Paper Citation Record · LEDGER

Dynamic-LLaVA: Efficient Multimodal Large Language Models via Dynamic Vision-language Context Sparsification

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2412.00876.

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

pith.paper-citation-record.v1
2412.00876 v4

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 2026-08-08T06:32:00.761636+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:26:17.210434Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T11:28:04.156046Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

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 17d12eee-c0e0-48c2-9b01-acc626a45d1b · inbound

CoreMatching: A Co-adaptive Sparse Inference Framework with Token and Neuron Pruning for Comprehensive Acceleration of Vision-Language Models cites this paper.

CoreMatching: A Co-adaptive Sparse Inference Framework with Token and Neuron Pruning for Comprehensive Acceleration of Vision-Language Models Dynamic-LLaVA: Efficient Multimodal Large Language Models via Dynamic Vision-language Context Sparsification

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T14:26:17.210434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e2cfb951-d60c-40ed-a503-e003e29d048e · inbound

EffiVLM-BENCH: A Comprehensive Benchmark for Evaluating Training-Free Acceleration in Large Vision-Language Models cites this paper.

EffiVLM-BENCH: A Comprehensive Benchmark for Evaluating Training-Free Acceleration in Large Vision-Language Models Dynamic-LLaVA: Efficient Multimodal Large Language Models via Dynamic Vision-language Context Sparsification

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T12:09:04.476990Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8f482e0-72c5-4128-b5c9-3dcb4ca4d28e · inbound

Generalizing vision-language models to novel domains: A comprehensive survey cites this paper.

Generalizing vision-language models to novel domains: A comprehensive survey Dynamic-LLaVA: Efficient Multimodal Large Language Models via Dynamic Vision-language Context Sparsification

Reference 287

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:05.844751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6dc1caad-d203-43c2-99b2-ab013659133f · 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 Dynamic-LLaVA: Efficient Multimodal Large Language Models via Dynamic Vision-language Context Sparsification

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T05:51:15.932858Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42a9394c-6e02-47db-8123-83d92b80befd · inbound

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

ViCA: Efficient Multimodal LLMs with Vision-Only Cross-Attention Dynamic-LLaVA: Efficient Multimodal Large Language Models via Dynamic Vision-language Context Sparsification

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-03T03:36:43.816273Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 351e5950-e55b-4294-bfbe-1bfe62c2fc2f · inbound

POINTS-Long: Adaptive Dual-Mode Visual Reasoning in MLLMs cites this paper.

POINTS-Long: Adaptive Dual-Mode Visual Reasoning in MLLMs Dynamic-LLaVA: Efficient Multimodal Large Language Models via Dynamic Vision-language Context Sparsification

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-11T10:41:04.106574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 17019183-24c4-4324-b72e-a840845346a6 · inbound

VisPCO: Visual Token Pruning Configuration Optimization via Budget-Aware Pareto-Frontier Learning for Vision-Language Models cites this paper.

VisPCO: Visual Token Pruning Configuration Optimization via Budget-Aware Pareto-Frontier Learning for Vision-Language Models Dynamic-LLaVA: Efficient Multimodal Large Language Models via Dynamic Vision-language Context Sparsification

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-10T11:30:18.493910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3386665b-c13f-4ecb-8e74-96742b88428b · inbound

LearnPruner: Rethinking Attention-based Token Pruning in Vision Language Models cites this paper.

LearnPruner: Rethinking Attention-based Token Pruning in Vision Language Models Dynamic-LLaVA: Efficient Multimodal Large Language Models via Dynamic Vision-language Context Sparsification

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:41:14.052310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5b0158d3-22ec-43c6-aad4-d357eddfa717 · inbound

Reroute, Don't Remove: Recoverable Visual Token Routing for Vision-Language Models cites this paper.

Reroute, Don't Remove: Recoverable Visual Token Routing for Vision-Language Models Dynamic-LLaVA: Efficient Multimodal Large Language Models via Dynamic Vision-language Context Sparsification

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-07-03T11:28:04.157902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.