Paper Citation Record · LEDGER

P-SpikeSSM: Harnessing Probabilistic Spiking State Space Models for Long-Range Dependency Tasks

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2406.02923.

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

pith.paper-citation-record.v1
2406.02923 v5

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 2026-08-16T06:30:59.297886+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:38:10.383796Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T22:13:30.786493Z

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 bc851775-ecec-49e6-9890-6c469b2ffdc1 · inbound

A Survey of Mamba cites this paper.

A Survey of Mamba P-SpikeSSM: Harnessing Probabilistic Spiking State Space Models for Long-Range Dependency Tasks

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-23T22:13:30.788541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation dbcf4636-578c-4482-a09a-bec7e5d6265f · inbound

Revisiting Reset Mechanisms in Spiking Neural Networks for Sequential Modeling: Specialized Discretization for Binary Activated RNN cites this paper.

Revisiting Reset Mechanisms in Spiking Neural Networks for Sequential Modeling: Specialized Discretization for Binary Activated RNN P-SpikeSSM: Harnessing Probabilistic Spiking State Space Models for Long-Range Dependency Tasks

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-16T10:38:10.383796Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08d5f35b-e5e4-41b8-8917-d155fc8682a7 · inbound

Rivaling Transformers: Multi-Scale Structured State-Space Mixtures for Agentic 6G O-RAN cites this paper.

Rivaling Transformers: Multi-Scale Structured State-Space Mixtures for Agentic 6G O-RAN P-SpikeSSM: Harnessing Probabilistic Spiking State Space Models for Long-Range Dependency Tasks

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-04T11:25:33.440578Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 944dcdfd-7129-4bcc-bf54-2cd67ff02878 · inbound

Phase State Space Models: Parallel, Surrogate-Free Training of Spiking Networks cites this paper.

Phase State Space Models: Parallel, Surrogate-Free Training of Spiking Networks P-SpikeSSM: Harnessing Probabilistic Spiking State Space Models for Long-Range Dependency Tasks

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T00:29:09.641953Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.