| """ |
| Generates a professional capstone project proposal DOCX for MiniSEAL. |
| """ |
|
|
| from docx import Document |
| from docx.shared import Pt, RGBColor, Inches |
| from docx.enum.text import WD_ALIGN_PARAGRAPH |
| from docx.oxml.ns import qn |
| from docx.oxml import OxmlElement |
|
|
|
|
| DARK_BLUE = RGBColor(0x1A, 0x36, 0x5D) |
| MED_BLUE = RGBColor(0x2C, 0x52, 0x82) |
| BLACK = RGBColor(0x00, 0x00, 0x00) |
|
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| |
|
|
| def heading(doc, text, level=1): |
| p = doc.add_paragraph() |
| p.paragraph_format.space_before = Pt(16 if level == 1 else 10) |
| p.paragraph_format.space_after = Pt(4) |
| r = p.add_run(text) |
| r.bold = True |
| r.font.color.rgb = DARK_BLUE if level == 1 else MED_BLUE |
| r.font.size = Pt(15 if level == 1 else 12) |
| return p |
|
|
|
|
| def body(doc, text): |
| p = doc.add_paragraph() |
| p.paragraph_format.space_after = Pt(7) |
| p.paragraph_format.line_spacing = Pt(15) |
| r = p.add_run(text) |
| r.font.size = Pt(11) |
| r.font.color.rgb = BLACK |
| return p |
|
|
|
|
| def bullet(doc, text, bold_lead=None): |
| """bullet_lead: optional bold prefix text before the rest.""" |
| p = doc.add_paragraph(style="List Bullet") |
| p.paragraph_format.space_after = Pt(4) |
| if bold_lead: |
| rb = p.add_run(bold_lead) |
| rb.bold = True |
| rb.font.size = Pt(11) |
| rn = p.add_run(text) |
| rn.font.size = Pt(11) |
| else: |
| r = p.add_run(text) |
| r.font.size = Pt(11) |
|
|
|
|
| def numbered(doc, text, bold_lead=None): |
| p = doc.add_paragraph(style="List Number") |
| p.paragraph_format.space_after = Pt(5) |
| if bold_lead: |
| rb = p.add_run(bold_lead) |
| rb.bold = True |
| rb.font.size = Pt(11) |
| rn = p.add_run(text) |
| rn.font.size = Pt(11) |
| else: |
| r = p.add_run(text) |
| r.font.size = Pt(11) |
|
|
|
|
| def rule(doc): |
| p = doc.add_paragraph() |
| pPr = p._p.get_or_add_pPr() |
| pBdr = OxmlElement("w:pBdr") |
| bot = OxmlElement("w:bottom") |
| bot.set(qn("w:val"), "single") |
| bot.set(qn("w:sz"), "6") |
| bot.set(qn("w:space"), "1") |
| bot.set(qn("w:color"), "BBBBBB") |
| pBdr.append(bot) |
| pPr.append(pBdr) |
| p.paragraph_format.space_after = Pt(8) |
|
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|
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| |
|
|
| doc = Document() |
|
|
| for sec in doc.sections: |
| sec.top_margin = Inches(1.0) |
| sec.bottom_margin = Inches(1.0) |
| sec.left_margin = Inches(1.25) |
| sec.right_margin = Inches(1.25) |
|
|
|
|
| |
| tp = doc.add_paragraph() |
| tp.alignment = WD_ALIGN_PARAGRAPH.CENTER |
| tp.paragraph_format.space_before = Pt(20) |
| tp.paragraph_format.space_after = Pt(6) |
| tr = tp.add_run("MiniSEAL") |
| tr.bold = True |
| tr.font.size = Pt(28) |
| tr.font.color.rgb = DARK_BLUE |
|
|
| sp = doc.add_paragraph() |
| sp.alignment = WD_ALIGN_PARAGRAPH.CENTER |
| sp.paragraph_format.space_after = Pt(4) |
| sr = sp.add_run( |
| "A Human-Supervised, Self-Adapting Language Model with\n" |
| "SEAL-Inspired Continuous Learning and LoRA-Based Adaptation" |
| ) |
| sr.font.size = Pt(13) |
| sr.font.color.rgb = MED_BLUE |
|
|
| cat = doc.add_paragraph() |
| cat.alignment = WD_ALIGN_PARAGRAPH.CENTER |
| cr = cat.add_run("Capstone Project Proposal | Department of Computer Science") |
| cr.font.size = Pt(10) |
| cr.font.color.rgb = RGBColor(0x77, 0x77, 0x77) |
| cr.italic = True |
|
|
| doc.add_paragraph() |
| rule(doc) |
|
|
|
|
| |
| heading(doc, "1. Title") |
| body(doc, |
| "MiniSEAL: A Human-Supervised, Self-Adapting Language Model with SEAL-Inspired Continuous Learning " |
| "and LoRA-Based Adaptation.") |
| rule(doc) |
|
|
|
|
| |
| heading(doc, "2. Introduction") |
|
|
| body(doc, |
| "Large Language Models have fundamentally transformed the landscape of artificial intelligence, " |
| "demonstrating strong performance across natural language understanding, question answering, " |
| "summarisation, and dialogue. Models such as GPT-4, Llama-3, and Claude have set new benchmarks " |
| "for what is achievable with large-scale Transformer architectures trained on vast corpora. " |
| "Despite these advances, a critical limitation persists: once a model is trained and deployed, " |
| "its weights are frozen. It cannot incorporate new knowledge, adapt to evolving institutional " |
| "contexts, or correct factual errors without undergoing an expensive and time-consuming full " |
| "retraining cycle.") |
|
|
| body(doc, |
| "This limitation is particularly pronounced in institutional deployments. A university assistant, " |
| "for example, must remain current with changes to course curricula, departmental policies, " |
| "research developments, and campus events β none of which a static model can accommodate. " |
| "Similarly, in enterprise environments, proprietary knowledge evolves continuously, and relying " |
