mlsanityData to 7 Oct 2026

AutoScrum: Automating Project Planning Using Large Language Models

arXiv 2306.03197 ↗First posted 5 Jun 2023cs.AI cs.CLcited by 1

On the map: LLM reasoning → Reasoning for agents, planning and code

Abstract (arXiv)

Recent advancements in the field of large language models have made it possible to use language models for advanced reasoning. In this paper we leverage this ability for designing complex project plans based only on knowing the current state and the desired state. Two approaches are demonstrated - a scrum based approach and a shortcut plan approach. The scrum based approach executes an automated process of requirements gathering, user story mapping, feature identification, task decomposition and finally generates questions and search terms for seeking out domain specific information to assist with task completion. The shortcut approach looks at most recent snapshot of the current and desired state and generates the next most reasonable task to do in order to get to the desired state as quickly as possible. In this paper we automate everything using a novel concept of "Language Programs". These are programs written in natural language designed to process input data through the language model. Guidance language is used for all LLM programs. All demo source code for this paper is available at https://github.com/autoscrum/autoscrum

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning. Builds on 8 of its 36 references that are in the map. First posted on arXiv 5 Jun 2023.

Builds on

8 of 36 references.

References · 8

  1. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
  2. 2023Voyager: An Open-Ended Embodied Agent with Large Language Modelscited by 2,081
  3. 2023Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Modelscited by 550
  4. 2023Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memorycited by 288
  5. 2023On the Tool Manipulation Capability of Open-source Large Language Modelscited by 122
  6. 2023InternGPT: Solving Vision-Centric Tasks by Interacting with ChatGPT Beyond Languagecited by 96
  7. 2023Improving accuracy of GPT-3/4 results on biomedical data using a retrieval-augmented language modelcited by 14
  8. 2023OlaGPT: Empowering LLMs With Human-like Problem-Solving Abilitiescited by 10

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Cited by year

Citing papers per year: 2023: 1.

1 paper.

Similar, not cited

Close in wording, no citation either way

  1. 2025Complex LLM Planning via Automated Heuristics Discovery
  2. 2025Plan-and-Act: Improving Planning of Agents for Long-Horizon Tasks
  3. 2023Self-planning Code Generation with Large Language Models
  4. 2025Scaling LLM Planning: NL2FLOW for Parametric Problem Generation and Rigorous Evaluation
  5. 2025TPS-Bench: Evaluating AI Agents' Tool Planning & Scheduling Abilities in Compounding Tasks
  6. 2024AutoFlow: Automated Workflow Generation for Large Language Model Agents
  7. 2026Language Models as Higher-Order Planning Formalizers
  8. 2025Large Language Models for Planning: A Comprehensive and Systematic Survey