Paper Citation Record · LEDGER

FTP: A Fine-grained Token-wise Pruner for Large Language Models via Token Routing

As of 17 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2412.11494.

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

pith.paper-citation-record.v1
2412.11494 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T14:57:27.662321Z

measured 29 of 29 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

29 of 29 outbound references displayed

verified exact0
verified fuzzy3
unresolved26
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7926d214-59c4-4cd2-bca8-31cd32be2505 · outbound

This paper cites ShadowLLM: Predictor-based Contextual Sparsity for Large Language Models.

FTP: A Fine-grained Token-wise Pruner for Large Language Models via Token Routing ShadowLLM: Predictor-based Contextual Sparsity for Large Language Models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T14:57:27.473508Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7bdb73e-6078-4d49-82c8-51007da723d5 · outbound

This paper cites Qwen Technical Report.

FTP: A Fine-grained Token-wise Pruner for Large Language Models via Token Routing Qwen Technical Report

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-11T14:57:27.486293Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c7bfb07-0cff-4a6b-8d35-d8eb84518be2 · outbound

This paper cites The Llama 3 Herd of Models.

FTP: A Fine-grained Token-wise Pruner for Large Language Models via Token Routing The Llama 3 Herd of Models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T14:57:27.500046Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5b9775c-3092-44b3-b88b-261dd267c354 · outbound

This paper cites Depth-adaptive transformer.

FTP: A Fine-grained Token-wise Pruner for Large Language Models via Token Routing Depth-adaptive transformer

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:57:28.248298Z

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.

Observation 62446f61-58ac-4d4b-8ad4-bbe5ca743ca7 · outbound

This paper cites Xl-sum: Large-scale multilingual abstractive sum- marization for 44 languages.

FTP: A Fine-grained Token-wise Pruner for Large Language Models via Token Routing Xl-sum: Large-scale multilingual abstractive sum- marization for 44 languages

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:57:28.208486Z

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.

Observation 25d8cfc8-71b8-43a1-95fd-c03e21235ad9 · outbound

This paper cites In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models.

FTP: A Fine-grained Token-wise Pruner for Large Language Models via Token Routing In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T14:57:27.531592Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a3dc63c-b151-4cc7-9af2-8ce3db85edf4 · outbound

This paper cites Compressing Context to Enhance Inference Efficiency of Large Language Models.

FTP: A Fine-grained Token-wise Pruner for Large Language Models via Token Routing Compressing Context to Enhance Inference Efficiency of Large Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T14:57:27.555568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 67c9036e-5de3-41e4-b7de-e19a01a168a0 · outbound

This paper cites Anytime Dense Prediction with Confidence Adaptivity.

FTP: A Fine-grained Token-wise Pruner for Large Language Models via Token Routing Anytime Dense Prediction with Confidence Adaptivity

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T14:57:27.561796Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b1b3ced-a91b-4af7-87cd-a40c25ab7436 · outbound

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

FTP: A Fine-grained Token-wise Pruner for Large Language Models via Token Routing ShortGPT: Layers in Large Language Models are More Redundant Than You Expect

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T14:57:27.571589Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82328b29-be81-4e2c-9672-8a8193e307d9 · outbound

This paper cites Large Language Models: A Survey.

FTP: A Fine-grained Token-wise Pruner for Large Language Models via Token Routing Large Language Models: A Survey

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T14:57:27.579545Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 567129bf-90b5-46be-a6a8-43297a01e3ce · outbound

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

FTP: A Fine-grained Token-wise Pruner for Large Language Models via Token Routing Mixture-of-Depths: Dynamically allocating compute in transformer-based language models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T14:57:27.587037Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91bf7c48-27a9-4baf-bc4c-022fd8b1f158 · outbound

This paper cites Weight subcloning: direct initialization of transformers using larger pretrained ones.

FTP: A Fine-grained Token-wise Pruner for Large Language Models via Token Routing Weight subcloning: direct initialization of transformers using larger pretrained ones

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T14:57:27.594549Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation afab0312-c173-4e21-b431-ba341f8d7d9b · outbound

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

FTP: A Fine-grained Token-wise Pruner for Large Language Models via Token Routing A Simple and Effective Pruning Approach for Large Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T14:57:27.601424Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a775116-d942-4406-9f3d-19ab39192ef5 · outbound

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

FTP: A Fine-grained Token-wise Pruner for Large Language Models via Token Routing Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T14:57:27.607105Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d86a23ae-8db0-4ddc-9d45-162ee7d35215 · outbound

This paper cites Model Compression and Efficient Inference for Large Language Models: A Survey.

