Paper Citation Record · LEDGER

Explore Activation Sparsity in Recurrent LLMs for Energy-Efficient Neuromorphic Computing

As of 11 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2501.16337.

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

pith.paper-citation-record.v1
2501.16337 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:19:24.019283Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

22 of 22 outbound references displayed

verified exact0
verified fuzzy8
unresolved14
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c5562b48-5857-4979-9205-568171d0a2ef · outbound

This paper cites GPT-4 Technical Report.

Explore Activation Sparsity in Recurrent LLMs for Energy-Efficient Neuromorphic Computing GPT-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T21:19:23.922837Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87a30612-f20c-438f-bb0b-fbebed7678b7 · outbound

This paper cites The Llama 3 Herd of Models.

Explore Activation Sparsity in Recurrent LLMs for Energy-Efficient Neuromorphic Computing The Llama 3 Herd of Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-10T21:19:23.927698Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f156fd0e-0fc7-4f90-8395-ef9b42ae677e · outbound

This paper cites Rwkv: Reinventing rnns for the transformer era,.

Explore Activation Sparsity in Recurrent LLMs for Energy-Efficient Neuromorphic Computing Rwkv: Reinventing rnns for the transformer era,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:19:24.331741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a0291032-3b11-4eb3-b132-96fcec15f48d · outbound

This paper cites Retentive Network: A Successor to Transformer for Large Language Models.

Explore Activation Sparsity in Recurrent LLMs for Energy-Efficient Neuromorphic Computing Retentive Network: A Successor to Transformer for Large Language Models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-10T21:19:23.936121Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea691e74-7c54-4358-ab4e-312f5810f135 · outbound

This paper cites xLSTM: Extended Long Short-Term Memory.

Explore Activation Sparsity in Recurrent LLMs for Energy-Efficient Neuromorphic Computing xLSTM: Extended Long Short-Term Memory

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T21:19:23.940816Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9a88c47-c086-40f5-8f71-0df4b92ab72e · outbound

This paper cites Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models.

Explore Activation Sparsity in Recurrent LLMs for Energy-Efficient Neuromorphic Computing Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T21:19:23.945339Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22133e76-1b74-4101-ad25-5d625cb362b3 · outbound

This paper cites Efficiently Modeling Long Sequences with Structured State Spaces.

Explore Activation Sparsity in Recurrent LLMs for Energy-Efficient Neuromorphic Computing Efficiently Modeling Long Sequences with Structured State Spaces

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T21:19:23.950201Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f377a526-82ab-4779-ab03-9ea6bc163e01 · outbound

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

Explore Activation Sparsity in Recurrent LLMs for Energy-Efficient Neuromorphic Computing Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T21:19:23.954662Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3bc9b1e5-b440-4f81-a096-9ca7be0d8fff · outbound

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

Explore Activation Sparsity in Recurrent LLMs for Energy-Efficient Neuromorphic Computing Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T21:19:23.959349Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0feb78d6-fd6a-4060-b8a9-8f1ae5619f68 · outbound

This paper cites Seneca: building a fully digital neuromorphic processor, design trade-offs and challenges,.

Explore Activation Sparsity in Recurrent LLMs for Energy-Efficient Neuromorphic Computing Seneca: building a fully digital neuromorphic processor, design trade-offs and challenges,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:19:24.317555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a3f49d0f-e203-42cc-920d-4762655fce7f · outbound

This paper cites Ibm northpole neural inference machine,.

Explore Activation Sparsity in Recurrent LLMs for Energy-Efficient Neuromorphic Computing Ibm northpole neural inference machine,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:19:24.304443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 17e097d0-2a4e-4069-a78b-748575751490 · outbound

This paper cites Efficient neuromorphic signal processing with loihi 2,.

Explore Activation Sparsity in Recurrent LLMs for Energy-Efficient Neuromorphic Computing Efficient neuromorphic signal processing with loihi 2,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:19:24.291338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a7255b4f-2cd9-4eac-b9e9-a8f04a4063dd · outbound

This paper cites Optimizing event-based neural networks on digital neuromorphic architecture: a comprehensive design space exploration,.

Explore Activation Sparsity in Recurrent LLMs for Energy-Efficient Neuromorphic Computing Optimizing event-based neural networks on digital neuromorphic architecture: a comprehensive design space exploration,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:19:24.276387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation cb0b1fa6-0a07-451c-b7ac-405676da8da7 · outbound

This paper cites ReLU Strikes Back: Exploiting Activation Sparsity in Large Language Models.

Explore Activation Sparsity in Recurrent LLMs for Energy-Efficient Neuromorphic Computing ReLU Strikes Back: Exploiting Activation Sparsity in Large Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T21:19:23.988219Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63e38274-17ff-406f-8156-4598bbca26bd · outbound

This paper cites ProSparse: Introducing and Enhancing Intrinsic Activation Sparsity within Large Language Models.

Explore Activation Sparsity in Recurrent LLMs for Energy-Efficient Neuromorphic Computing ProSparse: Introducing and Enhancing Intrinsic Activation Sparsity within Large Language Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T21:19:23.992694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac22a64b-e64e-44fe-bef2-2505d453f573 · outbound

This paper cites LLM-PBE: Assessing Data Privacy in Large Language Models.

Explore Activation Sparsity in Recurrent LLMs for Energy-Efficient Neuromorphic Computing LLM-PBE: Assessing Data Privacy in Large Language Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T21:19:23.996902Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99e4c831-8965-497b-b74d-8c498d1edb13 · outbound

This paper cites OPT: Open Pre-trained Transformer Language Models.

Explore Activation Sparsity in Recurrent LLMs for Energy-Efficient Neuromorphic Computing OPT: Open Pre-trained Transformer Language Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T21:19:24.001067Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae933482-52b4-41b1-a824-887b15879650 · outbound

This paper cites The MiniPile Challenge for Data-Efficient Language Models.

Explore Activation Sparsity in Recurrent LLMs for Energy-Efficient Neuromorphic Computing The MiniPile Challenge for Data-Efficient Language Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T21:19:24.006339Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 38eba2c7-f56f-4b09-9846-38b8d0904b19 · outbound

This paper cites The Pile: An 800GB Dataset of Diverse Text for Language Modeling.

Explore Activation Sparsity in Recurrent LLMs for Energy-Efficient Neuromorphic Computing The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T21:19:24.010584Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91825268-d338-4c1c-9cba-ae506be844e4 · outbound

This paper cites A framework for few-shot language model evaluation,.

Explore Activation Sparsity in Recurrent LLMs for Energy-Efficient Neuromorphic Computing A framework for few-shot language model evaluation,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:19:24.248026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ccfe20c8-8439-489a-990f-fba1c779cc26 · outbound

This paper cites Open the box of digital neuromorphic processor: Towards effective algorithm-hardware co-design,.

Explore Activation Sparsity in Recurrent LLMs for Energy-Efficient Neuromorphic Computing Open the box of digital neuromorphic processor: Towards effective algorithm-hardware co-design,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:19:24.233626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 66fb3cf2-0276-4769-8ad1-55e30b326699 · outbound

This paper cites Available: https://api.semanticscholar.org/CorpusID: 268836639.

Explore Activation Sparsity in Recurrent LLMs for Energy-Efficient Neuromorphic Computing Available: https://api.semanticscholar.org/CorpusID: 268836639

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:19:24.260928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

No inbound Pith citation observations are available.