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TaskGen: A Task-Based, Memory-Infused Agentic Framework using StrictJSON

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arxiv 2407.15734 v1 pith:EB7QL5YD submitted 2024-07-22 cs.AI cs.MA

John Chong Min Tan , Prince Saroj , Bharat Runwal , Hardik Maheshwari , Brian Lim Yi Sheng , Richard Cottrill , Alankrit Chona , Ambuj Kumar
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Mehul Motani
This is my paper · ORCID
classification cs.AIcs.MA
keywords taskgensolverateagentagenticdatasetframeworksolving
verification ladder T0 review T1 audit T2 compute T3 formal

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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 another Agent to execute. In order to reduce verbosity (and hence token usage), TaskGen uses StrictJSON that ensures JSON output from the Large Language Model (LLM), along with additional features such as type checking and iterative error correction. Key to the philosophy of TaskGen is the management of information/memory on a need-to-know basis. We empirically evaluate TaskGen on various environments such as 40x40 dynamic maze navigation with changing obstacle locations (100% solve rate), TextWorld escape room solving with dense rewards and detailed goals (96% solve rate), web browsing (69% of actions successful), solving the MATH dataset (71% solve rate over 100 Level-5 problems), Retrieval Augmented Generation on NaturalQuestions dataset (F1 score of 47.03%)

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Cited by 1 Pith paper

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  1. Generating a Low-code Complete Workflow via Task Decomposition and RAG

    cs.SE 2024-11 conditional novelty 4.0 of 10

    A case study and pattern catalogue arguing that Task Decomposition and RAG improve modularity, safety, testability, and time-to-market in a real enterprise workflow generator, with no quantitative evaluation.

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