Paper Citation Record · LEDGER

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling

As of 13 August 2026, this Paper Citation Record lists 67 of 67 outbound references and 7 inbound Pith citation observations for arXiv:2506.04179.

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

pith.paper-citation-record.v1
2506.04179 v1

Coverage vector

measured 67 of 67 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:51:51.687705Z

measured 74 of 74 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T04:30:14.506806Z

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

67 of 67 outbound references displayed

verified exact3
verified fuzzy7
unresolved57
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

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

Outbound references

Observation b9a91ac5-7b24-4128-b4a9-98d78d859e90 · outbound

This paper cites write newline.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling write newline

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.383918Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85c7b218-6568-4272-a198-914902691727 · outbound

This paper cites Fluctuation-based adaptive structured pruning for large language models.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling Fluctuation-based adaptive structured pruning for large language models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.390078Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c87c87a-4c8f-4d5a-b0f4-610e08c82850 · outbound

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

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling SliceGPT: Compress Large Language Models by Deleting Rows and Columns

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.394725Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 67f46d71-d247-4032-9029-1972de44a27a · outbound

This paper cites A., Bourne, J.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling A., Bourne, J

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.399546Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a3c16d1-8058-447a-affa-bbab4f6ab1f2 · outbound

This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.403709Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c8cbd3c-6ae2-4191-8156-ef6656c93417 · outbound

This paper cites Piqa: Reasoning about physical commonsense in natural language.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling Piqa: Reasoning about physical commonsense in natural language

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.408641Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65c68948-ae3f-4416-98e0-6a28d3eeb0bb · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling On the Opportunities and Risks of Foundation Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.414182Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e4ff121-ee0c-41e2-ab1d-93250b6dc3f8 · outbound

This paper cites Language Models are Few-Shot Learners.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling Language Models are Few-Shot Learners

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.419334Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44a512e3-664f-47e8-b6c4-1e23a1b71017 · outbound

This paper cites A Survey on Mixture of Experts in Large Language Models.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling A Survey on Mixture of Experts in Large Language Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.424020Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 567103ee-da14-48b1-994e-2e33bbada56b · outbound

This paper cites Streamlining Redundant Layers to Compress Large Language Models.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling Streamlining Redundant Layers to Compress Large Language Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.433698Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86b98c04-287d-430e-810a-5d9c39a29eba · outbound

This paper cites Unveiling the Key Factors for Distilling Chain-of-Thought Reasoning.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling Unveiling the Key Factors for Distilling Chain-of-Thought Reasoning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.438165Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59cd8c77-c4e2-48e5-9edf-3f6eddce050b · outbound

This paper cites EE-LLM: Large-Scale Training and Inference of Early-Exit Large Language Models with 3D Parallelism.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling EE-LLM: Large-Scale Training and Inference of Early-Exit Large Language Models with 3D Parallelism

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.442664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71cf1183-3754-46b1-9227-b71397be3bfe · outbound

This paper cites PaLM: Scaling Language Modeling with Pathways.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling PaLM: Scaling Language Modeling with Pathways

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.447085Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1a3d765-047d-485a-bb8d-0ac14c4a1096 · outbound

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

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.451645Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad860cb4-2897-4d47-93bc-454d33095eb6 · outbound

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

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.456217Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1a419c3-1e09-48a1-9735-38f8230e1542 · outbound

This paper cites Redpajama: an open dataset for training large language models.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling Redpajama: an open dataset for training large language models

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:51:52.695418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1e81d5a2-1bf9-4671-b9ad-37fb719fdde5 · outbound

This paper cites SkipDecode: Autoregressive Skip Decoding with Batching and Caching for Efficient LLM Inference.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling SkipDecode: Autoregressive Skip Decoding with Batching and Caching for Efficient LLM Inference

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.465417Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27c2cd37-b471-4ef8-a939-742ea0980d9b · outbound

This paper cites The Llama 3 Herd of Models.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling The Llama 3 Herd of Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.469908Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 758b547f-5e71-42ed-8775-8fc551ed1a5e · outbound

