NullVoider/Memory-Archive-Paradigm

Memory Archive: A Memory-Grounded Training Paradigm for Computer Use Agents

Kartik . A · Independent Researcher · Project Dockyard

📄 Read the Paper (PDF)  ·  💻 Memory Archive Tool

Publication note: As an independent researcher, this architecture is published as an open-science preprint via Zenodo (CERN) to establish formal prior art. A permanent, globally recognised DOI is attached to this work. The full paper is available here in both PDF and Markdown.

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The Problem

The dominant CUA training pipeline trains on (screenshot, action) pairs and deploys with plain-text prompts and retrieved documents the model has never seen during training. Every task boundary is a distribution shift. Binary outcome rewards provide no per-step signal. Every execution is zero-shot regardless of prior experience with the same task.

The Central Thesis

Format consistency eliminates the train-deploy distribution gap.

The memory.md artifact — a structured procedural document with per-step reasoning, actuation commands, and image references — is the same object at pre-training, supervised fine-tuning, post-training RL, and inference. The model trains on exactly what it retrieves at runtime. Additionally, the trained model generates its own memory.md at inference time, growing the library continuously and providing a multi-dimensional evaluation signal during training without any external benchmark.

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Abstract

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. Reasoning annotations are produced by a VLM Reasoning Model as the primary source, with human annotation as an alternative mode. The paper covers all four training stages at full technical depth — mathematical formulations, actuation artifact treatment, data construction pipelines, algorithm specifications, hyperparameter guidance, and failure mode analysis. A fifth section covers self-generated memory as an in-training evaluation mechanism.

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System Architecture

Memory Archive connects to Control-Center (actuation via gRPC), The-Eyes (screen capture via HTTP), and a VLM Reasoning Model. Both the VLM (primary) and human annotator (alternative) produce the same schema in reasoning.jsonl.

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The Four Training Stages

memory.md threads through all four stages as the shared format currency — the same artifact the model retrieves and follows at inference.

Stage 1 — Pre-Training: Format Internalization

The base model learns what a well-formed memory looks like, how step sections are structured, and how image references relate to actuation commands — before any task-specific fine-tuning.

Data mix: memory.md documents (40%) · reasoning.jsonl + image triples (30%) · actuation command files (20%) · general GUI screenshots (10%)

3-phase curriculum: actuation vocabulary → step-level visual-intent alignment → full compiled memories

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Stage 2 — SFT: Actuation as a First-Class Target

SFT uses Formulation B — a retrieved memory.md is in context at every training step. The model learns to read and follow a memory at train time, not just at inference.

Key design: CommandEvent JSON and step headers are full-weight targets (w = 1.0). Reasoning uses stage-dependent weighting (0.75 early → 0.50 late). Memory tokens are masked entirely (w = 0.0).

All Memory Archive artifacts assembled into a single multi-step training sequence.

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Stage 3 — Post-Training RL: Memory Adherence

Algorithm: GRPO — eliminates a separate value network, critical given 150+ image encodings per session in the KV cache.

Three-component reward ($G = 8$ trajectories per task):

Component	Weight	What it measures
$R_{\text{align}}$ — Step Alignment	$\alpha = 0.3$	Cosine similarity between agent reasoning and memory step text (domain-specific CUA encoder)
$R_{\text{spatial}}$ — Visual Grounding	$\beta = 0.4$	Euclidean pixel distance: agent click vs memory at-frame annotation
$R_{\text{outcome}}$ — Outcome Consistency	$\gamma = 0.3$	Visual encoder similarity between agent after-frame and memory after-frame

$R_{\text{spatial}}$ carries the highest weight — spatial precision is the hardest CUA skill to acquire from language supervision alone.

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Stage 4 — Inference: Retrieval-Augmented Execution

Two-stage retrieval: Bi-encoder HNSW (top-50 in ~3ms) → cross-encoder re-ranker (top-3 in ~80ms). Confidence gate at 0.65. OS/version pre-filter prevents stale memories.

Working memory update: deviation from the retrieved memory is tracked per step. Three consecutive steps with deviation score > 0.4 triggers re-retrieval or new memory creation.

New memory creation: on task success in the generalisation path, the full execution trajectory is compiled into a new memory.md and added to the library — growing it endogenously each cycle.

New memories created at inference and self-generated memories passing quality review both feed back into the pre-training corpus.

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Self-Generated Memory as In-Training Evaluation

At training checkpoints, the model produces its own memory.md through live CUA sessions. This gives four diagnostic signals without any external benchmark:

Signal	Detects	Threshold
MinHash LSH similarity to training memories	Overfitting	> 0.85 flags verbatim reproduction
Step count completeness + causal connective density	Underfitting	Monitored across training
Entity overlap: reasoning vs at/after frames	Context-awareness	> 0.75 average
Step count ratio < 1.0 vs human baseline	Super-human performance	Flagged for human review

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Comparison with Existing CUA Approaches

System	Process Labels	Memory at Inference	Format Consistency
Behavioral Cloning	None	None	Low
UI-TARS / OpenCUA-32B	Synthetic CoT	None	Medium
ICAL	VLM-abstracted	Retrieved (implicit)	High
HyMEM	None	Graph-structured	Medium
SkillRL	Distilled skills	Hierarchical skills	Medium
Memory Archive	VLM-gen + human cal	memory.md (same as training)	High — all stages identical

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Memory Archive Tool

The data collection system that generates the training corpus described in this paper is developed as part of Project Dockyard.

👉 github.com/nullvoider07/Memory-Archive

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Citation

@misc{kartik2026memoryarchive,
  title        = {Memory Archive: A Memory-Grounded Training Paradigm
                  for Computer Use Agents},
  author       = {Kartik A.},
  year         = {2026},
  howpublished = {Project Dockyard},
  doi          = {10.5281/zenodo.20176599},
  note         = {Independent Research. Preprint available at Zenodo:
                  \url{https://doi.org/10.5281/zenodo.20176599}}
}

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License

This work is licensed under the CC-BY-NC 4.0.

© 2026 Kartik A. · Project Dockyard