Paper Citation Record · LEDGER

LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2403.17919.

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

pith.paper-citation-record.v1
2403.17919 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:33:52.754333Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T23:47:28.526658Z

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 47ac372b-45d2-4da2-843c-777b6e795982 · inbound

Rethinking Data Mixture for Large Language Models: A Comprehensive Survey and New Perspectives cites this paper.

Rethinking Data Mixture for Large Language Models: A Comprehensive Survey and New Perspectives LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T13:33:52.754333Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87199d54-65de-4ae1-8da2-b4606b9373c0 · inbound

Scalable Parameter and Memory Efficient Pretraining for LLM: Recent Algorithmic Advances and Benchmarking cites this paper.

Scalable Parameter and Memory Efficient Pretraining for LLM: Recent Algorithmic Advances and Benchmarking LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T13:06:26.091459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc42aed7-32d9-4e23-a491-a20466003e96 · inbound

CLaSp: In-Context Layer Skip for Self-Speculative Decoding cites this paper.

CLaSp: In-Context Layer Skip for Self-Speculative Decoding LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T12:35:08.843505Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4582706e-1f12-4388-9402-9a06cb53819c · inbound

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts cites this paper.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T12:09:14.828645Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e56aa170-0938-48af-8751-55c8663e5247 · inbound

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation cites this paper.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T01:03:40.835197Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4d11c80-29ce-46d3-bb6f-08a658ac40fc · inbound

Geometrically Principled Randomized Optimization for Efficient LLM Training cites this paper.

Geometrically Principled Randomized Optimization for Efficient LLM Training LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T12:51:25.950349Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf1d601e-574f-4f14-a808-27cca2c92e09 · inbound

ParaBlock: Communication-Computation Parallel Block Coordinate Federated Learning for Large Language Models cites this paper.

ParaBlock: Communication-Computation Parallel Block Coordinate Federated Learning for Large Language Models LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-03T20:29:50.088506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad8f2af9-8e4e-4a1f-bfae-b45323152846 · inbound

Low Rank Adaptation for Adversarial Perturbation cites this paper.

Low Rank Adaptation for Adversarial Perturbation LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning

Reference 86

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:41:25.889194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2705d9ea-758b-4a3a-9b35-91b7ea410091 · inbound

Not How Many, But Which: Parameter Placement in Low-Rank Adaptation cites this paper.

Not How Many, But Which: Parameter Placement in Low-Rank Adaptation LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning

Reference 67

Resolution: verified exact; arxiv_id, observed 2026-05-13T06:17:23.199639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2b23c199-13aa-4e98-9633-84bf1a548376 · inbound

OptMuon: Closed-Loop Orthogonalized Momentum Methods for Stochastic Optimization with Zero-Noise Optimality cites this paper.

OptMuon: Closed-Loop Orthogonalized Momentum Methods for Stochastic Optimization with Zero-Noise Optimality LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning

Reference 93

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T23:47:28.528096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 88c64c50-b0b4-4e86-843d-181852b1f79e · inbound

OptMuon: Closed-Loop Orthogonalized Momentum Methods for Stochastic Optimization with Zero-Noise Optimality cites this paper.

OptMuon: Closed-Loop Orthogonalized Momentum Methods for Stochastic Optimization with Zero-Noise Optimality LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning

Reference 99

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T05:43:08.878987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.