Paper Citation Record · LEDGER

MambaLite-Micro: Memory-Optimized Mamba Inference on MCUs

As of 15 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2509.05488.

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

pith.paper-citation-record.v1
2509.05488 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T05:29:10.780393Z

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

12 of 12 outbound references displayed

verified exact1
verified fuzzy4
unresolved6
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d5a76b06-087d-498f-aeaa-5152e9d1d83f · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks.

MambaLite-Micro: Memory-Optimized Mamba Inference on MCUs Mobilenetv2: Inverted residuals and linear bottlenecks

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-05T05:29:10.725104Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9770fb24-1852-4c0e-82b5-7f03a18ac31a · outbound

This paper cites Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding.

MambaLite-Micro: Memory-Optimized Mamba Inference on MCUs Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-05T05:29:10.729997Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb7d2a58-2384-44e9-b822-807cd65dd084 · outbound

This paper cites Benchmarking TinyML Systems: Challenges and Direction.

MambaLite-Micro: Memory-Optimized Mamba Inference on MCUs Benchmarking TinyML Systems: Challenges and Direction

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-05T05:29:10.734983Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7709886d-9b72-448c-8032-c9377cde8006 · outbound

This paper cites Mcunet: Tiny deep learning on iot devices.Advances in neural information processing systems, 33:11711–11722, 2020.

MambaLite-Micro: Memory-Optimized Mamba Inference on MCUs Mcunet: Tiny deep learning on iot devices.Advances in neural information processing systems, 33:11711–11722, 2020

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:29:11.278017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7baf332f-a936-4ae0-8aec-3c2fe722fcad · outbound

This paper cites Tensorflow lite micro: Embedded machine learning for tinyml systems.Proceedings of machine learning and systems, 3:800–811, 2021.

MambaLite-Micro: Memory-Optimized Mamba Inference on MCUs Tensorflow lite micro: Embedded machine learning for tinyml systems.Proceedings of machine learning and systems, 3:800–811, 2021

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:29:11.262132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 97c809d5-f7a1-41f5-96fb-2d4fd46b2fe1 · outbound

This paper cites Improving the efficiency of transformers for resource-constrained devices.

MambaLite-Micro: Memory-Optimized Mamba Inference on MCUs Improving the efficiency of transformers for resource-constrained devices

Reference 6

Resolution: malformed identifier; arxiv_id_nonexistent, observed 2026-08-05T05:29:11.182045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b281a718-315c-4e82-85e0-c54a04366c76 · outbound

This paper cites Machine learning for microcontroller-class hardware: A review.IEEE Sensors Journal, 22(22):21362–21390, 2022.

MambaLite-Micro: Memory-Optimized Mamba Inference on MCUs Machine learning for microcontroller-class hardware: A review.IEEE Sensors Journal, 22(22):21362–21390, 2022

Reference 7

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-05T05:29:11.003993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 11da0735-4a87-4c51-a72b-47a9f54de27d · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

MambaLite-Micro: Memory-Optimized Mamba Inference on MCUs Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-05T05:29:10.761039Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e2719db8-fff0-44ac-9270-2f976715fb40 · outbound

This paper cites Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality.

MambaLite-Micro: Memory-Optimized Mamba Inference on MCUs Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-05T05:29:10.765605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a04ce28-faa0-4b67-a3df-776c3b7dbf49 · outbound

This paper cites Optimis- ing tinyml with quantization and distillation of transformer and mamba models for indoor localisation on edge devices.Scientific Reports, 15(1):10081, 2025.

MambaLite-Micro: Memory-Optimized Mamba Inference on MCUs Optimis- ing tinyml with quantization and distillation of transformer and mamba models for indoor localisation on edge devices.Scientific Reports, 15(1):10081, 2025

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:29:11.246074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e93a4c1a-1b87-4a9f-b98f-c843d4e5a2ec · outbound

This paper cites Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition.

MambaLite-Micro: Memory-Optimized Mamba Inference on MCUs Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T05:29:10.775645Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f044a698-ed73-4d14-8a1c-30a614e57ce2 · outbound

This paper cites A public domain dataset for human activity recognition using smartphones.

MambaLite-Micro: Memory-Optimized Mamba Inference on MCUs A public domain dataset for human activity recognition using smartphones

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:29:11.229776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

No inbound Pith citation observations are available.