Paper Citation Record · LEDGER

Fit and Prune: Fast and Training-free Visual Token Pruning for Multi-modal Large Language Models

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2409.10197.

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

pith.paper-citation-record.v1
2409.10197 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:38:31.752838Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T10:15:44.600996Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
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malformed identifier0
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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 04a75efd-fe91-4a21-b94f-83b37927bfcd · inbound

Growing a Multi-head Twig via Distillation and Reinforcement Learning to Accelerate Large Vision-Language Models cites this paper.

Growing a Multi-head Twig via Distillation and Reinforcement Learning to Accelerate Large Vision-Language Models Fit and Prune: Fast and Training-free Visual Token Pruning for Multi-modal Large Language Models

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-05-23T00:02:17.764089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 35d14d73-0835-4788-9491-3e49152ba743 · inbound

LaCo: Efficient Layer-wise Compression of Visual Tokens for Multimodal Large Language Models cites this paper.

LaCo: Efficient Layer-wise Compression of Visual Tokens for Multimodal Large Language Models Fit and Prune: Fast and Training-free Visual Token Pruning for Multi-modal Large Language Models

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-06T20:38:31.752838Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d18b4bf-9bd0-4340-9b6c-470f3b9efc82 · inbound

Fast3D: Accelerating 3D Multi-modal Large Language Models for Efficient 3D Scene Understanding cites this paper.

Fast3D: Accelerating 3D Multi-modal Large Language Models for Efficient 3D Scene Understanding Fit and Prune: Fast and Training-free Visual Token Pruning for Multi-modal Large Language Models

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-06T18:03:03.524477Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab771970-2e0f-4ef3-9662-e90719abfbf4 · inbound

SQAP-VLA: A Synergistic Quantization-Aware Pruning Framework for High-Performance Vision-Language-Action Models cites this paper.

SQAP-VLA: A Synergistic Quantization-Aware Pruning Framework for High-Performance Vision-Language-Action Models Fit and Prune: Fast and Training-free Visual Token Pruning for Multi-modal Large Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T19:47:08.863100Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f60cfa21-2421-4bf9-bc2c-8e4b2c0a7cc9 · inbound

ForestPrune: High-ratio Visual Token Compression for Video Multimodal Large Language Models via Spatial-Temporal Forest Modeling cites this paper.

ForestPrune: High-ratio Visual Token Compression for Video Multimodal Large Language Models via Spatial-Temporal Forest Modeling Fit and Prune: Fast and Training-free Visual Token Pruning for Multi-modal Large Language Models

Reference 61

Resolution: verified exact; arxiv_id, observed 2026-05-15T00:58:25.638913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8cd29f63-cb68-4c29-943a-5da731e9ceee · inbound

Toward Native Multimodal Modeling: A Roadmap cites this paper.

Toward Native Multimodal Modeling: A Roadmap Fit and Prune: Fast and Training-free Visual Token Pruning for Multi-modal Large Language Models

Reference 204

Resolution: verified exact; arxiv_id, observed 2026-06-29T23:04:01.765270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c8cfb87f-aaed-467f-9a20-037a7f0d1b85 · inbound

MS-Resampler: Multi-Scope Visual Resampling for Efficient Multimodal LLMs cites this paper.

MS-Resampler: Multi-Scope Visual Resampling for Efficient Multimodal LLMs Fit and Prune: Fast and Training-free Visual Token Pruning for Multi-modal Large Language Models

Reference 32

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T10:15:44.602300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b2a326a4-24bb-40bd-9e70-61d1d6b7b4b8 · inbound

SlimVLM: Sensitivity-aware Dynamic Structured Pruning with Adaptive Visual Token Selection for Efficient Vision-Language Models cites this paper.

SlimVLM: Sensitivity-aware Dynamic Structured Pruning with Adaptive Visual Token Selection for Efficient Vision-Language Models Fit and Prune: Fast and Training-free Visual Token Pruning for Multi-modal Large Language Models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-05T16:41:28.860018Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 03fcfec7-111b-4c18-8d98-5ee76158c127 · inbound

When Do Fewer Visual Tokens Accelerate Multimodal Inference? A Break-Even Study Across Decision Locations and Hardware cites this paper.

When Do Fewer Visual Tokens Accelerate Multimodal Inference? A Break-Even Study Across Decision Locations and Hardware Fit and Prune: Fast and Training-free Visual Token Pruning for Multi-modal Large Language Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-05T15:19:02.837652Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.