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Paper Citation Record · LEDGER

Boosting Multimodal Large Language Models with Visual Tokens Withdrawal for Rapid Inference

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2405.05803.

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

pith.paper-citation-record.v1
2405.05803 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:42:53.659544Z

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

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

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

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation b00a0740-01fd-48c8-a130-bbcfc683fd6c · inbound

GreedyPrune: Retenting Critical Visual Token Set for Large Vision Language Models cites this paper.

GreedyPrune: Retenting Critical Visual Token Set for Large Vision Language Models Boosting Multimodal Large Language Models with Visual Tokens Withdrawal for Rapid Inference

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-07T00:42:53.659544Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:42:53.659544Z digest=sha256:0a9a5c4c6fe5ad9c94d43dc197e0ad9cca077903ff75ad93358e06f5ec8dc930

Observation a8c4bba1-5541-4156-9d32-07021750a33a · inbound

LLaVA-Scissor: Token Compression with Semantic Connected Components for Video LLMs cites this paper.

LLaVA-Scissor: Token Compression with Semantic Connected Components for Video LLMs Boosting Multimodal Large Language Models with Visual Tokens Withdrawal for Rapid Inference

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-06T22:24:28.571315Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:24:28.571315Z digest=sha256:b603b750864421f8740bd917d1333c73ea85da6e5c26a326d7cabff9781d34f2

Observation 8b21d098-658d-4f82-8300-bd55089c689f · 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 Boosting Multimodal Large Language Models with Visual Tokens Withdrawal for Rapid Inference

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-06T18:03:01.514560Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:03:01.514560Z digest=sha256:0a6f736544e371e7dec151fb3648cefcad418e955c72472859d1af5d2dd3c5f4

Observation fdc7355a-a878-48c0-a559-eb2e5a9c13c0 · inbound

Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers cites this paper.

Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Boosting Multimodal Large Language Models with Visual Tokens Withdrawal for Rapid Inference

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-06T10:55:18.205488Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T10:55:18.205488Z digest=sha256:abe1c6b0fd9087fc5eaf979be1011ced89f4b5f7ceeb93a9bf1f867b11968bda

Observation 8d4a8035-c364-4586-ba74-5874fe2d81fa · inbound

Mitigating Information Loss under High Pruning Rates for Efficient Large Vision Language Models cites this paper.

Mitigating Information Loss under High Pruning Rates for Efficient Large Vision Language Models Boosting Multimodal Large Language Models with Visual Tokens Withdrawal for Rapid Inference

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-06T05:51:15.965046Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T05:51:15.965046Z digest=sha256:7aeffe3d71ac63e5d10bcda8907052cc82cf5a765b6216966cde95ca826f4d57

Observation 65e85f8d-6a2d-495b-ae7e-823283bcc887 · inbound

IKOD: Mitigating Visual Attention Degradation in Large Vision-Language Models cites this paper.

IKOD: Mitigating Visual Attention Degradation in Large Vision-Language Models Boosting Multimodal Large Language Models with Visual Tokens Withdrawal for Rapid Inference

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-06T04:29:52.320117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T04:29:52.320117Z digest=sha256:ca1871e83d846da48b9d4e1a7f5a09cd33db24781120d6d3bdbb807549295b57

Observation be74d94d-1371-41f4-9704-d21c8c7b1298 · inbound

Late-Layer Fusion is Enough: Dual-Path Vision Token Routing for Multimodal Large Language Models under Visual Saturation cites this paper.

Late-Layer Fusion is Enough: Dual-Path Vision Token Routing for Multimodal Large Language Models under Visual Saturation Boosting Multimodal Large Language Models with Visual Tokens Withdrawal for Rapid Inference

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-06-27T16:41:03.330850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-27T16:32:09.784716Z digest=sha256:f8dbda67375f8aa19546d9852e0f8543c41689f60aa3c3d0d38fcf0e7192a290

Observation d8e5c122-354f-4bf7-851f-23d6203502e0 · 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 Boosting Multimodal Large Language Models with Visual Tokens Withdrawal for Rapid Inference

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-05T16:41:28.862652Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T16:41:28.862652Z digest=sha256:31a46c987bf1b86d5bba557821b1ea7cda67bf6893f967c6ef6d715e66b8bdd5