Paper Citation Record · LEDGER

TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2312.16862.

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

pith.paper-citation-record.v1
2312.16862 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:23:18.721571Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-28T19:12:34.660205Z

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 f7d31332-9873-4d63-b4c5-57867f611a32 · inbound

MoE-LLaVA: Mixture of Experts for Large Vision-Language Models cites this paper.

MoE-LLaVA: Mixture of Experts for Large Vision-Language Models TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-16T02:33:30.198605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 97b20cd8-00d5-4aaf-92ac-9380db2105b9 · inbound

[CLS] Token Tells Everything Needed for Training-free Efficient MLLMs cites this paper.

[CLS] Token Tells Everything Needed for Training-free Efficient MLLMs TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-11T20:23:18.721571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2a20de0-3f71-458b-b134-5d1d6262bc07 · inbound

Enhancing Multimodal Large Language Models Complex Reason via Similarity Computation cites this paper.

Enhancing Multimodal Large Language Models Complex Reason via Similarity Computation TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-11T16:45:44.833743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6fed9b6c-9c23-4ad5-b1ee-45303176c4db · inbound

Eve: Efficient Multimodal Vision Language Models with Elastic Visual Experts cites this paper.

Eve: Efficient Multimodal Vision Language Models with Elastic Visual Experts TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-10T21:42:12.645932Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c9c4cca-bfce-4f33-91ac-e5c0250cd23e · inbound

Zero-shot Video Moment Retrieval via Off-the-shelf Multimodal Large Language Models cites this paper.

Zero-shot Video Moment Retrieval via Off-the-shelf Multimodal Large Language Models TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-10T20:34:12.356485Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c455f70b-8ff5-4f81-934d-8489ed68f59a · inbound

NanoVLMs: How small can we go and still make coherent Vision Language Models? cites this paper.

NanoVLMs: How small can we go and still make coherent Vision Language Models? TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-08T13:35:05.183485Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98136340-7b2c-42da-bf54-80601ce7418f · inbound

EvoMoE: Expert Evolution in Mixture of Experts for Multimodal Large Language Models cites this paper.

EvoMoE: Expert Evolution in Mixture of Experts for Multimodal Large Language Models TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T13:20:33.495378Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7842195-dade-46fb-95ff-89cf4b2f0476 · inbound

UHR-BAT: Budget-Aware Token Compression Vision-Language model for Ultra-High-Resolution Remote Sensing cites this paper.

UHR-BAT: Budget-Aware Token Compression Vision-Language model for Ultra-High-Resolution Remote Sensing TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-10T13:55:28.939019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7cc36ea4-aed9-4634-bb16-07ac1f6d2a7b · inbound

DREAM-S: Speculative Decoding with Searchable Drafting and Target-Aware Refinement for Multimodal Generation cites this paper.

DREAM-S: Speculative Decoding with Searchable Drafting and Target-Aware Refinement for Multimodal Generation TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones

Reference 38

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T19:02:34.648834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6c7d7e62-96b5-402b-9a14-a05512bc9d11 · inbound

LASER: Loss-Aware Singular-value Decomposition and Rank Allocation for Efficient Low-Precision Vision-Language Models cites this paper.

LASER: Loss-Aware Singular-value Decomposition and Rank Allocation for Efficient Low-Precision Vision-Language Models TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones

Reference 56

Resolution: verified exact; arxiv_id, observed 2026-06-28T19:12:34.661689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 626bdd31-d71c-440a-8817-6af15d1a960d · inbound

Look Less, Think Faster: Joint Token-Compute Adaptation for Multimodal LLMs cites this paper.

Look Less, Think Faster: Joint Token-Compute Adaptation for Multimodal LLMs TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-01T10:06:50.835461Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.