Paper Citation Record · LEDGER

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design

As of 18 August 2026, this Paper Citation Record lists 92 of 92 outbound references and 11 inbound Pith citation observations for arXiv:2501.05901.

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

pith.paper-citation-record.v1
2501.05901 v2

Coverage vector

measured 92 of 92 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:10:19.297675Z

measured 103 of 103 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:46:40.770467Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T09:45:39.600613Z

Reference resolution

92 of 92 outbound references displayed

verified exact1
verified fuzzy26
unresolved64
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ab615871-4096-4225-9695-e5944592ab1c · outbound

This paper cites TallyQA: Answering Complex Counting Questions.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design TallyQA: Answering Complex Counting Questions

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:18.841141Z

Source-reported events for the cited work

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Observation b513fdf7-c303-4b95-a45f-7b65ffcb9363 · outbound

This paper cites GPT-4 Technical Report.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design GPT-4 Technical Report

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:18.847175Z

Source-reported events for the cited work

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Observation bb92b421-f6c2-4d8e-8ddc-7ed51b510a0c · outbound

This paper cites Math-llava: Bootstrapping mathematical reasoning for multimodal large language models.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Math-llava: Bootstrapping mathematical reasoning for multimodal large language models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:18.852430Z

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Observation 1c76ed4c-db84-4feb-96d8-59e7373d7fd5 · outbound

This paper cites Scene text visual question answering.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Scene text visual question answering

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:18.857075Z

Source-reported events for the cited work

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Observation 47a88cda-d9bb-4064-8d9e-08f2f87d76a3 · outbound

This paper cites Language models are few-shot learners.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Language models are few-shot learners

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:18.861346Z

Source-reported events for the cited work

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Observation 2cb0df00-067f-40a6-a5ae-22a1db441e83 · outbound

This paper cites Coyo-700m: Image-text pair dataset.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Coyo-700m: Image-text pair dataset

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:18.865508Z

Source-reported events for the cited work

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Observation 4025de86-5d9a-4f13-9045-c08470052d69 · outbound

This paper cites ALLaVA: Harnessing GPT4V-Synthesized Data for Lite Vision-Language Models.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design ALLaVA: Harnessing GPT4V-Synthesized Data for Lite Vision-Language Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:18.869750Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5838efec-a0a4-4e7a-ba76-66ff2d3f4d26 · outbound

This paper cites ShareGPT4V: Improving Large Multi-Modal Models with Better Captions.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design ShareGPT4V: Improving Large Multi-Modal Models with Better Captions

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:18.874649Z

Source-reported events for the cited work

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Observation 0718d30d-9be4-45d4-b632-f34df5dbe19a · outbound

This paper cites Are We on the Right Way for Evaluating Large Vision-Language Models?.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Are We on the Right Way for Evaluating Large Vision-Language Models?

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:18.878895Z

Source-reported events for the cited work

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Observation 07d3c61f-97c8-43a9-b894-6fb073d2041b · outbound

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

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:18.883573Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 676dc6fd-a459-41f3-8e90-34323d5397fa · outbound

This paper cites How far are we to gpt-4v? closing the gap to commercial multimodal models with open-source suites.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design How far are we to gpt-4v? closing the gap to commercial multimodal models with open-source suites

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:18.888636Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea6a8da3-dac3-42e3-bd6c-0278617b14c8 · outbound

This paper cites Opencompass: A universal evaluation platform for foundation models.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Opencompass: A universal evaluation platform for foundation models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:18.893598Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 62681333-18af-40fa-993f-ddc558937b75 · outbound

This paper cites Patch n’ pack: Navit, a vision transformer for any aspect ratio and resolution, 2023.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Patch n’ pack: Navit, a vision transformer for any aspect ratio and resolution, 2023

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:18.897609Z

Source-reported events for the cited work

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Observation 91e0312c-25d2-4e9b-a0f7-87227ae4c170 · outbound

This paper cites Insight-V: Exploring Long-Chain Visual Reasoning with Multimodal Large Language Models.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Insight-V: Exploring Long-Chain Visual Reasoning with Multimodal Large Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:18.901451Z

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Observation 80de881f-3042-42e5-b01f-be3b710af1ef · outbound

This paper cites Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:18.905756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c899680f-fee7-4c7d-b750-e7abf6e1a6df · outbound

This paper cites Making the v in vqa matter: Elevating the role of image understanding in visual question answering.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Making the v in vqa matter: Elevating the role of image understanding in visual question answering

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:18.910490Z

Source-reported events for the cited work

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Observation c03a9b22-2881-4bda-b7ae-883bbcd2a9d4 · outbound

This paper cites Infinity-MM: Scaling Multimodal Performance with Large-Scale and High-Quality Instruction Data.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Infinity-MM: Scaling Multimodal Performance with Large-Scale and High-Quality Instruction Data

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:18.919339Z

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Unavailable: canonical work link unavailable.

