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Paper Citation Record · LEDGER

Optimized Piecewise Affine Abstractions of Neural Networks with Learnable Activation Functions

As of 23 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2602.06737.

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

pith.paper-citation-record.v1
2602.06737 v2

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T06:14:01.676857Z

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+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

12 of 12 outbound references displayed

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  • verified fuzzy0
  • unresolved11
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bbdea5d2-f3d9-4d4a-b718-7e88657b83c1 · outbound

This paper cites an unresolved cited work.

Optimized Piecewise Affine Abstractions of Neural Networks with Learnable Activation Functions Unresolved cited work

Reference 1

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no resolver link, observed 2026-08-04T06:14:01.489735Z

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Unavailable: canonical work link unavailable.

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Observation 7054ee36-ac2d-4d9c-b36c-35c6f1e90a73 · outbound

This paper cites an unresolved cited work.

Optimized Piecewise Affine Abstractions of Neural Networks with Learnable Activation Functions Unresolved cited work

Reference 2

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Observation d9355d58-80d7-4472-9c4f-4414a4fd1dc9 · outbound

This paper cites An approach to reachability analysis for feed-forward ReLU neural networks.

Optimized Piecewise Affine Abstractions of Neural Networks with Learnable Activation Functions An approach to reachability analysis for feed-forward ReLU neural networks

Reference 5

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Observation 3c062ee3-83c4-46e5-a0f7-33fcbd5f0130 · outbound

This paper cites an unresolved cited work.

Optimized Piecewise Affine Abstractions of Neural Networks with Learnable Activation Functions Unresolved cited work

Reference 11

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no resolver link, observed 2026-08-04T06:14:01.606245Z

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source=pdf_text observed=2026-08-04T06:14:01.606245Z digest=sha256:047078fe3e543f4f81aaff6294f0bbdba905ce6e1c35b612bcd87bc4db682a7b

Observation 7dc76359-8067-4ef6-9bd8-928900ec8d98 · outbound

This paper cites To computeM y for a given KAN, we recall the linearized function bψ(z) =    0z≤ −L aiz+b i z∈[z i, zi+1], i∈ {0,.

Optimized Piecewise Affine Abstractions of Neural Networks with Learnable Activation Functions To computeM y for a given KAN, we recall the linearized function bψ(z) =    0z≤ −L aiz+b i z∈[z i, zi+1], i∈ {0,

Reference 12

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source=pdf_text observed=2026-08-04T06:14:01.676857Z digest=sha256:512f83d11414e36d2026b6fb728b9b4c27f1157d0658e43910f1fcf09d346b76

Observation 6c6621ab-d197-4a8b-b9e0-1592d9397219 · outbound

This paper cites Bhattacharjee, S.

Optimized Piecewise Affine Abstractions of Neural Networks with Learnable Activation Functions Bhattacharjee, S

Reference 1969

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Unavailable: canonical work link unavailable.

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Observation 26ba0f37-5213-47b7-8155-09a55de663c5 · outbound

This paper cites Accessed 28 January 2026.

Optimized Piecewise Affine Abstractions of Neural Networks with Learnable Activation Functions Accessed 28 January 2026

Reference 2016

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no resolver link, observed 2026-08-04T06:14:01.353080Z

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source=pdf_text observed=2026-08-04T06:14:01.353080Z digest=sha256:2619318f1fba5e85dd4e4d2e3e7ed10a50dfb7e223cb6735e39ecc9c4470745c

Observation 1f6fbdcf-b9da-48b7-b410-7a628ef42ba3 · outbound

This paper cites Chebyshev Polynomial-Based Kolmogorov-Arnold Networks: An Efficient Architecture for Nonlinear Function Approximation.

Optimized Piecewise Affine Abstractions of Neural Networks with Learnable Activation Functions Chebyshev Polynomial-Based Kolmogorov-Arnold Networks: An Efficient Architecture for Nonlinear Function Approximation

Reference 2019

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Observation 7f3a42c2-26f7-40a3-8054-240e580086e8 · outbound

This paper cites Version 20.1.0.

Optimized Piecewise Affine Abstractions of Neural Networks with Learnable Activation Functions Version 20.1.0

Reference 2021

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Observation 212f49ab-8ab6-4409-8dd7-59537729dfc6 · outbound

This paper cites Howard, A.

Optimized Piecewise Affine Abstractions of Neural Networks with Learnable Activation Functions Howard, A

Reference 2025

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Observation 1275b0e1-77ba-4bc2-89f6-63e1d4d4c71b · outbound

This paper cites URL https://proceedings.

Optimized Piecewise Affine Abstractions of Neural Networks with Learnable Activation Functions URL https://proceedings

Reference 2158

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Observation 0423f276-e7b3-4013-a3b3-3228e10f44bb · outbound

This paper cites Gurobi Optimization, LLC.

Optimized Piecewise Affine Abstractions of Neural Networks with Learnable Activation Functions Gurobi Optimization, LLC

Reference 3396

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unresolved
no resolver link, observed 2026-08-04T06:14:00.795728Z

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Unavailable: canonical work link unavailable.

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Pith citing papers

No inbound Pith citation observations are available.