Paper Citation Record · LEDGER

ReLU Strikes Back: Exploiting Activation Sparsity in Large Language Models

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2310.04564.

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

pith.paper-citation-record.v1
2310.04564 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T13:44:00.593433Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T10:39:45.481307Z

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 b1b78aea-63f9-44a5-ac99-833b591b03f4 · inbound

SHARP: Accelerating Language Model Inference by SHaring Adjacent layers with Recovery Parameters cites this paper.

SHARP: Accelerating Language Model Inference by SHaring Adjacent layers with Recovery Parameters ReLU Strikes Back: Exploiting Activation Sparsity in Large Language Models

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-08T13:44:00.593433Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6808688d-741c-4180-8c1e-8b95383694c9 · inbound

On Space Folds of ReLU Neural Networks cites this paper.

On Space Folds of ReLU Neural Networks ReLU Strikes Back: Exploiting Activation Sparsity in Large Language Models

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T20:07:26.248303Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd293422-dbdf-40b9-8a85-9fd138d28d84 · inbound

SiLIF: Structured State Space Model Dynamics and Parametrization for Spiking Neural Networks cites this paper.

SiLIF: Structured State Space Model Dynamics and Parametrization for Spiking Neural Networks ReLU Strikes Back: Exploiting Activation Sparsity in Large Language Models

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-19T11:07:15.260111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d7dd08c2-2153-43ea-a3d4-cf8960f2ad0a · inbound

BlockFFN: Towards End-Side Acceleration-Friendly Mixture-of-Experts with Chunk-Level Activation Sparsity cites this paper.

BlockFFN: Towards End-Side Acceleration-Friendly Mixture-of-Experts with Chunk-Level Activation Sparsity ReLU Strikes Back: Exploiting Activation Sparsity in Large Language Models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T18:20:00.326711Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bff4c606-33e3-4f43-8258-bc8ccc9cd123 · inbound

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models cites this paper.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models ReLU Strikes Back: Exploiting Activation Sparsity in Large Language Models

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T05:11:11.632490Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca45b6e4-f873-4409-b5d2-5e1678fa5859 · inbound

Motivating Next-Gen Accelerators with Flexible (N:M) Activation Sparsity via Benchmarking Lightweight Post-Training Sparsification Approaches cites this paper.

Motivating Next-Gen Accelerators with Flexible (N:M) Activation Sparsity via Benchmarking Lightweight Post-Training Sparsification Approaches ReLU Strikes Back: Exploiting Activation Sparsity in Large Language Models

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-18T13:41:25.911858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 34ef5469-d50a-449e-953e-bad2ed9724c5 · inbound

Resting Neurons, Active Insights: Robustifying Activation Sparsity in LLMs via Spontaneity cites this paper.

Resting Neurons, Active Insights: Robustifying Activation Sparsity in LLMs via Spontaneity ReLU Strikes Back: Exploiting Activation Sparsity in Large Language Models

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-16T22:21:19.000745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4ae7e717-d6a6-4997-bbad-e62565ee0d79 · inbound

Resting Neurons, Active Insights: Robustifying Activation Sparsity in LLMs via Spontaneity cites this paper.

Resting Neurons, Active Insights: Robustifying Activation Sparsity in LLMs via Spontaneity ReLU Strikes Back: Exploiting Activation Sparsity in Large Language Models

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-22T11:54:51.122320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d0cf46d7-a057-4c14-ab32-228a08bc9970 · inbound

PowLU: An Activation Function for Stable Pre-Training of LLMs cites this paper.

PowLU: An Activation Function for Stable Pre-Training of LLMs ReLU Strikes Back: Exploiting Activation Sparsity in Large Language Models

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-06-29T21:53:59.458769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 707f502b-e8d0-49b9-bf84-1847f5a5d9ba · inbound

RT-Lynx: Putting the GEMM Sparsity In a Right Way for Diffusion Models cites this paper.

RT-Lynx: Putting the GEMM Sparsity In a Right Way for Diffusion Models ReLU Strikes Back: Exploiting Activation Sparsity in Large Language Models

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-06-29T19:43:54.732489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e2b2e5c2-07be-427a-9258-b8e15e13b9cd · inbound

Second-Order Path Kernel Interpolation Formulas in Machine Learning cites this paper.

Second-Order Path Kernel Interpolation Formulas in Machine Learning ReLU Strikes Back: Exploiting Activation Sparsity in Large Language Models

Reference 162

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T16:47:10.323933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation df2560b8-dcd8-4468-b7d2-f7cab059ea2e · inbound

GRINQH: Graded Input-based Quantization Hierarchy for Efficient LLM Generation cites this paper.

GRINQH: Graded Input-based Quantization Hierarchy for Efficient LLM Generation ReLU Strikes Back: Exploiting Activation Sparsity in Large Language Models

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-07-04T10:39:45.482722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 046a4b1d-a727-4c83-8e61-70a51656693c · inbound

Can Transformers Really Do It All? On the Compatibility of Inductive Biases Across Tasks cites this paper.

Can Transformers Really Do It All? On the Compatibility of Inductive Biases Across Tasks ReLU Strikes Back: Exploiting Activation Sparsity in Large Language Models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-01T17:31:48.435407Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4298369-c51b-4d2d-b6cc-71adf6e1b86a · inbound

SelectInfer: Selective Neuron Loading and Computation for On-Device LLMs cites this paper.

SelectInfer: Selective Neuron Loading and Computation for On-Device LLMs ReLU Strikes Back: Exploiting Activation Sparsity in Large Language Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-01T16:14:37.754503Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.