Paper Citation Record · LEDGER

LMUFormer: Low Complexity Yet Powerful Spiking Model With Legendre Memory Units

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2402.04882.

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

pith.paper-citation-record.v1
2402.04882 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 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 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:39:30.095377Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T13:48:21.572475Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation cb4e874c-b74c-4275-8164-b683ea2f459c · inbound

Learning-based Delay Compensation for Enhanced Control of Assistive Soft Robots cites this paper.

Learning-based Delay Compensation for Enhanced Control of Assistive Soft Robots LMUFormer: Low Complexity Yet Powerful Spiking Model With Legendre Memory Units

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-16T12:39:30.095377Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fae52069-af0f-48c8-8129-66ef3120affe · inbound

ASRC-SNN: Adaptive Skip Recurrent Connection Spiking Neural Network cites this paper.

ASRC-SNN: Adaptive Skip Recurrent Connection Spiking Neural Network LMUFormer: Low Complexity Yet Powerful Spiking Model With Legendre Memory Units

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-15T20:59:34.833831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dad707b4-4377-4715-8217-f5565b734954 · inbound

Towards Green Wearable Computing: A Physics-Aware Spiking Neural Network for Energy-Efficient IMU-based Human Activity Recognition cites this paper.

Towards Green Wearable Computing: A Physics-Aware Spiking Neural Network for Energy-Efficient IMU-based Human Activity Recognition LMUFormer: Low Complexity Yet Powerful Spiking Model With Legendre Memory Units

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:00:58.487933Z

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 a86cd39a-c20c-4d0d-9762-9c73379950df · inbound

LongSpike: Fractional Order Spiking State Space Models for Efficient Long Sequence Learning cites this paper.

LongSpike: Fractional Order Spiking State Space Models for Efficient Long Sequence Learning LMUFormer: Low Complexity Yet Powerful Spiking Model With Legendre Memory Units

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-07-03T13:48:21.573970Z

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.