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Shubham Agarwal

9 accepted papers

2026

BhashaKritika: Building Synthetic Pretraining Data at Scale for Indic Languages

AAAI 2026technical

In the context of pretraining of Large Language Models (LLMs), synthetic data has emerged as an alternative for generating high-quality pretraining data at scale. This is particularly beneficial in low resource language settings where the benefits of the recent LLMs have been unevenly distributed ac

Cited by 0SourcePDFScholar
2026

FlowCast: Trajectory Forecasting for Scalable Zero-Cost Speculative Flow Matching

ICLR 2026poster

Flow Matching (FM) has recently emerged as a powerful approach for high-quality visual generation. However, their prohibitively slow inference due to a large number of denoising steps limits their potential use in real-time or interactive applications. Existing acceleration methods, like distillatio…

Cited by 0SourceScholar
2026

IndicVisionBench: Benchmarking Cultural and Multilingual Understanding in VLMs

ICLR 2026poster

Vision-language models (VLMs) have demonstrated impressive generalization across multimodal tasks, yet most evaluation benchmarks remain Western-centric, leaving open questions about their performance in culturally diverse and multilingual settings. To address this gap, we introduce IndicVisionBench…

Cited by 0SourcecodeScholar
2025

BigDocs: An Open Dataset for Training Multimodal Models on Document and Code Tasks

ICLR 2025poster

Multimodal AI has the potential to significantly enhance document-understanding tasks, such as processing receipts, understanding workflows, extracting data from documents, and summarizing reports. Code generation tasks that require long-structured outputs can also be enhanced by multimodality. Desp…

Cited by 0SourcePDFScholar
2025

Chitrarth: Bridging Vision and Language for a Billion People

ICASSP 2025accepted

Recent multimodal foundation models are primarily trained on English or high resource European language data, which limits their applicability to other medium and low-resource languages, such as the Indian languages. To address this limitation, we introduce Chitrarth (Chitra: Image; Artha: Meaning),…

Cited by 12SourceScholar
2025

Root Cause Analysis of Failures from Partial Causal Structures

UAI 2025

Finding the root cause of failures is a prominent problem in many complex networks. Causal inference provides us with tools to address this problem algorithmically to automate this process and solve it efficiently. The existing methods either use a known causal structure to identify root cause by ba

2025

StarVector: Generating Scalable Vector Graphics Code from Images and Text

AAAI 2025technical

Scalable Vector Graphics (SVG) have become integral to modern image rendering applications due to their infinite scalability and versatility, especially in graphic design and web development. SVGs are essentially long strings of code that adhere to a structured syntax with validity constraints. With…

Cited by 2SourcePDFScholar
2025

StarVector: Generating Scalable Vector Graphics Code from Images and Text

CVPR 2025poster

Scalable Vector Graphics (SVGs) are vital for modern image rendering due to their scalability and versatility. Previous SVG generation methods have focused on curve-based vectorization, lacking semantic understanding, often producing artifacts, and struggling with SVG primitives beyond path curves.…

Cited by 7SourcePDFScholar
2024

ReCON: Training-Free Acceleration for Text-to-Image Synthesis with Retrieval of Concept Prompt Trajectories

ECCV 2024poster

"Text-to-image diffusion models excel in generating photo-realistic images but are hampered by slow processing times. Training-free retrieval-based acceleration methods, which leverage pre-generated “trajectories,” have been introduced to address this. Yet, these methods often lack diversity and fid…