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+# Memory Archive: A Memory-Grounded Training Paradigm for Computer Use Agents
+
+[![License: CC BY-NC 4.0](https://img.shields.io/badge/License-CC%20BY--NC%204.0-lightgrey.svg)](LICENSE)
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.20176599.svg)](https://doi.org/10.5281/zenodo.20176599)
+[![Project Dockyard](https://img.shields.io/badge/Project-Dockyard-purple)](https://github.com/nullvoider07/Memory-Archive)
+[![Paper](https://img.shields.io/badge/Paper-PDF-red)](memory_archive.pdf)
+
+**Kartik . A** · Independent Researcher · Project Dockyard
+
+📄 [Read the Paper (PDF)](memory_archive_paradigm.pdf)  ·  💻 [Memory Archive Tool](https://github.com/nullvoider07/Memory-Archive)
+
+> **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.
+
+---
+
+## 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.
+
+---
+
+## 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.
+
+---
+
+## System Architecture
+
+<div align="center">
+
+![Memory Archive System Architecture](images/fig01.png)
+
+*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`.*
+
+</div>
+
+---
+
+## The Four Training Stages
+
+<div align="center">
+
+![Training Pipeline Overview](images/fig04.png)
+
+*`memory.md` threads through all four stages as the shared format currency — the same artifact the model retrieves and follows at inference.*
+
+</div>
+
+### 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
+
+---
+
+### 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`).
+
+<div align="center">
+
+![SFT Training Example Construction](images/fig05.png)
+
+*All Memory Archive artifacts assembled into a single multi-step training sequence.*
+
+</div>
+
+---
+
+### 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.
+
+<div align="center">
+
+![GRPO Training Loop](images/fig06.png)
+
+![Memory Adherence Reward Structure](images/fig07.png)
+
+</div>
+
+---
+
+### Stage 4 — Inference: Retrieval-Augmented Execution
+
+<div align="center">
+
+![Inference-Time Memory Retrieval Pipeline](images/fig08.png)
+
+![Two-Stage Retrieval Stack](images/fig09.png)
+
+</div>
+
+**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.
+
+<div align="center">
+
+![Format Consistency Lifecycle](images/fig10.png)
+
+*New memories created at inference and self-generated memories passing quality review both feed back into the pre-training corpus.*
+
+</div>
+
+---
+
+## 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 |
+
+---
+
+## 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** |
+
+---
+
+## Repository Structure
+
+```
+memory-archive-paradigm/
+├── images/
+│   ├── fig01.png               # Fig 1:  System Architecture
+│   ├── fig04.png               # Fig 4:  Training Pipeline Overview
+│   ├── fig05.png               # Fig 5:  SFT Training Example Construction
+│   ├── fig06.png               # Fig 6:  GRPO Training Loop
+│   ├── fig07.png               # Fig 7:  Memory Adherence Reward
+│   ├── fig08.png               # Fig 8:  Inference-Time Retrieval Pipeline
+│   ├── fig09.png               # Fig 9:  Two-Stage Retrieval Stack
+│   └── fig10.png               # Fig 10: Format Consistency Lifecycle
+├── .gitattributes
+├── .gitignore
+├── CITATION.cff                # Machine-readable citation
+├── LICENSE                     # CC BY-NC 4.0
+├── README.md
+└── memory_archive_paradigm.pdf # Compiled Paper
+```
+
+---
+
+## 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](https://github.com/nullvoider07/Memory-Archive)**
+
+---
+
+## Citation
+
+```bibtex
+@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}}
+}
+```
+
+---
+
+## License
+
+This work is licensed under the [CC-BY-NC 4.0](LICENSE).
+
+© 2026 Kartik A. · Project Dockyard