Papers by Nishan Srishankar
LAW: Legal Agentic Workflows for Custody and Fund Services Contracts (2025.coling-industry)
Copied to clipboard
William Watson, Nicole Cho, Nishan Srishankar, Zhen Zeng, Lucas Cecchi, Daniel Scott, Suchetha Siddagangappa, Rachneet Kaur, Tucker Balch, Manuela Veloso
| Challenge: | Currently, there are limited resources available to build a legal domain-specific Large Language Model (LLM) however, legal contracts are highly varied not only in terms of semantics but also accessibility. |
| Approach: | They propose a Large Language Model (LLM) that integrates multiple specialized agents and text agents to respond to user queries. |
| Outcome: | The proposed model outperforms the baseline model in complex tasks such as calculating a contract’s termination date by 92.9% points. |
AdaptAgent: Adapting Multimodal Web Agents with Few-Shot Learning from Human Demonstrations (2025.acl-long)
Copied to clipboard
| Challenge: | State-of-the-art multimodal web agents can perform many web tasks by processing user instructions and interacting with graphical user interfaces (GUIs). |
| Approach: | They propose to build multimodal web agents for few-shot adaptability using human demonstrations to improve their generalization and adaptability. |
| Outcome: | The proposed framework enables both proprietary and open-weights multimodal web agents to adapt to new websites and domains using few human demonstrations. |
ChartAgent: A Multimodal Agent for Visually Grounded Reasoning in Complex Chart Question Answering (2026.acl-long)
Copied to clipboard
| Challenge: | Recent multimodal LLMs have shown promise in chart-based visual question answering, but their performance declines sharply on unannotated charts. |
| Approach: | They propose a novel agentic framework that explicitly performs visual reasoning directly within the chart’s spatial domain. |
| Outcome: | The proposed framework achieves state-of-the-art accuracy on the ChartBench and ChartX benchmarks surpassing prior methods by up to 16.07% absolute gain overall and 17.31% on numerically intensive queries. |