Paper Citation Record · LEDGER

The Complexity of Verifying Feedforward Neural Networks in Quantised Settings

As of 18 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2605.29537.

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

pith.paper-citation-record.v1
2605.29537 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-29T00:09:07.981665Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

20 of 20 outbound references displayed

verified exact2
verified fuzzy0
unresolved17
parse uncertain1
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c91ddcbd-2895-45c6-9595-28fc0e3a6353 · outbound

This paper cites Johnson, and Changliu Liu.

The Complexity of Verifying Feedforward Neural Networks in Quantised Settings Johnson, and Changliu Liu

Reference 1

Resolution: unresolved; no resolver link, observed 2026-06-29T00:09:07.981665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 33d1aa0e-471d-42f0-906d-bc0f4b58978e · outbound

This paper cites The Fifth International Verification of Neural Networks Competition (VNN-COMP 2024): Summary and Results.

The Complexity of Verifying Feedforward Neural Networks in Quantised Settings The Fifth International Verification of Neural Networks Competition (VNN-COMP 2024): Summary and Results

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-06-29T00:12:49.986785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9cbd86eb-6f12-4031-886c-23d8c6c4b3eb · outbound

This paper cites MIT Press, Cambridge, MA, USA, October.

The Complexity of Verifying Feedforward Neural Networks in Quantised Settings MIT Press, Cambridge, MA, USA, October

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-29T00:09:07.981665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b4123e5-4bcb-4e10-a3d6-32bcaac8b471 · outbound

This paper cites Succinct representations of graphs.Inf.

The Complexity of Verifying Feedforward Neural Networks in Quantised Settings Succinct representations of graphs.Inf

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-29T00:09:07.981665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ef426c2-1881-4425-84a0-1cb010ea3651 · outbound

This paper cites A Survey of Quantization Methods for Efficient Neural Network Inference.

The Complexity of Verifying Feedforward Neural Networks in Quantised Settings A Survey of Quantization Methods for Efficient Neural Network Inference

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-06-29T00:12:49.989139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 046921bb-5899-4a28-a0ac-941eb09c747a · outbound

This paper cites Henzinger, Mathias Lechner, and Ðor¯de Žikeli ´c.

The Complexity of Verifying Feedforward Neural Networks in Quantised Settings Henzinger, Mathias Lechner, and Ðor¯de Žikeli ´c

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-29T00:09:07.981665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53cf61ca-e53d-42ff-ae88-c44480750cd7 · outbound

This paper cites A survey of safety and trustwor- thiness of deep neural networks: Verification, testing, ad- versarial attack and defence, and interpretability.Comput.

The Complexity of Verifying Feedforward Neural Networks in Quantised Settings A survey of safety and trustwor- thiness of deep neural networks: Verification, testing, ad- versarial attack and defence, and interpretability.Comput

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-29T00:09:07.981665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56ee41e7-4691-4584-8c8e-bfc4e4c45947 · outbound

This paper cites an unresolved cited work.

The Complexity of Verifying Feedforward Neural Networks in Quantised Settings Unresolved cited work

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-29T00:09:07.981665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec059a73-11d0-4f85-8c00-10fdb5253cb6 · outbound

This paper cites Barrett, David L.

The Complexity of Verifying Feedforward Neural Networks in Quantised Settings Barrett, David L

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-29T00:09:07.981665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e853ac1d-6514-400d-8820-2e40d72b8d04 · outbound

This paper cites Barrett, David L.

The Complexity of Verifying Feedforward Neural Networks in Quantised Settings Barrett, David L

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-29T00:09:07.981665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35b5d4e4-cf1d-4108-bb91-ea09ca53300e · outbound

This paper cites Combinatorial Optimization: Theory and Algorithms.

The Complexity of Verifying Feedforward Neural Networks in Quantised Settings Combinatorial Optimization: Theory and Algorithms

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-29T00:09:07.981665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe473226-3929-444d-8e33-7843273bbddd · outbound

This paper cites 2018 edi- tion,.

The Complexity of Verifying Feedforward Neural Networks in Quantised Settings 2018 edi- tion,

Reference 12

Resolution: parse uncertain; no resolver link, observed 2026-06-29T00:09:07.981665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c72a92f8-440b-4710-9945-07149612c1bf · outbound

This paper cites Complexity of fixed-size bit-vector logics.Theory Comput.

The Complexity of Verifying Feedforward Neural Networks in Quantised Settings Complexity of fixed-size bit-vector logics.Theory Comput

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-29T00:09:07.981665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3611d827-cf79-4842-8ec0-02c5a39ff9f0 · outbound

This paper cites Reachability analysis of deep neural net- works with provable guarantees.

The Complexity of Verifying Feedforward Neural Networks in Quantised Settings Reachability analysis of deep neural net- works with provable guarantees

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-29T00:09:07.981665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a03d28c-7389-4a08-8736-590d94dd90f3 · outbound

This paper cites Reachability is NP-complete even for the simplest neu- ral networks.

The Complexity of Verifying Feedforward Neural Networks in Quantised Settings Reachability is NP-complete even for the simplest neu- ral networks

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-29T00:09:07.981665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d36f777e-b358-48f1-be08-b22c33b1bdf1 · outbound

This paper cites [Sälzer and Lange, 2022] Marco Sälzer and Martin Lange.

The Complexity of Verifying Feedforward Neural Networks in Quantised Settings [Sälzer and Lange, 2022] Marco Sälzer and Martin Lange

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-29T00:09:07.981665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06fc58f1-2e7e-448a-9ad1-1792feaf8b45 · outbound

This paper cites Verifying and interpreting neu- ral networks using finite automata.

The Complexity of Verifying Feedforward Neural Networks in Quantised Settings Verifying and interpreting neu- ral networks using finite automata

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-29T00:09:07.981665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e993271d-1166-4956-8ce3-ed6195cb6d9c · outbound

This paper cites [Vaswaniet al., 2017 ] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N.

The Complexity of Verifying Feedforward Neural Networks in Quantised Settings [Vaswaniet al., 2017 ] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-29T00:09:07.981665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d976ea0-73f4-4d08-9ebb-e37b47107483 · outbound

This paper cites On the construction of automata from linear arithmetic constraints.

The Complexity of Verifying Feedforward Neural Networks in Quantised Settings On the construction of automata from linear arithmetic constraints

Reference 19

Resolution: unresolved; no resolver link, observed 2026-06-29T00:09:07.981665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 183f1adb-bb1a-455c-9e18-2d23cf26ff13 · outbound

This paper cites Complexity of reachability problems in neural networks.

The Complexity of Verifying Feedforward Neural Networks in Quantised Settings Complexity of reachability problems in neural networks

Reference 20

Resolution: unresolved; no resolver link, observed 2026-06-29T00:09:07.981665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.