Paper Citation Record · LEDGER

MINI-LLM: Memory-Efficient Structured Pruning for Large Language Models

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2407.11681.

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

pith.paper-citation-record.v1
2407.11681 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 2026-08-23T06:30:58.430688+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T13:11:27.226591Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation f94f497b-2ca5-434f-9822-3568c5aaf8cd · inbound

Lightweight Safety Classification Using Pruned Language Models cites this paper.

Lightweight Safety Classification Using Pruned Language Models MINI-LLM: Memory-Efficient Structured Pruning for Large Language Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T13:11:27.226591Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b459aaff-7bd0-4e27-8a53-9cb80e905329 · inbound

Adaptive Pruning for Large Language Models with Structural Importance Awareness cites this paper.

Adaptive Pruning for Large Language Models with Structural Importance Awareness MINI-LLM: Memory-Efficient Structured Pruning for Large Language Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T11:40:41.576792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3115ab8d-b32d-4938-9bb0-d46ac1d034bf · inbound

A Survey on Autonomy-Induced Security Risks in Large Model-Based Agents cites this paper.

A Survey on Autonomy-Induced Security Risks in Large Model-Based Agents MINI-LLM: Memory-Efficient Structured Pruning for Large Language Models

Reference 158

Resolution: unresolved; no resolver link, observed 2026-08-06T21:34:45.211340Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3afe0056-61d2-4fb7-8a26-dc13d06784e7 · inbound

A Survey: Towards Privacy and Security in Mobile Large Language Models cites this paper.

A Survey: Towards Privacy and Security in Mobile Large Language Models MINI-LLM: Memory-Efficient Structured Pruning for Large Language Models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-05T11:39:16.779457Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89909565-71fc-4374-a8bc-b063f4cd237a · inbound

DP-FlogTinyLLM: Differentially private federated log anomaly detection using Tiny LLMs cites this paper.

DP-FlogTinyLLM: Differentially private federated log anomaly detection using Tiny LLMs MINI-LLM: Memory-Efficient Structured Pruning for Large Language Models

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-10T02:53:29.784420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 488060d4-76bf-4e6a-8674-cc9c98399381 · inbound

SpenseGPT: Practical One-shot Pruning Enabling Sparse and Dense GEMMs for LLM Inference cites this paper.

SpenseGPT: Practical One-shot Pruning Enabling Sparse and Dense GEMMs for LLM Inference MINI-LLM: Memory-Efficient Structured Pruning for Large Language Models

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-06-27T14:00:59.492549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.