Observation d0c2b559-7bc1-484e-828f-9242f9ac77a8 · outbound

This paper cites Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:18.923630Z

Source-reported events for the cited work

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Observation 1462fed2-711d-4345-b0c5-c3e3e9323149 · outbound

This paper cites Hallusion- bench: An advanced diagnostic suite for entangled language hallucination and visual illusion in large vision-language models.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Hallusion- bench: An advanced diagnostic suite for entangled language hallucination and visual illusion in large vision-language models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:18.927686Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef12b89d-8900-4283-9f0e-12db7ea473fd · outbound

This paper cites Llava-uhd: an lmm perceiving any aspect ratio and high- resolution images.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Llava-uhd: an lmm perceiving any aspect ratio and high- resolution images

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:18.931724Z

Source-reported events for the cited work

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Observation 556e9947-1be6-4c92-84f4-4eb8b01a1400 · outbound

This paper cites Vizwiz grand challenge: Answering visual questions from blind people.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Vizwiz grand challenge: Answering visual questions from blind people

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:18.936887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1e5d2a2-3bff-4857-900b-05d99ab4b647 · outbound

This paper cites MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:18.941965Z

Source-reported events for the cited work

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Observation bbfef737-4be6-48b7-aa95-f7505d2cda4a · outbound

This paper cites an unresolved cited work.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Unresolved cited work

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:18.946148Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4cc1d556-df5e-4abc-b41f-386a1af80e37 · outbound

This paper cites Clevr: A diagnostic dataset for compositional language and elementary visual reasoning.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Clevr: A diagnostic dataset for compositional language and elementary visual reasoning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:10:20.525148Z

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Observation 2b36f082-0491-46cc-8bf8-dd42e72110e0 · outbound

This paper cites A diagram is worth a dozen images.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design A diagram is worth a dozen images

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:10:20.512612Z

Source-reported events for the cited work

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Observation 97bdcc57-f5c3-4628-896f-39c3de00308c · outbound

This paper cites The hateful memes challenge: Detecting hate speech in multimodal memes.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design The hateful memes challenge: Detecting hate speech in multimodal memes

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:10:20.500901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5a668c09-d27c-4093-bd22-12d86d265cb8 · outbound

This paper cites an unresolved cited work.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Unresolved cited work

Reference 28

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Source-reported events for the cited work

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Observation ed5e63c8-1221-44fd-9269-ba4a2db78de3 · outbound

This paper cites Shamma, Michael S.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Shamma, Michael S

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:10:20.478120Z

Source-reported events for the cited work

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Observation 5e680620-55fa-4e52-a03c-cf52f6ab3c64 · outbound

This paper cites Vqa-rad: Visual question answering dataset for radiology.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Vqa-rad: Visual question answering dataset for radiology

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:10:20.464301Z

Source-reported events for the cited work

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Observation a06cd701-f672-4ff1-82ef-f0dc6354bcfc · outbound

This paper cites Super-clevr: A virtual benchmark to diagnose domain robustness in visual reasoning.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Super-clevr: A virtual benchmark to diagnose domain robustness in visual reasoning

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:10:20.451654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 279fc10f-a3e4-4be4-8b7f-5214dc8d0fa3 · outbound

This paper cites Microsoft coco: Common objects in context.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Microsoft coco: Common objects in context

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:10:20.438500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1f34d0e7-f572-4820-a25a-e8b8903f9628 · outbound

This paper cites CLEVR-Math: A Dataset for Compositional Language, Visual and Mathematical Reasoning.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design CLEVR-Math: A Dataset for Compositional Language, Visual and Mathematical Reasoning

Reference 34

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Source-reported events for the cited work