This paper cites Not All Layers of LLMs Are Necessary During Inference.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling Not All Layers of LLMs Are Necessary During Inference

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.474200Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7bd51590-187b-4617-8788-ee3bce1ab7d4 · outbound

This paper cites and Alistarh, D.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling and Alistarh, D

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.478452Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 364f6f5f-86e6-498c-b655-e739447052b5 · outbound

This paper cites A framework for few-shot language model evaluation, 07 2024.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling A framework for few-shot language model evaluation, 07 2024

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.482651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ccf489c-0e36-49ee-835d-589bae8a606b · outbound

This paper cites Transformer Feed-Forward Layers Are Key-Value Memories.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling Transformer Feed-Forward Layers Are Key-Value Memories

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.486953Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4e4d684-fa8f-416f-b29c-851b1e4d532f · outbound

This paper cites The Unreasonable Ineffectiveness of the Deeper Layers.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling The Unreasonable Ineffectiveness of the Deeper Layers

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.491364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation abfc2050-4a0d-45b0-a51a-9de5804fed69 · outbound

This paper cites an unresolved cited work.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling Unresolved cited work

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.495830Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57458ad2-8bdf-4ae1-ab0c-0abbfd4fa9e6 · outbound

This paper cites What Matters in Transformers? Not All Attention is Needed.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling What Matters in Transformers? Not All Attention is Needed

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.500204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d9032ef-73f0-4afd-9cf3-2e37da695058 · outbound

This paper cites AdaSkip: Adaptive Sublayer Skipping for Accelerating Long-Context LLM Inference.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling AdaSkip: Adaptive Sublayer Skipping for Accelerating Long-Context LLM Inference

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-07T10:51:52.153846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d8479f21-51b8-4af9-953f-660eb6f9da2c · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling LoRA: Low-Rank Adaptation of Large Language Models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.509419Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d1caabb-9c82-4424-991a-1e4a52d10dad · outbound

This paper cites Categorical reparameterization with gumbel-softmax.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling Categorical reparameterization with gumbel-softmax

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:51:52.661867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0649cc5a-80e9-4a84-b612-c8d638cf1667 · outbound

This paper cites an unresolved cited work.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling Unresolved cited work

Reference 30

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:51:52.647194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 08e55413-5296-4b2e-8ddf-2bbbfb434140 · outbound

This paper cites Shortened LLaMA: Depth Pruning for Large Language Models with Comparison of Retraining Methods.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling Shortened LLaMA: Depth Pruning for Large Language Models with Comparison of Retraining Methods

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.522792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5abc13d0-f4da-471c-b819-681ded7b39fb · outbound

This paper cites Attention is not only a weight: Analyzing transformers with vector norms.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling Attention is not only a weight: Analyzing transformers with vector norms

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:51:52.632129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 54809726-6084-4e2a-af6d-f9ced485ff93 · outbound

This paper cites Crafting papers on machine learning.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling Crafting papers on machine learning

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.531435Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79118f7e-cfa5-433e-a06b-f998ef0168b7 · outbound

This paper cites GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.535857Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b63162f-4edb-44c7-9cba-bd987b043aaa · outbound

This paper cites Decoupled Weight Decay Regularization.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling Decoupled Weight Decay Regularization

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.540562Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c8584f6-4e45-430f-bd07-74eacc7830e9 · outbound

This paper cites Llm-pruner: On the structural pruning of large language models.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling Llm-pruner: On the structural pruning of large language models

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.545547Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b8412b4-dcb0-4f6a-bb2c-29d23a98e13c · outbound

This paper cites A* Sampling.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling A* Sampling

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.550043Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1254ca6-3c05-41bd-be2e-d6222051a084 · outbound

This paper cites an unresolved cited work.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling Unresolved cited work

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.554441Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39bd4f1d-4987-43e3-a356-07cd9f7a1921 · outbound

This paper cites ShortGPT: Layers in Large Language Models are More Redundant Than You Expect.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling ShortGPT: Layers in Large Language Models are More Redundant Than You Expect

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.558594Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0a6580b-815d-491f-9d5b-e47447f2665e · outbound

