Paper Citation Record · LEDGER

Interpretability-Guided Bi-objective Optimization: Aligning Accuracy and Explainability

As of 9 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2601.00655.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2601.00655 v3

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-16T17:33:33.049103Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

26 of 26 outbound references displayed

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verified fuzzy24
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0661e6d6-f836-43da-b618-cf99f1c31444 · outbound

This paper cites Axiomatic attribution for deep networks.

Interpretability-Guided Bi-objective Optimization: Aligning Accuracy and Explainability Axiomatic attribution for deep networks

Reference 1

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Source-reported events for the cited work

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Observation 656fe024-c5e0-428e-b8f2-ad4274d45ebb · outbound

This paper cites ” why should i trust you?.

Interpretability-Guided Bi-objective Optimization: Aligning Accuracy and Explainability ” why should i trust you?

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T17:41:09.313708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9c817174-5c5c-409d-ba16-0ae3393d3c7a · outbound

This paper cites A comprehensive survey on explainable ai: Techniques, applications, and future directions.

Interpretability-Guided Bi-objective Optimization: Aligning Accuracy and Explainability A comprehensive survey on explainable ai: Techniques, applications, and future directions

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T17:41:09.306067Z

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Observation b9d3c054-d968-42d1-a278-f74c36ab3416 · outbound

This paper cites A unified approach to interpreting model predictions.

Interpretability-Guided Bi-objective Optimization: Aligning Accuracy and Explainability A unified approach to interpreting model predictions

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T17:41:09.284747Z

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Observation 877d665f-95e4-4fba-8ec3-2905b96bf518 · outbound

This paper cites Right for the right reasons: Training differentiable models by constraining their explanations.

Interpretability-Guided Bi-objective Optimization: Aligning Accuracy and Explainability Right for the right reasons: Training differentiable models by constraining their explanations

Reference 5

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Observation 33b710e4-a50b-4a33-94d2-fe6e161b31dd · outbound

This paper cites Concept bottleneck models.

Interpretability-Guided Bi-objective Optimization: Aligning Accuracy and Explainability Concept bottleneck models

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T17:41:09.308443Z

Source-reported events for the cited work

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Observation bbaf9948-ca27-4f97-b448-f72315365391 · outbound

This paper cites Lyra: A learning approach to context-aware driver alerts.

Interpretability-Guided Bi-objective Optimization: Aligning Accuracy and Explainability Lyra: A learning approach to context-aware driver alerts

Reference 7

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Source-reported events for the cited work

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Observation b279176e-663e-4f13-b87c-17d7e43118cf · outbound

This paper cites Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.

Interpretability-Guided Bi-objective Optimization: Aligning Accuracy and Explainability Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

Reference 8

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Observation 55e742ec-ba73-43fa-9631-c5b590d100ba · outbound

This paper cites Multiple-gradient descent algorithm (mgda) for multiob- jective optimization.

Interpretability-Guided Bi-objective Optimization: Aligning Accuracy and Explainability Multiple-gradient descent algorithm (mgda) for multiob- jective optimization

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T17:41:09.288524Z

Source-reported events for the cited work

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Observation 4a34c0a6-7e2a-48fa-ac19-77d7bd034679 · outbound

This paper cites Steepest descent methods for multicriteria optimization.

Interpretability-Guided Bi-objective Optimization: Aligning Accuracy and Explainability Steepest descent methods for multicriteria optimization

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T17:41:09.296572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 23088504-869e-4f4c-87a0-e649bd4f689b · outbound

This paper cites Multi-task learning as multi-objective opti- mization.

Interpretability-Guided Bi-objective Optimization: Aligning Accuracy and Explainability Multi-task learning as multi-objective opti- mization

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T17:41:09.300821Z

Source-reported events for the cited work

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Observation 5f439652-9725-4b63-8b68-de2586525a77 · outbound

This paper cites Pareto multi-task learning.

Interpretability-Guided Bi-objective Optimization: Aligning Accuracy and Explainability Pareto multi-task learning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T17:41:09.298663Z

Source-reported events for the cited work

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Observation aa6089d0-2ccc-4ae5-b654-fd67577d4fce · outbound

This paper cites Multi-objective optimization by learning space partitions.

Interpretability-Guided Bi-objective Optimization: Aligning Accuracy and Explainability Multi-objective optimization by learning space partitions

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T17:38:11.878669Z

Source-reported events for the cited work

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Observation 3fbb642c-d563-43b3-884e-fe60c5510134 · outbound

This paper cites Conflict-averse gradient descent for multi-task learning.