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Observation b87fee95-0856-495a-8e22-3c68b60d4298 · outbound

This paper cites Visual Spatial Reasoning.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Visual Spatial Reasoning

Reference 35

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Source-reported events for the cited work

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Observation 7fdb3330-8930-43c6-945b-ef2c112488e4 · outbound

This paper cites LLaVA-OneVision: Easy Visual Task Transfer.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design LLaVA-OneVision: Easy Visual Task Transfer

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:19.001752Z

Source-reported events for the cited work

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Observation 43688fbe-a495-494e-b536-0252f7f4a1ea · outbound

This paper cites Visual Instruction Tuning.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Visual Instruction Tuning

Reference 38

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Source-reported events for the cited work

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Observation 74c3b448-56bd-4900-a086-dbf51ec653b1 · outbound

This paper cites Llava-next: Improved reasoning, ocr, and world knowledge, January 2024.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Llava-next: Improved reasoning, ocr, and world knowledge, January 2024

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:19.015439Z

Source-reported events for the cited work

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Observation c45c1d61-fafc-4bf1-a8d3-e6399630810f · outbound

This paper cites Visual instruction tuning.Advances in neural information processing systems , 36, 2024.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Visual instruction tuning.Advances in neural information processing systems , 36, 2024

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:10:20.419011Z

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Observation e4b6a11d-b402-4600-874f-fca0a5ed8fbf · outbound

This paper cites Optimal Transmitter Design and Pilot Spacing in MIMO Non-Stationary Aging Channels.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Optimal Transmitter Design and Pilot Spacing in MIMO Non-Stationary Aging Channels

Reference 41

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Source-reported events for the cited work

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Observation 8ddac59e-487a-4567-8482-dfe89c57a8dd · outbound

This paper cites Kangaroo: A Powerful Video-Language Model Supporting Long-context Video Input.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Kangaroo: A Powerful Video-Language Model Supporting Long-context Video Input

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:19.028341Z

Source-reported events for the cited work

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Observation d6fcd753-8a4f-4a5f-ad9b-50ec0fa983de · outbound

This paper cites POINTS1.5: Building a Vision-Language Model towards Real World Applications.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design POINTS1.5: Building a Vision-Language Model towards Real World Applications

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:19.033317Z

Source-reported events for the cited work

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Observation cea65a07-8ace-4636-8444-dec67b24ff45 · outbound

This paper cites Mmbench: Is your multi-modal model an all-around player? In European conference on computer vision, pages 216–233.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Mmbench: Is your multi-modal model an all-around player? In European conference on computer vision, pages 216–233

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:19.038904Z

Source-reported events for the cited work

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Observation fbd25527-43aa-4167-a237-e0ec800df1cd · outbound

This paper cites Ocrbench: on the hidden mystery of ocr in large multimodal models.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Ocrbench: on the hidden mystery of ocr in large multimodal models

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:19.045966Z

Source-reported events for the cited work

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Observation 426dea57-00aa-48a3-98fc-486b2eb1b324 · outbound

This paper cites an unresolved cited work.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Unresolved cited work

Reference 46

Resolution: unresolved; raw_fallback, observed 2026-08-10T21:10:20.398456Z

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Observation 51954eab-9ef7-4b15-87e6-d813b9c6c99d · outbound

This paper cites Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision, Language, Audio, and Action.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision, Language, Audio, and Action

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:19.060895Z

Source-reported events for the cited work

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Observation 1a99e5ca-649b-463b-a91a-dee72ac159ea · outbound

This paper cites An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:19.065456Z

Source-reported events for the cited work

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Observation 602a996e-fd75-43f3-ae7e-7f3003a15456 · outbound

This paper cites Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:19.071219Z

Source-reported events for the cited work

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Observation 5afb215e-1594-4a62-a25a-5249612787c4 · outbound

This paper cites Scienceqa: A challenging dataset for multi-modal reasoning.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Scienceqa: A challenging dataset for multi-modal reasoning

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:10:20.387378Z

Source-reported events for the cited work

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Observation 9b315588-3a12-4fba-b859-fd49e30458d6 · outbound

This paper cites Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:19.080916Z

Source-reported events for the cited work

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Observation faf8283a-7e18-46ca-a27c-1f8a2aeb734b · outbound

