36 citations · 247 across the 59 of their papers we have counts for
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cs.AI2024
TaskGen: A Task-Based, Memory-Infused Agentic Framework using StrictJSON
John Chong Min Tan, Prince Saroj, Bharat Runwal +6
TaskGen is an open-sourced agentic framework which uses an Agent to solve an arbitrary task by breaking them down into subtasks. Each subtask is mapped to an Equipped Function or a…
cs.AI2023★ 3 cited
Large Language Model (LLM) as a System of Multiple Expert Agents: An Approach to solve the Abstraction and Reasoning Corpus (ARC) Challenge
John Chong Min Tan, Mehul Motani
We attempt to solve the Abstraction and Reasoning Corpus (ARC) Challenge using Large Language Models (LLMs) as a system of multiple expert agents. Using the flexibility of LLMs to…
cs.AI2023
Learning, Fast and Slow: A Goal-Directed Memory-Based Approach for Dynamic Environments
John Chong Min Tan, Mehul Motani
Model-based next state prediction and state value prediction are slow to converge. To address these challenges, we do the following: i) Instead of a neural network, we do model-bas…