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Paper Citation Record · LEDGER

PALS: Percentile-Aware Layerwise Sparsity for LLM Pruning

As of 18 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 1 inbound Pith citation observation for arXiv:2607.07557.

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

pith.paper-citation-record.v1
2607.07557 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-09T07:15:50.133587Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T01:03:27.178402Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T01:03:31.456510Z

Reference resolution

27 of 27 outbound references displayed

  • verified exact1
  • verified fuzzy14
  • unresolved1
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch11

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6a74a955-ec21-464d-93ce-7fac425d9fc2 · outbound

This paper cites Advances in neural information processing systems , volume=.

PALS: Percentile-Aware Layerwise Sparsity for LLM Pruning Advances in neural information processing systems , volume=

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T07:16:04.228177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-07-09T07:15:50.133587Z digest=sha256:8874ec56392021ad4c0570969408f08236bcee411f01dc4eddfad84547f0107d

Observation f663a40c-335f-4aad-99e2-cafa0af3314b · outbound

This paper cites PaLM: Scaling Language Modeling with Pathways.

PALS: Percentile-Aware Layerwise Sparsity for LLM Pruning PaLM: Scaling Language Modeling with Pathways

Reference 2

Resolution
metadata mismatch
local_arxiv, observed 2026-07-09T07:16:04.174928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-07-09T07:15:50.133587Z digest=sha256:eb544d4d71c9fc5c1a1b300c70964d64b01b7d1841a735edb7653488638ee156

Observation 5639d8fa-c0e9-417e-b0c7-dff11af5d970 · outbound

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

PALS: Percentile-Aware Layerwise Sparsity for LLM Pruning LLaMA: Open and Efficient Foundation Language Models

Reference 3

Resolution
metadata mismatch
local_arxiv, observed 2026-07-09T07:16:04.168693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-07-09T07:15:50.133587Z digest=sha256:5ae5426c4fcd848f4efd1b72e6bda496f9b6b2f48a1dd693ac4a671218ae30fb

Observation f77dd2a4-2fbd-46de-9b41-64d1d8d85395 · outbound

This paper cites Journal of Machine Learning Research , volume=.

PALS: Percentile-Aware Layerwise Sparsity for LLM Pruning Journal of Machine Learning Research , volume=

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T07:16:04.221170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-07-09T07:15:50.133587Z digest=sha256:79a3db73a2623ad835e949e3f46ba7f33665fa9f2c9511c2b722aa116791a151

Observation e7343406-b679-4d17-848a-7342a0b86274 · outbound

This paper cites Advances in neural information processing systems , pages=.

PALS: Percentile-Aware Layerwise Sparsity for LLM Pruning Advances in neural information processing systems , pages=

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T07:16:04.244867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-07-09T07:15:50.133587Z digest=sha256:888303d45a6c537f02a44a2f7ab5b71c2eeb36cca2ebca3c65a72f424571f380

Observation 3b1b7d12-6888-4f46-bf94-56996568d230 · outbound

This paper cites Advances in neural information processing systems , pages=.

PALS: Percentile-Aware Layerwise Sparsity for LLM Pruning Advances in neural information processing systems , pages=

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T07:16:04.239075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-07-09T07:15:50.133587Z digest=sha256:c41984d5af95146fee72312eb1429b6357314dcba4162c76c655adb57aadfbb5

Observation 5a20f46f-a478-46c6-955c-15fcb14da5df · outbound

This paper cites SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot.

PALS: Percentile-Aware Layerwise Sparsity for LLM Pruning SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot

Reference 7

Resolution
metadata mismatch
local_arxiv, observed 2026-07-09T07:16:04.163023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-07-09T07:15:50.133587Z digest=sha256:fdae1a0aa921b63b3c7a0bd2dc961a4dc0af2f0edd542d73c76ad92eaa4e5a0e

Observation 2bc277b6-8600-4d6e-bae7-6fc5a07dd8e5 · outbound

This paper cites A Simple and Effective Pruning Approach for Large Language Models.

PALS: Percentile-Aware Layerwise Sparsity for LLM Pruning A Simple and Effective Pruning Approach for Large Language Models

Reference 8

Resolution
metadata mismatch
local_arxiv, observed 2026-07-09T07:16:04.196767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-07-09T07:15:50.133587Z digest=sha256:72a7a1f411727ab234b7442b4be9df5d5b6c303e5d58d1607383131c5093b50c

Observation 562d79ca-6318-4440-a7dc-1d6f3fc6e665 · outbound

This paper cites Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned.