This paper cites Ovis: Structural Embedding Alignment for Multimodal Large Language Model.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Ovis: Structural Embedding Alignment for Multimodal Large Language Model

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:19.086305Z

Source-reported events for the cited work

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Observation df0713eb-5b23-46cd-b0e5-07f2bdd4e536 · outbound

This paper cites BlueLM-V-3B: Algorithm and System Co-Design for Multimodal Large Language Models on Mobile Devices.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design BlueLM-V-3B: Algorithm and System Co-Design for Multimodal Large Language Models on Mobile Devices

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:19.092613Z

Source-reported events for the cited work

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Observation 2c3bb458-c345-4ce0-8ed2-259c8fcd3909 · outbound

This paper cites Valley: Video Assistant with Large Language model Enhanced abilitY.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Valley: Video Assistant with Large Language model Enhanced abilitY

Reference 54

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Source-reported events for the cited work

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Observation 1eab67f3-c535-4a24-865d-b4530561e025 · outbound

This paper cites Ok-vqa: A benchmark for visual question answering using external knowledge.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Ok-vqa: A benchmark for visual question answering using external knowledge

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:10:20.358350Z

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Observation 353656bd-fe9b-4c6f-92e1-1b81dbff3304 · outbound

This paper cites Ok-vqa: A benchmark for visual question answering using external knowledge.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Ok-vqa: A benchmark for visual question answering using external knowledge

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:10:20.340532Z

Source-reported events for the cited work

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Observation adf12f67-ea75-4759-a1a2-07e94e91a30d · outbound

This paper cites Marti and H.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Marti and H

Reference 57

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Observation 44b7547d-f58e-47a4-a323-65634818ad0d · outbound

This paper cites ChartQA: A benchmark for question answering about charts with visual and logical reasoning.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design ChartQA: A benchmark for question answering about charts with visual and logical reasoning

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:10:20.311658Z

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Observation 3d2831b3-79aa-4907-bb89-0af2b5b1282f · outbound

This paper cites Mishra, K.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Mishra, K

Reference 59

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Observation 6346ee5f-5605-4fa1-890f-aaa2178c0369 · outbound

This paper cites Icdar 2019 crohme + tfd: Competition on recognition of handwritten mathematical expressions and typeset formula detection.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Icdar 2019 crohme + tfd: Competition on recognition of handwritten mathematical expressions and typeset formula detection

Reference 60

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This paper cites Training language models to follow instructions with human feedback.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Training language models to follow instructions with human feedback

Reference 61

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This paper cites Image Textualization: An Automatic Framework for Creating Accurate and Detailed Image Descriptions.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Image Textualization: An Automatic Framework for Creating Accurate and Detailed Image Descriptions

Reference 62

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This paper cites Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models

Reference 63

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This paper cites Improving language understanding by generative pre-training.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Improving language understanding by generative pre-training

Reference 64

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Reference 65

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Observation 8c1421ce-8e09-4402-ad0f-3d0f91432404 · outbound

This paper cites Eagle: Exploring The Design Space for Multimodal LLMs with Mixture of Encoders.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Eagle: Exploring The Design Space for Multimodal LLMs with Mixture of Encoders

Reference 66

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This paper cites Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:10:20.228777Z

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Observation ec2e600a-074d-4f2f-bf64-3a83eefd9069 · outbound

This paper cites TextCaps: a Dataset for Image Captioning with Reading Comprehension.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design TextCaps: a Dataset for Image Captioning with Reading Comprehension

Reference 68

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Observation 0e8077b3-2988-41c0-b4e8-8e1c72a91eb9 · outbound

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Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Singh, V

Reference 69

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This paper cites Textocr: Towards large-scale end-to-end reasoning for arbitrary-shaped scene text.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Textocr: Towards large-scale end-to-end reasoning for arbitrary-shaped scene text

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:10:20.204075Z

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Observation ca5a63c9-7208-4835-95c2-001239932aa2 · outbound

This paper cites Generative multimodal models are in-context learners.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Generative multimodal models are in-context learners

Reference 71

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Observation ca662c2d-b219-4774-af37-dde5ac2468f8 · outbound

This paper cites Bluelm: An open multilingual 7b language model.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Bluelm: An open multilingual 7b language model

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:10:20.182778Z

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Observation 73800cdf-9925-4ec7-a495-d7b0270492c1 · outbound