| "on a commercially hosted model introduces significant data privacy risks. There is therefore a " |
| "strong need for a locally owned, continuously learning language model that can adapt to new " |
| "information safely and efficiently.") |
|
|
| body(doc, |
| "This project, MiniSEAL, addresses this need by designing and implementing a complete, private " |
| "language model pipeline from first principles. The system covers every layer of the modern LLM " |
| "stack β from a custom Byte-Pair Encoding tokeniser and a decoder-only Transformer architecture " |
| "to a four-stage training pipeline comprising Pretraining, Supervised Fine-Tuning, Direct " |
| "Preference Optimisation, and SEAL-inspired Continuous Adaptation. The self-adaptation engine, " |
| "which forms the central novelty of this project, is directly inspired by the Self-Adapting " |
| "Language Models framework published by MIT CSAIL in 2025. Unlike the original SEAL framework, " |
| "which employs an autonomous Reinforcement Learning feedback loop, this project replaces that " |
| "loop with a Human-in-the-Loop safety quarantine system, ensuring that every proposed weight " |
| "update is reviewed and approved by a human administrator before being applied β making " |
| "continuous learning safe, auditable, and suitable for real-world institutional deployment.") |
|
|
| rule(doc) |
|
|
|
|
| |
| heading(doc, "3. Literature Survey") |
|
|
| body(doc, |
| "The theoretical and methodological foundation of MiniSEAL is drawn from four seminal research " |
| "contributions that directly inform its architecture, training methodology, and adaptation " |
| "strategy.") |
|
|
| |
| heading(doc, "3.1 Attention Is All You Need β Vaswani et al. (2017)", level=2) |
| body(doc, |
| "Vaswani et al. introduced the Transformer architecture, proposing that sequence-to-sequence " |
| "tasks could be solved entirely through self-attention mechanisms, dispensing with recurrent " |
| "or convolutional networks. The paper formalised scaled dot-product attention, multi-head " |
| "attention, and the encoder-decoder structure that became the basis for all subsequent large " |
| "language models. The decoder-only variant of this architecture, used in autoregressive models " |
| "such as GPT and LLaMA, forms the core model structure of this project.") |
|
|
| |
| heading(doc, "3.2 Self-Adapting Language Models (SEAL) β Zweiger et al., MIT CSAIL (2025)", level=2) |
| body(doc, |
| "This paper represents the primary research motivation for this project. Zweiger et al. " |
| "propose a framework in which a deployed language model autonomously generates structured " |
| "self-edits β synthetic training examples derived from a raw passage of text β and applies " |
| "supervised fine-tuning on its own weights to incorporate new factual knowledge persistently. " |
| "The framework uses a Reinforcement Learning feedback loop in which the reward signal is the " |
| "model's performance improvement on a downstream task following the self-edit. The authors " |
| "demonstrate strong results in knowledge incorporation and few-shot generalisation tasks, while " |
| "identifying catastrophic forgetting as a key open challenge. This project adopts the SEAL " |
| "self-edit generation paradigm but replaces the autonomous RL loop with a human-supervised " |
| "approval gate, mitigating the risk of adversarial or erroneous self-edits corrupting model weights.") |
|
|
| |
| heading(doc, "3.3 LoRA: Low-Rank Adaptation β Hu et al., Microsoft Research (2022)", level=2) |
| body(doc, |
| "Hu et al. proposed Low-Rank Adaptation as a parameter-efficient approach to fine-tuning large " |
| "language models. Rather than updating all model weights during adaptation, LoRA freezes the " |
| "pretrained weights and injects small, trainable low-rank matrix pairs into each targeted layer. " |
| "The effective update is expressed as ΞW = BA, where the rank r is far smaller than the original " |
| "weight dimensions, reducing trainable parameters by over 98% relative to full fine-tuning while " |
| "achieving comparable performance. This technique is employed in this project as the exclusive " |
| "mechanism for continuous adaptation, ensuring that the base model remains intact and that " |
| "individual knowledge updates remain modular, versioned, and independently removable.") |
|
|
| |
| heading(doc, "3.4 Direct Preference Optimisation β Rafailov et al., Stanford University (2023)", level=2) |
| body(doc, |
| "Rafailov et al. introduced Direct Preference Optimisation as a stable, mathematically grounded " |
| "alternative to Reinforcement Learning from Human Feedback for aligning language model behaviour " |
| "with human preferences. DPO reformulates the preference alignment objective as a closed-form " |