PALS: Percentile-Aware Layerwise Sparsity for LLM Pruning Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned

Reference 9

Resolution
metadata mismatch
local_arxiv, observed 2026-07-09T07:16:04.180815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-07-09T07:15:50.133587Z digest=sha256:248e62b1359e7a05fcd8b4379467eb8a691ce63e9bc096d6e99c60769cb0e358

Observation cbd78e39-63fe-45e7-a617-f222a1e79e22 · outbound

This paper cites Advances in neural information processing systems , volume=.

PALS: Percentile-Aware Layerwise Sparsity for LLM Pruning Advances in neural information processing systems , volume=

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T07:16:04.247693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-07-09T07:15:50.133587Z digest=sha256:59c755705bf7ba79101e934f9f7df48128138da9ab837b9316eecb290543e38b

Observation 4f7c88c5-2b81-4888-938e-97472b07bd0b · outbound

This paper cites Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) , pages=.

PALS: Percentile-Aware Layerwise Sparsity for LLM Pruning Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) , pages=

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T07:16:04.253566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-07-09T07:15:50.133587Z digest=sha256:1e0f6f611bba7238dc00fe9a5029167c6d177b30e3e44a8ed953ccdf262c3596

Observation 2f300183-cc21-4f51-ab88-c901bfe3e3b2 · outbound

This paper cites Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , pages=.

PALS: Percentile-Aware Layerwise Sparsity for LLM Pruning Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , pages=

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T07:16:04.232030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-07-09T07:15:50.133587Z digest=sha256:e715c6667fd2758ed14f73bb57c61e7a05350bd3a34ee4bc4eec2ab3dce6ed14

Observation af32c5ea-1cbd-4f8c-b6e6-6e5b6b2f4eeb · outbound

This paper cites International Conference on Learning Representations , year=.

PALS: Percentile-Aware Layerwise Sparsity for LLM Pruning International Conference on Learning Representations , year=

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T07:16:04.241795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-07-09T07:15:50.133587Z digest=sha256:83669f612139999dd449cb734231572fd4164533cd92f33734c9cba139c56b8a

Observation 1118065d-29a9-4e80-b2dd-3256e62b90ca · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

PALS: Percentile-Aware Layerwise Sparsity for LLM Pruning Advances in Neural Information Processing Systems , volume=

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T07:16:04.256819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-07-09T07:15:50.133587Z digest=sha256:86f6bdaa940b689b1c6c1672b0dbaca0eb8203476d5cdca0b8e3a3c8c48973cb

Observation 2629a558-3966-41c0-95f2-2db890f38932 · outbound

This paper cites Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning.

PALS: Percentile-Aware Layerwise Sparsity for LLM Pruning Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning

Reference 15

Resolution
verified exact
local_arxiv, observed 2026-07-09T07:16:04.147759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-07-09T07:15:50.133587Z digest=sha256:9577a7b7b0370edc2f08de3693e91d93f2aaf280ea7b3c21e86cd2d0962f0bca

Observation 66a076d9-44b3-4178-8ce2-6b33414b96fe · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

PALS: Percentile-Aware Layerwise Sparsity for LLM Pruning Advances in Neural Information Processing Systems , volume=

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T07:16:04.224568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-07-09T07:15:50.133587Z digest=sha256:0948fa545d07466cbe52be175d315d483dd6fe7a028ca98819e2b45d48af6b94

Observation 18df66cb-6e13-410c-9756-ba4d85f5fa9a · outbound

This paper cites SliceGPT: Compress Large Language Models by Deleting Rows and Columns.

PALS: Percentile-Aware Layerwise Sparsity for LLM Pruning SliceGPT: Compress Large Language Models by Deleting Rows and Columns

Reference 17

Resolution
metadata mismatch
local_arxiv, observed 2026-07-09T07:16:04.206433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-07-09T07:15:50.133587Z digest=sha256:834608d5def54e99e015baedf49c62fe2d1b760805cce2c55cf5c09913298974

Observation 54cfb100-cc16-4e96-9fd5-5d63a240f6c9 · outbound

This paper cites International Conference on Learning Representations , year=.

PALS: Percentile-Aware Layerwise Sparsity for LLM Pruning International Conference on Learning Representations , year=

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T07:16:04.262265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-07-09T07:15:50.133587Z digest=sha256:8c65666f514330a98297953759682011f1ca68ddf9f698a61de407fcab2f0a66

Observation cade172c-629a-4fd1-9d5e-28713ce8f0ed · outbound

This paper cites Faster gaze prediction with dense networks and Fisher pruning.