FTP: A Fine-grained Token-wise Pruner for Large Language Models via Token Routing Model Compression and Efficient Inference for Large Language Models: A Survey

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T14:57:27.612783Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fbc542f4-3d8e-40f9-94d9-b5eec5b73084 · outbound

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

FTP: A Fine-grained Token-wise Pruner for Large Language Models via Token Routing Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T14:57:27.619770Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ad201fb-9411-4681-956a-e36e86fbda5b · outbound

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

FTP: A Fine-grained Token-wise Pruner for Large Language Models via Token Routing LaCo: Large Language Model Pruning via Layer Collapse

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-11T14:57:27.625982Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24c578fc-d579-4638-a711-602f533774ae · outbound

This paper cites Did You Read the Instructions? Rethinking the Effectiveness of Task Definitions in Instruction Learning.

FTP: A Fine-grained Token-wise Pruner for Large Language Models via Token Routing Did You Read the Instructions? Rethinking the Effectiveness of Task Definitions in Instruction Learning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T14:57:27.631484Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 165cf2d3-4ca2-4381-9e5a-aa09ac34edf3 · outbound

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

FTP: A Fine-grained Token-wise Pruner for Large Language Models via Token Routing HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T14:57:27.637839Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4fa577b5-af44-4da0-a106-c75f9d5b3e23 · outbound

This paper cites A Survey of Large Language Models.

FTP: A Fine-grained Token-wise Pruner for Large Language Models via Token Routing A Survey of Large Language Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T14:57:27.650898Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83dc59c8-aacf-4857-b220-cf515fee7487 · outbound

This paper cites BlockPruner: Fine-grained Pruning for Large Language Models.

FTP: A Fine-grained Token-wise Pruner for Large Language Models via Token Routing BlockPruner: Fine-grained Pruning for Large Language Models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-11T14:57:27.656730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bbc856ee-af68-4361-a01f-8ddae27968e4 · outbound

This paper cites an unresolved cited work.

FTP: A Fine-grained Token-wise Pruner for Large Language Models via Token Routing Unresolved cited work

Reference 29

Resolution: unresolved; raw_fallback, observed 2026-08-11T14:57:28.187066Z

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.

Observation b1bc1a00-5a49-4a02-bf38-f9fe7df36884 · outbound

This paper cites LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models.

FTP: A Fine-grained Token-wise Pruner for Large Language Models via Token Routing LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-11T14:57:27.548016Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39140dc0-ce8b-45ab-80bf-e1d8715e6492 · outbound

This paper cites APT: Adaptive Pruning and Tuning Pretrained Language Models for Efficient Training and Inference.

FTP: A Fine-grained Token-wise Pruner for Large Language Models via Token Routing APT: Adaptive Pruning and Tuning Pretrained Language Models for Efficient Training and Inference

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-11T14:57:27.645472Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de63f3d5-aec5-4ef2-b550-2ca84d2ffd83 · outbound

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

FTP: A Fine-grained Token-wise Pruner for Large Language Models via Token Routing Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-11T14:57:27.492381Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ffb1638-c57d-439a-8a1b-8c0e0c2c008c · outbound

This paper cites Measuring Massive Multitask Language Understanding.

FTP: A Fine-grained Token-wise Pruner for Large Language Models via Token Routing Measuring Massive Multitask Language Understanding

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-11T14:57:27.524940Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 47989978-ba53-44db-aefc-d14e6addc0ba · outbound

This paper cites Categorical Reparameterization with Gumbel-Softmax.

FTP: A Fine-grained Token-wise Pruner for Large Language Models via Token Routing Categorical Reparameterization with Gumbel-Softmax

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-11T14:57:27.541321Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bca45947-b405-4b6e-acdf-c8f700ecc828 · outbound

This paper cites Discrete model compression with resource constraint for deep neural networks.

FTP: A Fine-grained Token-wise Pruner for Large Language Models via Token Routing Discrete model compression with resource constraint for deep neural networks

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:57:28.228922Z

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.

Observation 7e33be7c-4e42-49a4-870e-e1ee5d130438 · outbound

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

FTP: A Fine-grained Token-wise Pruner for Large Language Models via Token Routing SliceGPT: Compress Large Language Models by Deleting Rows and Columns

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-11T14:57:27.479414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.