Paper Citation Record · LEDGER

Deterministic Differentiable Structured Pruning for Large Language Models

As of 4 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 1 inbound Pith citation observation for arXiv:2603.08065.

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

pith.paper-citation-record.v1
2603.08065 v2

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-15T14:13:51.621181Z

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-31T08:00:57.357203Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

13 of 13 outbound references displayed

verified exact8
verified fuzzy4
unresolved0
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 78dbfe2c-467f-4322-b2aa-628724a80875 · outbound

This paper cites doi: 10.1038/s41586-025-09422-z.

Deterministic Differentiable Structured Pruning for Large Language Models doi: 10.1038/s41586-025-09422-z

Reference 1

Resolution: verified exact; doi, observed 2026-05-15T14:15:54.499628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c2044c8d-551b-4411-a558-66710063e2da · outbound

This paper cites How do llms use their depth?arXiv preprint arXiv:2510.18871.

Deterministic Differentiable Structured Pruning for Large Language Models How do llms use their depth?arXiv preprint arXiv:2510.18871

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-15T14:15:54.672428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 464fc6db-a1df-439b-adf2-a29b4985f07d · outbound

This paper cites Prun- ing large language models with semi-structural adaptive sparse training.Proceedings of the AAAI Conference on Artificial Intelligence, 39(23):24167–24175, Apr.

Deterministic Differentiable Structured Pruning for Large Language Models Prun- ing large language models with semi-structural adaptive sparse training.Proceedings of the AAAI Conference on Artificial Intelligence, 39(23):24167–24175, Apr

Reference 3

Resolution: verified exact; doi, observed 2026-05-15T14:15:54.488210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 78f5891b-bf3b-4c63-ad45-62ef8a31eee9 · outbound

This paper cites LoRAP: Transformer Sub-Layers Deserve Differentiated Structured Compression for Large Language Models.

Deterministic Differentiable Structured Pruning for Large Language Models LoRAP: Transformer Sub-Layers Deserve Differentiated Structured Compression for Large Language Models

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-15T14:15:54.686536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f514184a-3ceb-40fe-b12e-d380b00d8be2 · outbound

This paper cites HEAPr: Hessian-based Efficient Atomic Expert Pruning in Output Space.

Deterministic Differentiable Structured Pruning for Large Language Models HEAPr: Hessian-based Efficient Atomic Expert Pruning in Output Space

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-26T03:04:56.387869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 187e73ca-0dbb-4e06-82b2-5736f271c7dd · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Deterministic Differentiable Structured Pruning for Large Language Models LLaMA: Open and Efficient Foundation Language Models

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-05-15T14:15:54.676971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 229f2aba-0425-43af-8e20-9de7c244256d · outbound

This paper cites GLUE: A multi-task benchmark and analy- sis platform for natural language understanding.

Deterministic Differentiable Structured Pruning for Large Language Models GLUE: A multi-task benchmark and analy- sis platform for natural language understanding

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:15:56.299754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b207c22a-84fd-4340-abe7-56f825fc5b62 · outbound

This paper cites Structured pruning of large language models.

Deterministic Differentiable Structured Pruning for Large Language Models Structured pruning of large language models

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:15:56.295181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9eea13d7-1881-49a1-b4b7-166c18f5783b · outbound

This paper cites Xia, M., Gao, T., Zeng, Z., and Chen, D.

Deterministic Differentiable Structured Pruning for Large Language Models Xia, M., Gao, T., Zeng, Z., and Chen, D

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:15:56.297440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6de74ce4-e716-41fa-a930-aa20025550c9 · outbound

This paper cites CAMERA: Multi-matrix joint compression for moe models via micro-expert redundancy analysis.

Deterministic Differentiable Structured Pruning for Large Language Models CAMERA: Multi-matrix joint compression for moe models via micro-expert redundancy analysis

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-15T14:15:54.691992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ebdf61ab-54a5-46d8-80a9-8f82e7b6577e · outbound

This paper cites LoRAPrune: Structured pruning meets low- rank parameter-efficient fine-tuning.

Deterministic Differentiable Structured Pruning for Large Language Models LoRAPrune: Structured pruning meets low- rank parameter-efficient fine-tuning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T14:15:56.293066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e5c5f494-f2ae-4ae2-8272-8fb38a9d8b14 · outbound

This paper cites Hence Sµr(z(r))− |A (r)| = KX k=1 ϕ(z(r) k ;µ r)− KX k=1 I[z(r) k >0] ≤ 1 2.

Deterministic Differentiable Structured Pruning for Large Language Models Hence Sµr(z(r))− |A (r)| = KX k=1 ϕ(z(r) k ;µ r)− KX k=1 I[z(r) k >0] ≤ 1 2

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-15T14:15:54.667885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7611ce92-1427-446f-a953-770e1cb0a6e4 · outbound

This paper cites U-shaped.

Deterministic Differentiable Structured Pruning for Large Language Models U-shaped

Reference 13

Resolution: malformed identifier; arxiv_id, observed 2026-05-15T14:15:54.682025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Pith citing papers

Observation c213ac60-5bda-4ec8-91fd-8196332a3d9b · inbound

WIDE: Boosting Adaptive LLM Inference via Token-level Dynamic Width Pruning cites this paper.

WIDE: Boosting Adaptive LLM Inference via Token-level Dynamic Width Pruning Deterministic Differentiable Structured Pruning for Large Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-31T08:00:57.357203Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.