Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-05T14:41:15.586140Z
Paper Citation Record · LEDGER
MMG-Vid: Maximizing Marginal Gains at Segment-level and Token-level for Efficient Video LLMs
As of 7 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 2 inbound Pith citation observations for arXiv:2508.21044.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-13T17:05:52.493660Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-13T17:08:00.954550Z
Reference resolution
12 of 12 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved12
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation f29c4805-84c0-4a34-92c7-6508dc49d3ec · outbound
This paper cites PruneVid: Visual Token Pruning for Efficient Video Large Language Models.
MMG-Vid: Maximizing Marginal Gains at Segment-level and Token-level for Efficient Video LLMs PruneVid: Visual Token Pruning for Efficient Video Large Language Models
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T14:41:15.535604Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ce4e96b2-8cef-4572-8cf7-50b4b1ef4c25 · outbound
This paper cites In European Conference on Computer Vision, 323–340.
MMG-Vid: Maximizing Marginal Gains at Segment-level and Token-level for Efficient Video LLMs In European Conference on Computer Vision, 323–340
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T14:41:15.541099Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cbfd5e3d-863a-4930-afec-362e3a42d90c · outbound
This paper cites arXiv preprint arXiv:2403.15388.
MMG-Vid: Maximizing Marginal Gains at Segment-level and Token-level for Efficient Video LLMs arXiv preprint arXiv:2403.15388
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T14:41:15.547335Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1fb9ee88-8002-412b-98db-60ff0724ad84 · outbound
This paper cites arXiv preprint arXiv:2505.21334.
MMG-Vid: Maximizing Marginal Gains at Segment-level and Token-level for Efficient Video LLMs arXiv preprint arXiv:2505.21334
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T14:41:15.552875Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e844714e-1986-46ad-abc5-1084ac886157 · outbound
This paper cites arXiv preprint arXiv:2503.11187.
MMG-Vid: Maximizing Marginal Gains at Segment-level and Token-level for Efficient Video LLMs arXiv preprint arXiv:2503.11187
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T14:41:15.559338Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6e288fbc-89bf-4447-b45b-1edfde484ffc · outbound
This paper cites LLaVA-Scissor: Token Compression with Semantic Connected Components for Video LLMs.
MMG-Vid: Maximizing Marginal Gains at Segment-level and Token-level for Efficient Video LLMs LLaVA-Scissor: Token Compression with Semantic Connected Components for Video LLMs
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T14:41:15.565494Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ea89fa3a-6c78-4761-b489-6d1bd969cae8 · outbound
This paper cites Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution.
MMG-Vid: Maximizing Marginal Gains at Segment-level and Token-level for Efficient Video LLMs Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T14:41:15.570938Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e26d286f-8408-4b14-bce5-80b80213c53a · outbound
This paper cites PyramidDrop: Accelerating Your Large Vision-Language Models via Pyramid Visual Redundancy Reduction.
MMG-Vid: Maximizing Marginal Gains at Segment-level and Token-level for Efficient Video LLMs PyramidDrop: Accelerating Your Large Vision-Language Models via Pyramid Visual Redundancy Reduction
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T14:41:15.575985Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f5871a72-838b-4606-a26c-86655c41fd12 · outbound
This paper cites VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding.
MMG-Vid: Maximizing Marginal Gains at Segment-level and Token-level for Efficient Video LLMs VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T14:41:15.580800Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3852654f-126a-4d63-bc82-d74c946be87f · outbound
This paper cites MLVU: Benchmarking Multi-task Long Video Understanding.
MMG-Vid: Maximizing Marginal Gains at Segment-level and Token-level for Efficient Video LLMs MLVU: Benchmarking Multi-task Long Video Understanding
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T14:41:15.586140Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5901147d-987a-471d-a87d-180b646201cc · outbound
This paper cites arXiv preprint arXiv:2411.17686.
MMG-Vid: Maximizing Marginal Gains at Segment-level and Token-level for Efficient Video LLMs arXiv preprint arXiv:2411.17686
Reference 2024
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T14:41:15.530096Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 09ded1e6-3c85-45f7-881a-d26649f03049 · outbound
This paper cites LongVILA: Scaling Long-Context Visual Language Models for Long Videos.
MMG-Vid: Maximizing Marginal Gains at Segment-level and Token-level for Efficient Video LLMs LongVILA: Scaling Long-Context Visual Language Models for Long Videos
Reference 2025
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T14:41:15.525180Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
Observation 29f1ba24-1c86-4ce9-8209-8e21e967bea8 · inbound
DINO-VO: Learning Where to Focus for Enhanced State Estimation cites this paper.
DINO-VO: Learning Where to Focus for Enhanced State Estimation MMG-Vid: Maximizing Marginal Gains at Segment-level and Token-level for Efficient Video LLMs
Reference 42
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T17:08:00.956426Z
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.
Observation d356d0ca-6110-45a7-ab72-1ed6e6e22b76 · inbound
Sink-Token-Aware Pruning for Fine-Grained Video Understanding in Efficient Video LLMs cites this paper.
Sink-Token-Aware Pruning for Fine-Grained Video Understanding in Efficient Video LLMs MMG-Vid: Maximizing Marginal Gains at Segment-level and Token-level for Efficient Video LLMs
Reference 28
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-10T00:49:48.956060Z
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.