Papers by Subhojit Som

2 papers
WebSTAR: Scalable Data Synthesis for Computer Use Agents with Step-Level Filtering (2026.acl-long)

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Challenge: Existing datasets rely on human demonstrations, limiting scalability.
Approach: They propose a scalable data synthesis pipeline that transforms noisy rollouts into reliable supervision without human annotation.
Outcome: The proposed pipeline transforms noisy rollouts into reliable supervision without human annotation.
DUBLIN: Visual Document Understanding By Language-Image Network (2023.emnlp-industry)

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Challenge: DUBLIN is a pixel-based visual document understanding model that does not rely on OCR.
Approach: They propose a pixel-based visual document understanding model that does not rely on OCR.
Outcome: The proposed model performs on extractive tasks such as DocVQA, InfoVQA and AI2D, and strong performance on abstraction datasets such as VisualMRC and text captioning.

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