Paper Citation Record · LEDGER

Vamba: Understanding Hour-Long Videos with Hybrid Mamba-Transformers

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2503.11579.

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

pith.paper-citation-record.v1
2503.11579 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:32:28.160847Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T14:28:32.156461Z

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 deb1afbc-0833-454f-b69e-147638220818 · inbound

IV-Bench: A Benchmark for Image-Grounded Video Perception and Reasoning in Multimodal LLMs cites this paper.

IV-Bench: A Benchmark for Image-Grounded Video Perception and Reasoning in Multimodal LLMs Vamba: Understanding Hour-Long Videos with Hybrid Mamba-Transformers

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-16T11:32:28.160847Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08eef1c9-d3c6-4b8e-a355-ced9c4eaa905 · inbound

VideoEval-Pro: Robust and Realistic Long Video Understanding Evaluation cites this paper.

VideoEval-Pro: Robust and Realistic Long Video Understanding Evaluation Vamba: Understanding Hour-Long Videos with Hybrid Mamba-Transformers

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T15:34:41.182215Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b12d4599-5ba5-4932-903e-1b42254041f7 · inbound

REVISOR: Beyond Textual Reflection, Towards Multimodal Introspective Reasoning in Long-Form Video Understanding cites this paper.

REVISOR: Beyond Textual Reflection, Towards Multimodal Introspective Reasoning in Long-Form Video Understanding Vamba: Understanding Hour-Long Videos with Hybrid Mamba-Transformers

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-17T22:20:22.892543Z

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 14a6485b-564d-4afb-a1c2-1d18eeda970b · inbound

Graph-to-Frame RAG: Visual-Space Knowledge Fusion for Training-Free and Auditable Video Reasoning cites this paper.

Graph-to-Frame RAG: Visual-Space Knowledge Fusion for Training-Free and Auditable Video Reasoning Vamba: Understanding Hour-Long Videos with Hybrid Mamba-Transformers

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:30:52.742612Z

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 2e145ce6-9302-4243-aee6-a100dba3acb9 · inbound

OProver: A Unified Framework for Agentic Formal Theorem Proving cites this paper.

OProver: A Unified Framework for Agentic Formal Theorem Proving Vamba: Understanding Hour-Long Videos with Hybrid Mamba-Transformers

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-20T14:48:23.515186Z

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 82500cf9-6d87-46ea-993f-ca5803db17f5 · inbound

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers cites this paper.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Vamba: Understanding Hour-Long Videos with Hybrid Mamba-Transformers

Reference 254

Resolution: verified exact; arxiv_id, observed 2026-07-03T14:28:32.157973Z

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.