7 citations · 8 across the 4 of their papers we have counts for
4 papers
MCP-Cosmos: World Model-Augmented Agents for Complex Task Execution in MCP Environments
Giridhar Ganapavarapu, Dhaval Patel
The Model Context Protocol (MCP) has unified the interface between Large Language Models (LLMs) and external tools, yet a fundamental gap remains in how agents conceptualize the en…
SPIRAL: Symbolic LLM Planning via Grounded and Reflective Search
Yifan Zhang, Giridhar Ganapavarapu, Srideepika Jayaraman +3
Large Language Models (LLMs) often falter at complex planning tasks that require exploration and self-correction, as their linear reasoning process struggles to recover from early…
TsSHAP: Robust model agnostic feature-based explainability for time series forecasting
Vikas C. Raykar, Arindam Jati, Sumanta Mukherjee +4
A trustworthy machine learning model should be accurate as well as explainable. Understanding why a model makes a certain decision defines the notion of explainability. While vario…
AutoAI-TS: AutoAI for Time Series Forecasting
Syed Yousaf Shah, Dhaval Patel, Long Vu +10
A large number of time series forecasting models including traditional statistical models, machine learning models and more recently deep learning have been proposed in the literat…