classifiers

Reference 2021

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This paper cites The Computational Complexity of Circuit Discovery for Inner Interpretability.

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons The Computational Complexity of Circuit Discovery for Inner Interpretability

Reference 2022

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This paper cites Local vs.

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons Local vs

Reference 2023

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Observation 1f72ee64-62d3-4d31-905d-992cd34ca8d6 · outbound

This paper cites Learning to Faithfully Rationalize by Construction.

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons Learning to Faithfully Rationalize by Construction

Reference 2024

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Pith citing papers

Observation 8fe8ffd0-81c3-4d12-b643-19fe6d4c1261 · inbound

PRISM: Recovering Instruction Sets from Language Model Activations cites this paper.

PRISM: Recovering Instruction Sets from Language Model Activations Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons

Reference 4

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