Paper Citation Record · LEDGER

AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2309.16058.

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

pith.paper-citation-record.v1
2309.16058 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T10:50:59.410275Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T14:38:21.854307Z

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 7fbeb5c6-9225-47ca-b367-543246347823 · inbound

A Survey on Multimodal Large Language Models cites this paper.

A Survey on Multimodal Large Language Models AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model

Reference 32

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T02:56:42.375485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0b227414-42b3-439a-942d-912492fda826 · inbound

SensorChat: Answering Qualitative and Quantitative Questions during Long-Term Multimodal Sensor Interactions cites this paper.

SensorChat: Answering Qualitative and Quantitative Questions during Long-Term Multimodal Sensor Interactions AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-09T10:50:59.410275Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91648bd9-2afd-44d2-9dbd-0626ebffc624 · inbound

Multilingual and Multimodal LLMs in the Wild: Building for Low-Resource Languages cites this paper.

Multilingual and Multimodal LLMs in the Wild: Building for Low-Resource Languages AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-20T14:38:21.856002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 27a9bfa8-03fa-4d2f-85cb-36ff6857e15c · inbound

QLPO: Quadrant-weighted Sampling for Length-aware Policy Optimization cites this paper.

QLPO: Quadrant-weighted Sampling for Length-aware Policy Optimization AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model

Reference 199

Resolution: unresolved; no resolver link, observed 2026-08-01T06:48:44.836809Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.