Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Multi-Agent VQA: Exploring Multi-Agent Foundation Models in Zero-Shot Visual Question Answering
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2403.14783.
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 2026-08-06T16:51:23.503836Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-13T07:42:30.502621Z
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 2d1d844b-fbb9-4eba-9dd2-e89947ffb9d8 · inbound
Describe Anything Model for Visual Question Answering on Text-rich Images cites this paper.
Describe Anything Model for Visual Question Answering on Text-rich Images Multi-Agent VQA: Exploring Multi-Agent Foundation Models in Zero-Shot Visual Question Answering
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T16:51:23.503836Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 28657e13-d92a-4280-8023-4dd015d03f6b · inbound
Dual Latent Memory for Visual Multi-agent System cites this paper.
Dual Latent Memory for Visual Multi-agent System Multi-Agent VQA: Exploring Multi-Agent Foundation Models in Zero-Shot Visual Question Answering
Reference 2019
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T06:04:51.556159Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 46985fed-e9b3-4416-b6fc-fbf857c63e41 · inbound
Learning to Communicate Locally for Large-Scale Multi-Agent Pathfinding cites this paper.
Learning to Communicate Locally for Large-Scale Multi-Agent Pathfinding Multi-Agent VQA: Exploring Multi-Agent Foundation Models in Zero-Shot Visual Question Answering
Reference 17
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T03:45:56.176479Z
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 4055044b-69b3-4130-b711-00b21bbf3c7e · inbound
Learning to Communicate Locally for Large-Scale Multi-Agent Pathfinding cites this paper.
Learning to Communicate Locally for Large-Scale Multi-Agent Pathfinding Multi-Agent VQA: Exploring Multi-Agent Foundation Models in Zero-Shot Visual Question Answering
Reference 17
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T07:42:30.505929Z
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.