cff-version: 1.2.0 message: "This repository contains a research paper. Please cite it using the metadata in preferred-citation below." type: software # required by CFF schema — actual citation type is in preferred-citation title: "Memory Archive: A Memory-Grounded Training Paradigm for Computer Use Agents" authors: - given-names: "Kartik" name-suffix: "A." date-released: "2026-05-14" abstract: > A research analysis of the training paradigm enabled by the Memory Archive data collection system, developed as part of Project Dockyard. Memory Archive produces a structured, annotated dataset comprising per-step actuation records, process-level reasoning annotations, visual state triples, and compiled task guides called memories. This data is used across all four stages of the CUA training and deployment lifecycle: pre-training, supervised fine-tuning, post-training reinforcement, and inference-time retrieval. The central novelty is format consistency — the memory.md artifact is the same structured object at pre-training, fine-tuning, post-training, and inference time, eliminating the train-deploy distribution gap that undermines most CUA systems. keywords: - computer use agents - memory-grounded training - format consistency - reinforcement learning - retrieval-augmented execution - process supervision - vision-language models repository-code: "https://github.com/nullvoider07/memory-archive-paradigm" url: "https://github.com/nullvoider07/memory-archive-paradigm" license: CC-BY-NC-4.0 preferred-citation: type: report title: "Memory Archive: A Memory-Grounded Training Paradigm for Computer Use Agents" authors: - given-names: "Kartik" name-suffix: "A." year: 2026 institution: name: "Project Dockyard" url: "https://doi.org/10.5281/zenodo.20176599" doi: "10.5281/zenodo.20176599" notes: "Independent research. Preprint available at Zenodo." references: - type: software title: "Memory Archive" authors: - given-names: "Kartik" name-suffix: "A." url: "https://github.com/nullvoider07/Memory-Archive" notes: "The data collection system that generates the training corpus described in this paper."