This paper cites Locating and Editing Factual Associations in GPT.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling Locating and Editing Factual Associations in GPT

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.563082Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eebd4424-dded-424e-9b4e-c9ffc82c33f7 · outbound

This paper cites Pointer Sentinel Mixture Models.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling Pointer Sentinel Mixture Models

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.567460Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c67925a3-5da0-4d95-8f55-64ccc4570217 · outbound

This paper cites Language Models Implement Simple Word2Vec-style Vector Arithmetic.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling Language Models Implement Simple Word2Vec-style Vector Arithmetic

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.572272Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ce2b429-d578-4770-9ce2-6ad2150ebed0 · outbound

This paper cites Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.576835Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 389ff1c7-a688-42fe-b99c-a2f603a2374f · outbound

This paper cites In-context learning and induction heads.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling In-context learning and induction heads

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.581218Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b16e24a0-c1c1-42db-8ee7-a3f9bad809ce · outbound

This paper cites GPT-4 Technical Report.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling GPT-4 Technical Report

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.585303Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0982c9c1-6ca9-4d84-ba1c-c739a1938630 · outbound

This paper cites Mixture-of-Depths: Dynamically allocating compute in transformer-based language models.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling Mixture-of-Depths: Dynamically allocating compute in transformer-based language models

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.589682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25bfb0f7-bcdd-4886-8414-e6d0f5840e55 · outbound

This paper cites L., Bhagavatula, C., and Choi, Y.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling L., Bhagavatula, C., and Choi, Y

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.594024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac2d0f5c-1f4b-43ce-b5ec-f955e944b69f · outbound

This paper cites From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inference.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inference

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.598371Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6465e8e1-1279-4512-9075-4d31891bf671 · outbound

This paper cites Confident adaptive language modeling.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling Confident adaptive language modeling

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:51:52.573169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 81592bf1-f8b0-46af-b943-035313db2e30 · outbound

This paper cites A deeper look at depth pruning of LLMs.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling A deeper look at depth pruning of LLMs

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.607178Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b973fc4-827a-4292-85b3-0f0987a2249b · outbound

This paper cites SLEB: Streamlining LLMs through Redundancy Verification and Elimination of Transformer Blocks.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling SLEB: Streamlining LLMs through Redundancy Verification and Elimination of Transformer Blocks

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.611505Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c71b8e6-4aef-4efa-a5ab-84932d71a32e · outbound

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

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling LLaMA: Open and Efficient Foundation Language Models

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.616024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6df09788-7a3c-44f3-8680-fe9326680144 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.620475Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9db35863-6b2f-4f01-b7da-f586531836fe · outbound

This paper cites Accelerating LLaMA Inference by Enabling Intermediate Layer Decoding via Instruction Tuning with LITE.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling Accelerating LLaMA Inference by Enabling Intermediate Layer Decoding via Instruction Tuning with LITE

Reference 54

Resolution: verified exact; local_arxiv, observed 2026-08-07T10:51:51.890716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1bd9520c-03a4-45dd-ba5f-09427cb3ef43 · outbound

This paper cites N., Kaiser, L.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling N., Kaiser, L

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.629304Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6b78270-85ac-48e4-8724-6d02e4619f28 · outbound

This paper cites Efficient Large Language Models: A Survey.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling Efficient Large Language Models: A Survey

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.633694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3db335a5-80ab-45da-8228-8af86a885a96 · outbound

This paper cites SkipNet: Learning Dynamic Routing in Convolutional Networks.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling SkipNet: Learning Dynamic Routing in Convolutional Networks

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.638326Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 620cf30b-06b6-4e6a-be4d-5ecac84743da · outbound

This paper cites Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.643273Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f77f889f-2dd9-45c8-b9f2-72365a5c2adb · outbound

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

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.647844Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation adb616ff-7897-4ee0-9337-b5581c307a1f · outbound

This paper cites Llmcdsr: Enhancing cross-domain sequential recommendation with large language models.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling Llmcdsr: Enhancing cross-domain sequential recommendation with large language models

