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cs.AI2026

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…

cs.AI2024

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…

cs.AI2024

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…

cs.AI2024

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…

cs.AI2024

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…