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3yr ROI

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References (45)

[1]
InfiniteVL: Synergizing Linear and Sparse Attention for Highly-Efficient, Unlimited-Input Vision-Language Models
2025Hongyuan Tao, Bencheng Liao et al.
[2]
Luth: Efficient French Specialization for Small Language Models and Cross-Lingual Transfer
2025Maxence Lasbordes, Sinou'e Gad
[3]
Temporal Chain of Thought: Long-Video Understanding by Thinking in Frames
2025Anurag Arnab, Ahmet Iscen et al.
[4]
GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning
2025GLM-V Team Wenyi Hong, Wenmeng Yu et al.
[5]
SoLoPO: Unlocking Long-Context Capabilities in LLMs via Short-to-Long Preference Optimization
2025Huashan Sun, Shengyi Liao et al.
[6]
MMLongBench: Benchmarking Long-Context Vision-Language Models Effectively and Thoroughly
2025Zhaowei Wang, Wenhao Yu et al.
[7]
BOLT: Boost Large Vision-Language Model Without Training for Long-Form Video Understanding
2025Shuming Liu, Chen Zhao et al.
[8]
WildLong: Synthesizing Realistic Long-Context Instruction Data at Scale
2025Jiaxi Li, Xingxing Zhang et al.
[9]
LongPO: Long Context Self-Evolution of Large Language Models through Short-to-Long Preference Optimization
2025Guanzheng Chen, Xin Li et al.
[10]
Long-VITA: Scaling Large Multi-modal Models to 1 Million Tokens with Leading Short-Context Accuracy
2025Yunhang Shen, Chaoyou Fu et al.
[11]
The Best Instruction-Tuning Data are Those That Fit
2025Dylan Zhang, Qirun Dai et al.
[12]
NExtLong: Toward Effective Long-Context Training without Long Documents
2025Chaochen Gao, Xing Wu et al.
[13]
LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks
2024Yushi Bai, Shangqing Tu et al.
[14]
V2PE: Improving Multimodal Long-Context Capability of Vision-Language Models with Variable Visual Position Encoding
2024Junqi Ge, Ziyi Chen et al.
[15]
Self-Preference Bias in LLM-as-a-Judge
2024Koki Wataoka, Tsubasa Takahashi et al.
[16]
How to Train Long-Context Language Models (Effectively)
2024Tianyu Gao, Alexander Wettig et al.
[17]
HELMET: How to Evaluate Long-Context Language Models Effectively and Thoroughly
2024Howard Yen, Tianyu Gao et al.
[18]
MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark
2024Xiang Yue, Tianyu Zheng et al.
[19]
VLMEvalKit: An Open-Source ToolKit for Evaluating Large Multi-Modality Models
2024Haodong Duan, Junming Yang et al.
[20]
SPIQA: A Dataset for Multimodal Question Answering on Scientific Papers
2024Shraman Pramanick, R. Chellappa et al.

Showing 20 of 45 references

Founder's Pitch

"Build a high-performance API for visual document question answering with long-context capabilities."

document-understandingScore: 7View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

3/4 signals

7.5

Quick Build

4/4 signals

10

Series A Potential

3/4 signals

7.5

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Why It Matters

This research is crucial for improving document comprehension in machine learning models, especially for documents too long for traditional text-only language models. It bridges the gap between visual inputs, such as PDFs, and text processing, greatly enhancing tasks like question answering and summarization over extended documentation.

Product Angle

Leverage the open-source synthetic data pipelines and training recipes provided to rapidly develop a robust API or enterprise software that enables advanced document understanding and question answering using visual inputs.

Disruption

Could potentially replace less efficient text-to-text document analysis solutions that struggle with information loss due to format conversion, offering a more comprehensive solution that directly processes visual document formats like PDFs.

Product Opportunity

The market opportunity includes legal, academic, and corporate sectors needing efficient document processing solutions. Organizations are willing to pay for tools that enhance productivity by automatically extracting information from lengthy, complex documents.

Use Case Idea

Develop a cloud-based service for enterprises to automate processing and querying massive datasets of complex documents such as legal contracts, academic papers, or policy documents, improving workflow efficiency in document-heavy industries.

Science

The paper explores training large vision-language models that can handle long context lengths, using up to 344K context tokens. It employs continued pretraining, supervised finetuning, and preference optimization techniques to improve long-document visual question answering performance. The methodology involves extending known text-to-visual context transfer benefits to visual-to-text, showing training benefits across modalities.

Method & Eval

The model was evaluated using benchmarks like MMLongBenchDoc and MMLBD-C, achieving state-of-the-art results. The authors released datasets and checkpoints that outperform existing open-weight models in the context of long-document question answering.

Caveats

The approach may require extensive computational resources for training despite not being classified as training-at-scale. Also, the model's applicability might be limited without significant customization for specific document types or industries.

Author Intelligence

Austin Veselka

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austin.veselka@lighton.ai