PALS: Percentile-Aware Layerwise Sparsity for LLM Pruning Faster gaze prediction with dense networks and Fisher pruning

Reference 19

Resolution
metadata mismatch
local_arxiv, observed 2026-07-09T07:16:04.153672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-07-09T07:15:50.133587Z digest=sha256:b06905ff3956831d8290f3593b6ed97602847aeb69dfb9fd10f26b11f8cb52eb

Observation 400c6307-dab2-4c98-9956-0c8bfff0d564 · outbound

This paper cites LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning.

PALS: Percentile-Aware Layerwise Sparsity for LLM Pruning LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning

Reference 20

Resolution
metadata mismatch
local_arxiv, observed 2026-07-09T07:16:04.191634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-07-09T07:15:50.133587Z digest=sha256:757a452b80f3c7bbd55e9fcdc8ac929e0f1108e79fe5d7a9fd37293e63b2840b

Observation 569525ea-2a68-4734-ac65-a2a754f7df68 · outbound

This paper cites Pointer Sentinel Mixture Models.

PALS: Percentile-Aware Layerwise Sparsity for LLM Pruning Pointer Sentinel Mixture Models

Reference 21

Resolution
metadata mismatch
local_arxiv, observed 2026-07-09T07:16:04.201755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-07-09T07:15:50.133587Z digest=sha256:2b68eef0b6d5ab3a455cf45da04f97d18ac5467e4f8476931d86939c5fb50904

Observation c30846b4-42a4-4ab3-bf45-41f8ea60f783 · outbound

This paper cites Proceedings of the AAAI Conference on Artificial Intelligence , volume=.

PALS: Percentile-Aware Layerwise Sparsity for LLM Pruning Proceedings of the AAAI Conference on Artificial Intelligence , volume=

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T07:16:04.235867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-07-09T07:15:50.133587Z digest=sha256:c205b2b09f1498181ad736ffe21b9cdad6f5d16bf3b7c62293781301254cc5c3

Observation 29ba3800-ea07-4dcc-9318-ab0fe9f173c2 · outbound

This paper cites Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , pages=.

PALS: Percentile-Aware Layerwise Sparsity for LLM Pruning Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , pages=

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T07:16:04.259697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-07-09T07:15:50.133587Z digest=sha256:ebd4d731450c0d47e3f0e47930c5412b7a70d416b225fd4c0c3a1961f97a1fd6

Observation 8144a2aa-1545-4f47-baf0-2b533f87bb9f · outbound

This paper cites BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions.

PALS: Percentile-Aware Layerwise Sparsity for LLM Pruning BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 24

Resolution
metadata mismatch
local_arxiv, observed 2026-07-09T07:16:04.158216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-07-09T07:15:50.133587Z digest=sha256:df06498b091c647ec1a57e5fb5047e8bd07fe062dc380ce4156e991f691d2cf3

Observation ec3affd4-8c04-4339-817f-03d9e47d7c3c · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

PALS: Percentile-Aware Layerwise Sparsity for LLM Pruning Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 25

Resolution
metadata mismatch
local_arxiv, observed 2026-07-09T07:16:04.186198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-07-09T07:15:50.133587Z digest=sha256:902323c9053f1e9b1bf584e868a646c5e6142c5c54ae3b3957a4fdd840a998c6

Observation d2930964-d68d-4b1f-b848-6e33656bbb45 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

PALS: Percentile-Aware Layerwise Sparsity for LLM Pruning Advances in Neural Information Processing Systems , volume=

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T07:16:04.250260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-07-09T07:15:50.133587Z digest=sha256:9825ff82a68d57822e99f884eabbae4523ad704ea393f8b160df91dfc9627179

Observation cdf9f81e-66af-482f-9f30-b39e38a1a082 · outbound

This paper cites an unresolved cited work.

PALS: Percentile-Aware Layerwise Sparsity for LLM Pruning Unresolved cited work

Reference 27

Resolution
unresolved
raw_fallback, observed 2026-07-09T07:16:04.217693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-07-09T07:15:50.133587Z digest=sha256:20fb08005bf7a941fa480870855dec1667f6802521307a61d04fc97574cb9684

Pith citing papers

Observation 1a880480-ac7b-4bbe-be50-505c8a2d4e2a · inbound

When Compression Scores Cannot Decide: Information Boundaries for Group-Robust LLM Pruning cites this paper.

When Compression Scores Cannot Decide: Information Boundaries for Group-Robust LLM Pruning PALS: Percentile-Aware Layerwise Sparsity for LLM Pruning

Reference 21

Resolution
metadata mismatch
local_arxiv, observed 2026-08-07T01:03:31.548017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T01:03:27.178402Z digest=sha256:6136a07bbd344639484ccaaebdd5d89f4b8f53fce4b13d56b6726f6215164565