Paper Citation Record · LEDGER

Mono-InternVL: Pushing the Boundaries of Monolithic Multimodal Large Language Models with Endogenous Visual Pre-training

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2410.08202.

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

pith.paper-citation-record.v1
2410.08202 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 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 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T14:25:55.793608Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-16T09:16:17.330970Z

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 9f39929b-a390-4545-91ca-7d44a6c219a5 · inbound

Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization cites this paper.

Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization Mono-InternVL: Pushing the Boundaries of Monolithic Multimodal Large Language Models with Endogenous Visual Pre-training

Reference 63

Resolution: verified exact; arxiv_id, observed 2026-05-16T09:16:17.333274Z

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 ea889e35-28b7-4e61-9495-e975b419072e · inbound

Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling cites this paper.

Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling Mono-InternVL: Pushing the Boundaries of Monolithic Multimodal Large Language Models with Endogenous Visual Pre-training

Reference 172

Resolution: verified exact; arxiv_id, observed 2026-05-10T13:23:57.806018Z

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 6ce369cc-c52d-4505-888e-59dff9b73e0e · inbound

EVEv2: Improved Baselines for Encoder-Free Vision-Language Models cites this paper.

EVEv2: Improved Baselines for Encoder-Free Vision-Language Models Mono-InternVL: Pushing the Boundaries of Monolithic Multimodal Large Language Models with Endogenous Visual Pre-training

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-08T14:25:55.793608Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13d25a94-f9e4-46f9-ac01-cb6142da07aa · inbound

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models cites this paper.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models Mono-InternVL: Pushing the Boundaries of Monolithic Multimodal Large Language Models with Endogenous Visual Pre-training

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T15:21:05.372302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ef13d95-c7ca-4dad-b000-c89cc7399d84 · inbound

Visual Embodied Brain: Let Multimodal Large Language Models See, Think, and Control in Spaces cites this paper.

Visual Embodied Brain: Let Multimodal Large Language Models See, Think, and Control in Spaces Mono-InternVL: Pushing the Boundaries of Monolithic Multimodal Large Language Models with Endogenous Visual Pre-training

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-07T12:16:13.209440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c10ab9b-7207-44a0-b710-b1ec91a7e84f · inbound

GOBench: Benchmarking Geometric Optics Generation and Understanding of MLLMs cites this paper.

GOBench: Benchmarking Geometric Optics Generation and Understanding of MLLMs Mono-InternVL: Pushing the Boundaries of Monolithic Multimodal Large Language Models with Endogenous Visual Pre-training

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T11:55:23.699416Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e05ec7b-f3b2-4b73-9f76-0d1c6efa92ff · inbound

SMAR: Soft Modality-Aware Routing Strategy for MoE-based Multimodal Large Language Models Preserving Language Capabilities cites this paper.

SMAR: Soft Modality-Aware Routing Strategy for MoE-based Multimodal Large Language Models Preserving Language Capabilities Mono-InternVL: Pushing the Boundaries of Monolithic Multimodal Large Language Models with Endogenous Visual Pre-training

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T06:07:18.718743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6c1c945-7804-4a3a-abcf-16ce18d90eec · inbound

Breaking Bad Molecules: Are MLLMs Ready for Structure-Level Molecular Detoxification? cites this paper.

Breaking Bad Molecules: Are MLLMs Ready for Structure-Level Molecular Detoxification? Mono-InternVL: Pushing the Boundaries of Monolithic Multimodal Large Language Models with Endogenous Visual Pre-training

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-07T04:17:59.681471Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a59232dd-a6e0-4986-affb-a787d45ad1d7 · inbound

LaVi: Efficient Large Vision-Language Models via Internal Feature Modulation cites this paper.

LaVi: Efficient Large Vision-Language Models via Internal Feature Modulation Mono-InternVL: Pushing the Boundaries of Monolithic Multimodal Large Language Models with Endogenous Visual Pre-training

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-06T23:42:13.784958Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0432910-0c1c-432f-b169-d2edd481baa3 · inbound

Language-Unlocked ViT (LUViT): Empowering Self-Supervised Vision Transformers with LLMs cites this paper.

Language-Unlocked ViT (LUViT): Empowering Self-Supervised Vision Transformers with LLMs Mono-InternVL: Pushing the Boundaries of Monolithic Multimodal Large Language Models with Endogenous Visual Pre-training

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T21:17:08.054862Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf96d5d1-f44a-4e86-b065-e6a114834a41 · inbound

MagicVL-2B: Empowering Vision-Language Models on Mobile Devices with Lightweight Visual Encoders via Curriculum Learning cites this paper.

MagicVL-2B: Empowering Vision-Language Models on Mobile Devices with Lightweight Visual Encoders via Curriculum Learning Mono-InternVL: Pushing the Boundaries of Monolithic Multimodal Large Language Models with Endogenous Visual Pre-training

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-06T05:36:19.867128Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd768d52-3dee-44e1-a7bf-da37ea51c617 · inbound

InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency cites this paper.

InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency Mono-InternVL: Pushing the Boundaries of Monolithic Multimodal Large Language Models with Endogenous Visual Pre-training

Reference 80

Resolution: verified exact; arxiv_id, observed 2026-05-10T11:58:59.207528Z

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.