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:51:52.550467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation cfdedba0-f9df-4cc3-a173-0be35f3f733e · outbound

This paper cites LaCo: Large Language Model Pruning via Layer Collapse.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling LaCo: Large Language Model Pruning via Layer Collapse

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.656370Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e874a923-5978-4797-a718-667fd4a9fdce · outbound

This paper cites Jump to conclusions: Short-cutting transformers with linear transformations.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling Jump to conclusions: Short-cutting transformers with linear transformations

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:51:52.535639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c94b92bf-9967-4071-a714-347db78a2237 · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.665131Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 75111c31-4174-43dc-b107-f845284d354a · outbound

This paper cites Learning to Skip for Language Modeling.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling Learning to Skip for Language Modeling

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.669918Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed1fbe4e-1023-4944-866e-4d7844fb6999 · outbound

This paper cites FinerCut: Finer-grained Interpretable Layer Pruning for Large Language Models.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling FinerCut: Finer-grained Interpretable Layer Pruning for Large Language Models

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.674338Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d14d098-babc-451a-b4ff-7eaa3f9312cd · outbound

This paper cites Unveiling In-Context Learning: A Coordinate System to Understand Its Working Mechanism.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling Unveiling In-Context Learning: A Coordinate System to Understand Its Working Mechanism

Reference 66

Resolution: verified exact; local_arxiv, observed 2026-08-07T10:51:51.755863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 98eb3340-97d0-4814-a797-4655cb23ce42 · outbound

This paper cites LLaMA-MoE: Building Mixture-of-Experts from LLaMA with Continual Pre-training.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling LLaMA-MoE: Building Mixture-of-Experts from LLaMA with Continual Pre-training

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.683200Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32cf3760-6124-4f49-b387-c1a692a0a5c2 · outbound

This paper cites Aligning books and movies: Towards story-like visual explanations by watching movies and reading books.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling Aligning books and movies: Towards story-like visual explanations by watching movies and reading books

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:51:52.521096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

Observation 385cbaf2-4b61-46d3-baba-663a457b67f8 · inbound

Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models cites this paper.

Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling

Reference 57

Resolution: verified exact; arxiv_id, observed 2026-05-21T14:50:15.327483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9f8eb748-1179-4bab-a8f9-329a9567700c · inbound

ViCA: Efficient Multimodal LLMs with Vision-Only Cross-Attention cites this paper.

ViCA: Efficient Multimodal LLMs with Vision-Only Cross-Attention SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-03T03:36:43.880446Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b638f52-bda5-4990-90fc-d9e0c9e851c7 · inbound

SkipOPU: An FPGA-based Overlay Processor for Large Language Models with Dynamically Allocated Computation cites this paper.

SkipOPU: An FPGA-based Overlay Processor for Large Language Models with Dynamically Allocated Computation SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-02T18:16:48.745554Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c46fa90-6f3a-4f91-9a0f-55d937d31d15 · inbound

ProactiveLLM: Learning Active Interaction for Streaming Large Language Models cites this paper.

ProactiveLLM: Learning Active Interaction for Streaming Large Language Models SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling

Reference 115

Resolution: verified exact; arxiv_id, observed 2026-06-28T19:12:34.801340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation df16597e-72c9-4aaf-9dd3-92d8d01c3822 · inbound

CascadeFormer: Depth-Tapered Transformers Motivated by Gradient Fan-in Asymmetry cites this paper.

CascadeFormer: Depth-Tapered Transformers Motivated by Gradient Fan-in Asymmetry SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling

Reference 45

Resolution: verified exact; arxiv_id, observed 2026-06-26T05:29:00.096032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 58de471e-c8c0-4003-841f-75c0c4d1bee2 · inbound

End-to-End Dynamic Sparsity for Resource-Adaptive LLM Inference cites this paper.

End-to-End Dynamic Sparsity for Resource-Adaptive LLM Inference SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-07-01T17:35:52.025582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8ec82576-f1d4-4563-a1ce-99ee577f7eab · inbound

Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin cites this paper.

Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-10T04:30:14.506806Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.