Paper Citation Record · LEDGER

Quamba: A Post-Training Quantization Recipe for Selective State Space Models

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2410.13229.

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

pith.paper-citation-record.v1
2410.13229 v2

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 2026-08-07T06:34:17.273281+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:54:51.776424Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T21:08:58.138772Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
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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 4f9dc540-850d-4776-9e5c-fda8eb03a6e2 · inbound

Quantizing Small-Scale State-Space Models for Edge AI cites this paper.

Quantizing Small-Scale State-Space Models for Edge AI Quamba: A Post-Training Quantization Recipe for Selective State Space Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T00:54:51.776424Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a2ae952-0c62-4fbf-8c97-ef6486a77be3 · inbound

QS4D: Quantization-aware training for efficient hardware deployment of structured state-space sequential models cites this paper.

QS4D: Quantization-aware training for efficient hardware deployment of structured state-space sequential models Quamba: A Post-Training Quantization Recipe for Selective State Space Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T19:16:24.272098Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c6ced7d0-e01f-4d1d-bb4b-4e3632aff35d · inbound

eMamba: Efficient Acceleration Framework for Mamba Models in Edge Computing cites this paper.

eMamba: Efficient Acceleration Framework for Mamba Models in Edge Computing Quamba: A Post-Training Quantization Recipe for Selective State Space Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T20:34:25.774194Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d20110d7-33b5-4b6b-a17f-4a294c61d121 · inbound

COREY: Entropy-Guided Runtime Chunk Scheduling for Selective Scan Kernels cites this paper.

COREY: Entropy-Guided Runtime Chunk Scheduling for Selective Scan Kernels Quamba: A Post-Training Quantization Recipe for Selective State Space Models

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-11T11:06:03.476969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6ef94bb7-6a23-4f8f-8e7a-76502c651650 · inbound

Ternary Mamba: Grouped Quantization-Aware Training of W1.58A16 State Space Models cites this paper.

Ternary Mamba: Grouped Quantization-Aware Training of W1.58A16 State Space Models Quamba: A Post-Training Quantization Recipe for Selective State Space Models

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-07-03T21:08:58.140324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 200549dc-d921-4109-8bf5-e377a06fe0fa · inbound

Energy- and Memory-Efficient PEFT Methods for Personalized On-Device SLMs on Consumer GPUs cites this paper.

Energy- and Memory-Efficient PEFT Methods for Personalized On-Device SLMs on Consumer GPUs Quamba: A Post-Training Quantization Recipe for Selective State Space Models

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-06T23:25:10.355759Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.