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Paper Citation Record · LEDGER

FrameFusion: Combining Similarity and Importance for Video Token Reduction on Large Vision Language Models

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2501.01986.

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

pith.paper-citation-record.v1
2501.01986 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:56:05.308899Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T18:36:44.180127Z

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 a916a4f9-d205-473b-b4f7-13df30d1a489 · inbound

DynTok: Dynamic Compression of Visual Tokens for Efficient and Effective Video Understanding cites this paper.

DynTok: Dynamic Compression of Visual Tokens for Efficient and Effective Video Understanding FrameFusion: Combining Similarity and Importance for Video Token Reduction on Large Vision Language Models

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-07T10:56:05.308899Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:56:05.308899Z digest=sha256:cf40b5e68f95ea5d673598b95b34f1e27420f838266d718a04512d910d0c2812

Observation ca7d1ebe-c8c2-49ed-9433-75b741fd4dc1 · inbound

PAROAttention: Pattern-Aware ReOrdering for Efficient Sparse and Quantized Attention in Visual Generation Models cites this paper.

PAROAttention: Pattern-Aware ReOrdering for Efficient Sparse and Quantized Attention in Visual Generation Models FrameFusion: Combining Similarity and Importance for Video Token Reduction on Large Vision Language Models

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-06T23:51:05.316975Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:51:05.316975Z digest=sha256:d2e0cd57928e8d14af034d8fecc7154665524aa2d78022c005632e815df1800e

Observation e842939a-c212-4e01-b7fd-dfc93bcc6dee · inbound

Multi-Granular Spatio-Temporal Token Merging for Training-Free Acceleration of Video LLMs cites this paper.

Multi-Granular Spatio-Temporal Token Merging for Training-Free Acceleration of Video LLMs FrameFusion: Combining Similarity and Importance for Video Token Reduction on Large Vision Language Models

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-06T18:32:46.455680Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:32:46.455680Z digest=sha256:587cf79070d813f2c3c49d2928b32e57c8d24d34453cea968954cc54882de3aa

Observation 6c4cd2ce-78b2-4a17-a86d-c31cafcbcdc1 · inbound

Video Parallel Scaling: Aggregating Diverse Frame Subsets for VideoLLMs cites this paper.

Video Parallel Scaling: Aggregating Diverse Frame Subsets for VideoLLMs FrameFusion: Combining Similarity and Importance for Video Token Reduction on Large Vision Language Models

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-18T18:36:44.182879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-18T18:35:01.328250Z digest=sha256:d46cb30e415ee68ef675474cec86ed770e285439dc006c0a5cfc2590246b3c91

Observation f2b099df-6088-4428-8865-d4a635427214 · inbound

Token Reduction via Local and Global Contexts Optimization for Efficient Video Large Language Models cites this paper.

Token Reduction via Local and Global Contexts Optimization for Efficient Video Large Language Models FrameFusion: Combining Similarity and Importance for Video Token Reduction on Large Vision Language Models

Reference 16

Resolution
verified exact
arxiv_id, observed 2026-05-15T18:26:26.952439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-15T18:25:21.621268Z digest=sha256:b9592bcb3dfe31ab96363670c4d01015c4a8b17988f9a2406836b44d57dbd439

Observation f1fb8254-03a9-498b-8f29-a67fafa8698c · 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 FrameFusion: Combining Similarity and Importance for Video Token Reduction on Large Vision Language Models

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-05-15T00:58:25.578048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-15T00:56:47.841355Z digest=sha256:ffea031612c38a49cac1e0b863211926e0759b9a37ed7be8ad927bd130a16a6a

Observation e751a35f-1465-4842-b509-70896a92690c · inbound

AdaSpark: Adaptive Sparsity for Efficient Long-Video Understanding cites this paper.

AdaSpark: Adaptive Sparsity for Efficient Long-Video Understanding FrameFusion: Combining Similarity and Importance for Video Token Reduction on Large Vision Language Models

Reference 12

Resolution
verified exact
arxiv_id, observed 2026-05-11T06:56:04.303398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-10T17:21:47.439019Z digest=sha256:c879aa1c32280c1dcf790d5f7d23178abd564302967bea4f36080b72ce32d652

Observation 283bf54e-4e36-456a-8fb0-281b24c26d04 · inbound

VLMaxxing through FrameMogging Training-Free Anti-Recomputation for Video Vision-Language Models cites this paper.

VLMaxxing through FrameMogging Training-Free Anti-Recomputation for Video Vision-Language Models FrameFusion: Combining Similarity and Importance for Video Token Reduction on Large Vision Language Models

Reference 12

Resolution
verified exact
arxiv_id, observed 2026-05-11T23:11:13.939144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-08T01:30:15.463051Z digest=sha256:b8726711895c969ed6dc98fcf0c4a317851a52545af04ad33cec98fa162d42cc

Observation d7ea209d-850e-41a8-a95d-ce6317404937 · inbound

OTT-Vid: Optimal Transport Temporal Token Compression for Video Large Language Models cites this paper.

OTT-Vid: Optimal Transport Temporal Token Compression for Video Large Language Models FrameFusion: Combining Similarity and Importance for Video Token Reduction on Large Vision Language Models

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-05-13T05:52:22.314857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-13T05:49:36.807884Z digest=sha256:32250db113fd108a1d33b28e600503b609f2188c4fbef4a2fe385a3254c07584

Observation e387f156-1e65-4a9d-a278-1fd740ff47d1 · inbound

OmniRefine: Alignment-Aware Cooperative Compression for Efficient Omnimodal Large Language Models cites this paper.

OmniRefine: Alignment-Aware Cooperative Compression for Efficient Omnimodal Large Language Models FrameFusion: Combining Similarity and Importance for Video Token Reduction on Large Vision Language Models

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-05-13T04:52:16.246111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-13T04:52:03.076788Z digest=sha256:e1b4bfd20c8978936027f33bae80cfa7277347910e45983bf423b9044ac92c4b