4 citations · 5 across the 5 of their papers we have counts for
6 papers
VERDICT: Visual Extraction-Result Decoder with Integrated Confidence and Traceable Evidence
Harikrishnan P M, Goutham Vignesh, Siva Prasad +5
Enterprises run schema-guided structured field extraction on documents with frontier multimodal models, and accuracy keeps improving as new models and systems arrive. Two things ha…
Stop Thinking, Start Looking: Efficient Post-Training for Multimodal Document Question Answering via Reasoning-Free Alignment
Harikrishnan P M, Goutham Vignesh, Ganesh Parab +4
Efficient multimodal document question answering with explicit visual grounding, locating the precise document region that supports each answer remains an open challenge. Current a…
DocAnnot -- Accelerating the Creation of Key Information Extraction Datasets with GenAI-Powered Auto-annotation
Siddartha Reddy, Harikrishnan P M, Goutham Vignesh +2
Key Information Extraction (KIE) is vital for many document applications, but creating training datasets is traditionally a time-consuming manual process. We introduce DocAnnot, a…
Leveraging Latent Evolutionary Optimization for Targeted Molecule Generation
Siddartha Reddy N, Sai Prakash MV, Varun V +2
Lead optimization is a pivotal task in the drug design phase within the drug discovery lifecycle. The primary objective is to refine the lead compound to meet specific molecular pr…
Minimizing Factual Inconsistency and Hallucination in Large Language Models
Muneeswaran I, Shreya Saxena, Siva Prasad +5
Large Language Models (LLMs) are widely used in critical fields such as healthcare, education, and finance due to their remarkable proficiency in various language-related tasks. Ho…
Synergistic Fusion of Graph and Transformer Features for Enhanced Molecular Property Prediction
M V Sai Prakash, Siddartha Reddy N, Ganesh Parab +3
Molecular property prediction is a critical task in computational drug discovery. While recent advances in Graph Neural Networks (GNNs) and Transformers have shown to be effective…