Paper Citation Record · LEDGER

SPikE-SSM: A Sparse, Precise, and Efficient Spiking State Space Model for Long Sequences Learning

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2410.17268.

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

pith.paper-citation-record.v1
2410.17268 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:55:02.070963Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T13:48:21.535269Z

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 f0f8cc3e-dbcb-45e4-bd4e-0162a8702dab · inbound

Efficient Spiking Point Mamba for Point Cloud Analysis cites this paper.

Efficient Spiking Point Mamba for Point Cloud Analysis SPikE-SSM: A Sparse, Precise, and Efficient Spiking State Space Model for Long Sequences Learning

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-16T11:55:02.070963Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ffb30ab9-86e6-45be-b78c-da23d3333781 · 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 SPikE-SSM: A Sparse, Precise, and Efficient Spiking State Space Model for Long Sequences Learning

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-16T10:38:10.562731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e1f17e3-1b68-45b8-9763-26e4dc24356a · inbound

FCKT: Fine-Grained Cross-Task Knowledge Transfer with Semantic Contrastive Learning for Targeted Sentiment Analysis cites this paper.

FCKT: Fine-Grained Cross-Task Knowledge Transfer with Semantic Contrastive Learning for Targeted Sentiment Analysis SPikE-SSM: A Sparse, Precise, and Efficient Spiking State Space Model for Long Sequences Learning

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T13:46:20.968379Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28df3c9b-c117-46fe-bca5-30df56047f98 · inbound

SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba cites this paper.

SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba SPikE-SSM: A Sparse, Precise, and Efficient Spiking State Space Model for Long Sequences Learning

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-08-04T02:08:04.038092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f5f3ac7b-cf25-41c8-982b-240c9154246c · inbound

MAR: Efficient Large Language Models via Module-aware Architecture Refinement cites this paper.

MAR: Efficient Large Language Models via Module-aware Architecture Refinement SPikE-SSM: A Sparse, Precise, and Efficient Spiking State Space Model for Long Sequences Learning

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-08-04T02:08:04.038092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 196e9b40-f23e-4d1b-8aeb-d6677ba72679 · 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 SPikE-SSM: A Sparse, Precise, and Efficient Spiking State Space Model for Long Sequences Learning

Reference 74

Resolution: verified exact; arxiv_id, observed 2026-08-04T02:08:04.038092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.