This paper cites G-llava: Solving geometric problem with multi-modal large language model.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design G-llava: Solving geometric problem with multi-modal large language model

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:10:20.170288Z

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Observation 88552b8c-acd1-4b67-955c-0707907b8ffa · outbound

This paper cites Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:19.197628Z

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Observation a0252062-f4ca-48f6-b1a4-fecbe6d33d66 · outbound

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

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:19.202065Z

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Observation 57530a53-239c-4ace-8891-15428ad7a436 · outbound

This paper cites LLaVA-CoT: Let Vision Language Models Reason Step-by-Step.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design LLaVA-CoT: Let Vision Language Models Reason Step-by-Step

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:19.208549Z

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Observation 97af1f60-fa2e-47f0-9ea1-036141f5622b · outbound

This paper cites Vision-Flan: Scaling Human-Labeled Tasks in Visual Instruction Tuning.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Vision-Flan: Scaling Human-Labeled Tasks in Visual Instruction Tuning

Reference 77

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Observation 7c2defd8-7458-410c-a61a-b57c4d6dd806 · outbound

This paper cites xgen-mm(blip-3): A family of open large multimodal models.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design xgen-mm(blip-3): A family of open large multimodal models

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:10:20.155096Z

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Observation e3d56420-b439-47f2-a7a7-6d73f7f31fff · outbound

This paper cites Qwen2.5 Technical Report.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Qwen2.5 Technical Report

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:19.224500Z

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Observation de5346b8-f4ac-4498-8ad4-89cf841e88fb · outbound

This paper cites MiniCPM-V: A GPT-4V Level MLLM on Your Phone.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design MiniCPM-V: A GPT-4V Level MLLM on Your Phone

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:19.228951Z

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Observation c9fdfd70-3e8f-4ca7-8e13-458658167b38 · outbound

This paper cites Modeling context in referring expressions.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Modeling context in referring expressions

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:10:20.142272Z

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Observation 1806a7ff-388d-4c4b-9707-0687db3b66f6 · outbound

This paper cites Mm-vet: Evaluating large multimodal models for integrated capabilities.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Mm-vet: Evaluating large multimodal models for integrated capabilities

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:19.237553Z

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Observation 343f21ad-1e5f-4a60-b8f4-2c44f6368eac · outbound

This paper cites Syntax-aware network for handwritten mathematical expression recognition.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Syntax-aware network for handwritten mathematical expression recognition

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:10:20.121985Z

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Observation 267b1168-795c-44e7-954c-d4d5fe374a13 · outbound

This paper cites Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi

Reference 84

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Unavailable: canonical work link unavailable.

Observation a3d01ea9-d201-4c7d-982f-76d57e889848 · outbound

This paper cites Sigmoid loss for language image pre-training, 2023.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Sigmoid loss for language image pre-training, 2023

Reference 85

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Observation c2886988-a028-45f1-b81f-d218cc139a4b · outbound

This paper cites Raven: A dataset for relational and analogical visual reasoning.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Raven: A dataset for relational and analogical visual reasoning

Reference 86

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Observation 168265e7-b8c0-42a8-a794-7549f0767f37 · outbound

This paper cites Unimath: A foundational and multimodal mathematical reasoner.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Unimath: A foundational and multimodal mathematical reasoner

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:10:20.076146Z

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Observation bdcd38c5-f79f-4057-a19b-7d0a72bfd90a · outbound

This paper cites InfinityMATH: A Scalable Instruction Tuning Dataset in Programmatic Mathematical Reasoning.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design InfinityMATH: A Scalable Instruction Tuning Dataset in Programmatic Mathematical Reasoning

Reference 88

Resolution: verified exact; local_arxiv, observed 2026-08-10T21:10:19.427271Z

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Observation f81bbf74-28f4-490b-832b-344e24487c5f · outbound

This paper cites Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding

Reference 89

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Observation 6e36f441-a28e-497c-9b40-36cd2d2faff2 · outbound

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Valley2: Exploring Multimodal Models with Scalable Vision-Language Design MAVIS: Mathematical Visual Instruction Tuning with an Automatic Data Engine

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:19.283464Z

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Observation 73c457fe-6a3c-4da4-a64d-2612acffd3cb · outbound

