9 citations · 14 across the 7 of their papers we have counts for
8 papers
ChartAgent: A Multimodal Agent for Visually Grounded Reasoning in Complex Chart Question Answering
Rachneet Kaur, Nishan Srishankar, Zhen Zeng +2
Recent multimodal LLMs have shown promise in chart-based visual question answering, but their performance declines sharply on unannotated charts-those requiring precise visual inte…
LAW: Legal Agentic Workflows for Custody and Fund Services Contracts
William Watson, Nicole Cho, Nishan Srishankar +7
Legal contracts in the custody and fund services domain govern critical aspects such as key provider responsibilities, fee schedules, and indemnification rights. However, it is cha…
AdaptAgent: Adapting Multimodal Web Agents with Few-Shot Learning from Human Demonstrations
Gaurav Verma, Rachneet Kaur, Nishan Srishankar +3
State-of-the-art multimodal web agents, powered by Multimodal Large Language Models (MLLMs), can autonomously execute many web tasks by processing user instructions and interacting…
FISHNET: Financial Intelligence from Sub-querying, Harmonizing, Neural-Conditioning, Expert Swarms, and Task Planning
Nicole Cho, Nishan Srishankar, Lucas Cecchi +1
Financial intelligence generation from vast data sources has typically relied on traditional methods of knowledge-graph construction or database engineering. Recently, fine-tuned f…
Is There No Such Thing as a Bad Question? H4R: HalluciBot For Ratiocination, Rewriting, Ranking, and Routing
William Watson, Nicole Cho, Nishan Srishankar
Hallucination continues to be one of the most critical challenges in the institutional adoption journey of Large Language Models (LLMs). While prior studies have primarily focused…
Sim2Real Docs: Domain Randomization for Documents in Natural Scenes using Ray-traced Rendering
Nikhil Maddikunta, Huijun Zhao, Sumit Keswani +5
In the past, computer vision systems for digitized documents could rely on systematically captured, high-quality scans. Today, transactions involving digital documents are more lik…
Grounded Relational Inference: Domain Knowledge Driven Explainable Autonomous Driving
Chen Tang, Nishan Srishankar, Sujitha Martin +1
Explainability is essential for autonomous vehicles and other robotics systems interacting with humans and other objects during operation. Humans need to understand and anticipate…
Flow-FL: Data-Driven Federated Learning for Spatio-Temporal Predictions in Multi-Robot Systems
Nathalie Majcherczyk, Nishan Srishankar, Carlo Pinciroli
In this paper, we show how the Federated Learning (FL) framework enables learning collectively from distributed data in connected robot teams. This framework typically works with c…