Interpretability-Guided Bi-objective Optimization: Aligning Accuracy and Explainability Conflict-averse gradient descent for multi-task learning

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T17:38:11.873482Z

Source-reported events for the cited work

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Observation e2ea2070-f2cd-4f03-8aca-7b407ea273e4 · outbound

This paper cites Visualizing the impact of feature attribution baselines.

Interpretability-Guided Bi-objective Optimization: Aligning Accuracy and Explainability Visualizing the impact of feature attribution baselines

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T17:38:11.868473Z

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Observation adbba4f9-8704-4776-b97f-e38e4db077ea · outbound

This paper cites The middle child problem: Revisiting parametric linear models for attribution priors.

Interpretability-Guided Bi-objective Optimization: Aligning Accuracy and Explainability The middle child problem: Revisiting parametric linear models for attribution priors

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T17:38:11.870955Z

Source-reported events for the cited work

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Observation f8e802bc-ed58-4b84-acc1-ca3a1f90915c · outbound

This paper cites Improving performance of deep learning models with axiomatic attribution priors and expected gradients.

Interpretability-Guided Bi-objective Optimization: Aligning Accuracy and Explainability Improving performance of deep learning models with axiomatic attribution priors and expected gradients

Reference 17

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Source-reported events for the cited work

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Observation 2f872eb4-ef89-4e00-8286-9f753726ca00 · outbound

This paper cites Explainable ai: A brief survey on history, research areas, approaches and challenges.

Interpretability-Guided Bi-objective Optimization: Aligning Accuracy and Explainability Explainable ai: A brief survey on history, research areas, approaches and challenges

Reference 18

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Observation da2bb767-1ff3-425d-aa79-93a4de195653 · outbound

This paper cites Time Interpret: a Unified Model Interpretability Library for Time Series.

Interpretability-Guided Bi-objective Optimization: Aligning Accuracy and Explainability Time Interpret: a Unified Model Interpretability Library for Time Series

Reference 19

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Source-reported events for the cited work

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Observation 6f3e7587-a6d6-40bd-a158-26c788f51e04 · outbound

This paper cites Explaining with shortest paths on manifolds.

Interpretability-Guided Bi-objective Optimization: Aligning Accuracy and Explainability Explaining with shortest paths on manifolds

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T17:41:09.287502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c5d8ddd5-6a88-4360-9bb8-2826bada4a10 · outbound

This paper cites Unraveling the geometry of loss landscapes in deep learning.

Interpretability-Guided Bi-objective Optimization: Aligning Accuracy and Explainability Unraveling the geometry of loss landscapes in deep learning

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T17:41:09.285636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 712ad414-38f2-4b68-84fe-1ef620a3ba84 · outbound

This paper cites Gradient surgery for multi-task learning.

Interpretability-Guided Bi-objective Optimization: Aligning Accuracy and Explainability Gradient surgery for multi-task learning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T17:41:09.294387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5c0db364-2b8c-4729-b0b6-1bb1b725c680 · outbound

This paper cites Identifying and attacking the saddle point problem in high- dimensional non-convex optimization.

Interpretability-Guided Bi-objective Optimization: Aligning Accuracy and Explainability Identifying and attacking the saddle point problem in high- dimensional non-convex optimization

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T17:41:09.303273Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 923c6fd7-67da-4ade-9266-0f4d82bf998a · outbound

This paper cites Continuous pareto-optimality for multi-task learning.

Interpretability-Guided Bi-objective Optimization: Aligning Accuracy and Explainability Continuous pareto-optimality for multi-task learning

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T17:41:09.293925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation df2e2bf7-7aa8-44dc-b32d-0ee285848c13 · outbound

This paper cites Spirtes, C.

Interpretability-Guided Bi-objective Optimization: Aligning Accuracy and Explainability Spirtes, C

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T17:38:11.863072Z

Source-reported events for the cited work

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Observation b0a69eaf-31d4-424d-96cf-811706acf078 · outbound

This paper cites Estimating high-dimensional directed acyclic graphs with the pc-algorithm.

Interpretability-Guided Bi-objective Optimization: Aligning Accuracy and Explainability Estimating high-dimensional directed acyclic graphs with the pc-algorithm

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T17:38:11.865925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

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