This paper cites Improve Vision Language Model Chain-of-thought Reasoning.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Improve Vision Language Model Chain-of-thought Reasoning

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-10T21:10:19.288710Z

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Observation 0489dc65-fdfd-472b-9299-b388e7b33607 · outbound

This paper cites Multimodal C4: An Open, Billion-scale Corpus of Images Interleaved with Text.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Multimodal C4: An Open, Billion-scale Corpus of Images Interleaved with Text

Reference 92

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Valley2: Exploring Multimodal Models with Scalable Vision-Language Design Visual7w: Grounded question answering in images

Reference 93

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Observation ce148f0c-2014-4bcd-ac11-1879993774e0 · outbound

This paper cites doi: 10.18653/v1/2022.findings-acl.177.

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design doi: 10.18653/v1/2022.findings-acl.177

Reference 2022

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Observation a06ac0d2-14e8-4317-87d0-8bd78ffc564d · outbound

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Reference 2023

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Pith citing papers

Observation e1190d8e-f3dd-4f60-b6ae-010722314397 · inbound

VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding cites this paper.

VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding Valley2: Exploring Multimodal Models with Scalable Vision-Language Design

Reference 155

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Observation 5a16af12-89a8-4a63-b999-feeecd9dd16e · inbound

Video-MMLU: A Massive Multi-Discipline Lecture Understanding Benchmark cites this paper.

Video-MMLU: A Massive Multi-Discipline Lecture Understanding Benchmark Valley2: Exploring Multimodal Models with Scalable Vision-Language Design

Reference 136

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Observation 8ea8ea45-ee51-4ca6-b8c8-5614187626ea · inbound

Reinforced MLLM: A Survey on RL-Based Reasoning in Multimodal Large Language Models cites this paper.

Reinforced MLLM: A Survey on RL-Based Reasoning in Multimodal Large Language Models Valley2: Exploring Multimodal Models with Scalable Vision-Language Design

Reference 116

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R1-ShareVL: Incentivizing Reasoning Capability of Multimodal Large Language Models via Share-GRPO Valley2: Exploring Multimodal Models with Scalable Vision-Language Design

Reference 16

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Observation f7ffcbb5-699a-45e6-a7b4-69e78ef5f9e6 · inbound

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GThinker: Towards General Multimodal Reasoning via Cue-Guided Rethinking Valley2: Exploring Multimodal Models with Scalable Vision-Language Design

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T11:55:14.123082Z

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VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? Valley2: Exploring Multimodal Models with Scalable Vision-Language Design

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:34.362773Z

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Observation baa7f74c-5f0a-411f-8950-4950baa3dbff · inbound

Interpretable Open-Vocabulary Referring Object Detection with Reverse Contrast Attention cites this paper.

Interpretable Open-Vocabulary Referring Object Detection with Reverse Contrast Attention Valley2: Exploring Multimodal Models with Scalable Vision-Language Design

Reference 27

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Observation a8d4a86e-227e-4eb6-b32a-43cb1ac76d0b · inbound

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R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model? Valley2: Exploring Multimodal Models with Scalable Vision-Language Design

Reference 27

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Observation 81c2255b-4e49-474f-8c34-793f6145de02 · inbound

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Valley3: Scaling Omni Foundation Models for E-commerce Valley2: Exploring Multimodal Models with Scalable Vision-Language Design

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-05-11T16:51:06.067123Z

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Observation 8097c691-8871-4976-a9d6-5017b52998cc · inbound

Learning to Deny: Action Denial in Multimodal Large Language Models cites this paper.

Learning to Deny: Action Denial in Multimodal Large Language Models Valley2: Exploring Multimodal Models with Scalable Vision-Language Design

Reference 75

Resolution: verified exact; arxiv_id, observed 2026-07-01T09:45:39.602043Z

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Observation de51de91-1470-4da0-9212-b98ff718d6b0 · inbound

VADER: Adaptive Debiasing for Hallucination Mitigation in Video Large Language Models cites this paper.

VADER: Adaptive Debiasing for Hallucination Mitigation in Video Large Language Models Valley2: Exploring Multimodal Models with Scalable Vision-Language Design

Reference 118

Resolution: unresolved; no resolver link, observed 2026-08-14T04:35:49.025460Z

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Unavailable: canonical work link unavailable.