Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-06-27T10:51:25.700776Z
Paper Citation Record · LEDGER
The Standard Interpretable Model: A general theory of interpretable machine learning to deductively design interpretable methods using Lagrangian mechanics
As of 14 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:2606.12289.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
17 of 17 outbound references displayed
- verified exact12
- verified fuzzy0
- unresolved5
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 105c0882-e53d-4bb6-9af5-be6275e05475 · outbound
This paper cites Foundations of Interpretable Models.
The Standard Interpretable Model: A general theory of interpretable machine learning to deductively design interpretable methods using Lagrangian mechanics Foundations of Interpretable Models
Reference 1
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T08:17:45.525078Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation a5f3e2df-048d-4e1c-b043-bf62a1cd5c4d · outbound
This paper cites Sparse Autoencoders Find Highly Interpretable Features in Language Models.
The Standard Interpretable Model: A general theory of interpretable machine learning to deductively design interpretable methods using Lagrangian mechanics Sparse Autoencoders Find Highly Interpretable Features in Language Models
Reference 2
- Resolution
- verified exact
- local_arxiv, observed 2026-07-03T08:17:45.553285Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 825fa86b-17ac-447c-b555-8fd5fa284717 · outbound
This paper cites an unresolved cited work.
The Standard Interpretable Model: A general theory of interpretable machine learning to deductively design interpretable methods using Lagrangian mechanics Unresolved cited work
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-06-27T10:51:25.700776Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 87813505-2b3f-49a7-801e-499e935bc40f · outbound
This paper cites Towards Automatic Concept-based Explanations.
The Standard Interpretable Model: A general theory of interpretable machine learning to deductively design interpretable methods using Lagrangian mechanics Towards Automatic Concept-based Explanations
Reference 4
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T08:17:45.540155Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation b2ead70d-57c3-4bcb-9e9d-0299c4aacde3 · outbound
This paper cites an unresolved cited work.
The Standard Interpretable Model: A general theory of interpretable machine learning to deductively design interpretable methods using Lagrangian mechanics Unresolved cited work
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T08:17:45.550703Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 5eec9345-f925-4c17-99b5-98adcacc8f67 · outbound
This paper cites The (Un)reliability of saliency methods.
The Standard Interpretable Model: A general theory of interpretable machine learning to deductively design interpretable methods using Lagrangian mechanics The (Un)reliability of saliency methods
Reference 6
- Resolution
- verified exact
- local_arxiv, observed 2026-07-03T08:17:45.545289Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation e7419dee-40bd-4c26-8bc7-54f27a27b23b · outbound
This paper cites an unresolved cited work.
The Standard Interpretable Model: A general theory of interpretable machine learning to deductively design interpretable methods using Lagrangian mechanics Unresolved cited work
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-06-27T10:51:25.700776Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 05384e42-6602-4af5-b29a-34c99013a85b · outbound
This paper cites Lagrange.
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-06-27T10:51:25.700776Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b33c65cd-b35d-469e-b86d-cf8d91e02db1 · outbound
This paper cites Don't Lose Focus: Activation Steering via Key-Orthogonal Projections.
The Standard Interpretable Model: A general theory of interpretable machine learning to deductively design interpretable methods using Lagrangian mechanics Don't Lose Focus: Activation Steering via Key-Orthogonal Projections
Reference 9
- Resolution
- verified exact
- local_arxiv, observed 2026-07-03T08:17:45.534436Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation ce2c5210-6fbe-451b-96ee-ab7c6ef0befa · outbound
This paper cites Imposing Hard Constraints on Deep Networks: Promises and Limitations.
The Standard Interpretable Model: A general theory of interpretable machine learning to deductively design interpretable methods using Lagrangian mechanics Imposing Hard Constraints on Deep Networks: Promises and Limitations
Reference 10
- Resolution
- verified exact
- local_arxiv, observed 2026-07-03T08:17:45.530103Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 3f560512-9d91-4e31-b363-70c90c0015f5 · outbound
This paper cites Concept-based explainable artificial intelligence: A survey.
The Standard Interpretable Model: A general theory of interpretable machine learning to deductively design interpretable methods using Lagrangian mechanics Concept-based explainable artificial intelligence: A survey
Reference 11
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T08:17:45.547968Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation b6017761-20bc-40ec-9bd0-7f550b137ced · outbound
This paper cites Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations.
The Standard Interpretable Model: A general theory of interpretable machine learning to deductively design interpretable methods using Lagrangian mechanics Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations
Reference 12
- Resolution
- verified exact
- local_arxiv, observed 2026-07-03T08:17:45.535113Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 0d7465e9-54aa-4cd3-8b04-a621d738109b · outbound
This paper cites Schubert and P.
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-06-27T10:51:25.700776Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4856d0be-1f94-4fe2-8fe3-9e06e9bcfcd9 · outbound
This paper cites A Closer Look at the Intervention Procedure of Concept Bottleneck Models.
The Standard Interpretable Model: A general theory of interpretable machine learning to deductively design interpretable methods using Lagrangian mechanics A Closer Look at the Intervention Procedure of Concept Bottleneck Models
Reference 14
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T08:17:45.537542Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 8a7345d0-96c6-4801-9abc-3a9df9ecd1b7 · outbound
This paper cites Towards Compositional Interpretability for XAI.
The Standard Interpretable Model: A general theory of interpretable machine learning to deductively design interpretable methods using Lagrangian mechanics Towards Compositional Interpretability for XAI
Reference 15
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T08:17:45.542872Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 5ca46e19-1d61-4c05-b793-b79a9278504a · outbound
This paper cites Stochastic Concept Bottleneck Models.
The Standard Interpretable Model: A general theory of interpretable machine learning to deductively design interpretable methods using Lagrangian mechanics Stochastic Concept Bottleneck Models
Reference 16
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T08:17:45.527526Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation e59a175c-af38-44bb-a2be-6342a7a022a6 · outbound
This paper cites an unresolved cited work.
The Standard Interpretable Model: A general theory of interpretable machine learning to deductively design interpretable methods using Lagrangian mechanics Unresolved cited work
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-06-27T10:51:25.700776Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.