| "classification loss operating over pairs of preferred and rejected model responses, eliminating " |
| "the need for a separately trained reward model. The resulting training procedure is significantly " |
| "more stable and computationally efficient than RLHF. In this project, DPO is applied following " |
| "Supervised Fine-Tuning to ensure that the model produces responses that are helpful, safe, and " |
| "appropriately toned before continuous self-adaptation begins.") |
|
|
| rule(doc) |
|
|
|
|
| |
| heading(doc, "4. Aim of the Project") |
| body(doc, |
| "The overarching aim of this project is to design, implement, and train a complete, privately " |
| "owned language model that is capable of continuous, human-supervised self-adaptation. " |
| "The specific objectives are:") |
|
|
| numbered(doc, |
| "To design and implement a decoder-only Transformer language model and a custom Byte-Pair " |
| "Encoding tokeniser entirely from scratch, without relying on any pre-built foundational model " |
| "or commercial API.") |
| numbered(doc, |
| "To establish a complete, sequential four-stage training pipeline β Pretraining on raw corpora, " |
| "Supervised Fine-Tuning on instruction-response pairs, Direct Preference Optimisation on " |
| "preference data, and SEAL-inspired Continuous Adaptation β each implemented as a modular, " |
| "independently executable service.") |
| numbered(doc, |
| "To implement a SEAL-inspired self-adaptation engine in which the model generates its own " |
| "structured training data from raw passages and updates its weights persistently and efficiently " |
| "using Low-Rank Adaptation (LoRA).") |
| numbered(doc, |
| "To engineer a Human-in-the-Loop safety system that intercepts every model-generated self-edit, " |
| "automatically assesses its risk level, stages it in a quarantine database, and requires explicit " |
| "human approval before any weight update is committed β preventing model poisoning and ensuring " |
| "auditability.") |
| numbered(doc, |
| "To train and validate the complete pipeline on an NVIDIA A100 GPU, demonstrating that the system " |
| "is scalable to research-grade hardware and suitable for institutional deployment.") |
|
|
| rule(doc) |
|
|
|
|
| |
| heading(doc, "5. Methodology and Planned Process") |
|
|
| body(doc, |
| "The project follows a structured, sequential development methodology. Each stage builds directly " |
| "on the outputs of the previous stage, mirroring the training pipeline used by leading AI " |
| "research organisations.") |
|
|
| |
| heading(doc, "Step 1 β Architecture Design and Tokeniser Training", level=2) |
| body(doc, |
| "The first step involves designing the core model architecture: a decoder-only Transformer with " |
| "pre-norm RMSNorm, Rotary Positional Embedding (RoPE), and a SwiGLU feed-forward network. " |
| "In parallel, a custom Byte-Pair Encoding tokeniser is trained on the project corpus, producing " |
| "a vocabulary tailored to the target domain. This step establishes the foundational components " |
| "on which all subsequent training stages depend.") |
|
|
| |
| heading(doc, "Step 2 β Pretraining", level=2) |
| body(doc, |
| "The model is pretrained using next-token prediction on a curated raw text corpus. This stage " |
| "teaches the model the statistical structure of language, building a general-purpose language " |
| "understanding capability. Training is performed on an NVIDIA A100 GPU using the causal " |
| "cross-entropy loss. The output is a base model checkpoint that serves as the starting point " |
| "for all subsequent alignment and adaptation stages.") |
|
|
| |
| heading(doc, "Step 3 β Supervised Fine-Tuning and Preference Alignment", level=2) |
| body(doc, |
| "The pretrained base model undergoes two sequential alignment stages. First, Supervised " |
| "Fine-Tuning trains the model on instruction-response pairs, transforming it from a raw " |
| "next-token predictor into an instruction-following assistant. Second, Direct Preference " |
| "Optimisation aligns the model's tone and behaviour with human preferences by training it " |
| "to prefer high-quality responses over low-quality ones. After this stage, the model is " |
| "safe, helpful, and appropriately calibrated.") |
|
|
| |
| heading(doc, "Step 4 β SEAL-Inspired Continuous Self-Adaptation", level=2) |
| body(doc, |
| "This is the core novelty of the project. The aligned model is given a raw passage of text " |
| "and generates its own structured question-and-answer pairs β self-edits β describing the " |
| "key facts contained in the passage. These self-edits are automatically scored for risk and " |
| "staged in a quarantine database. A human administrator reviews each self-edit on a web " |
| "dashboard and approves or rejects it. Approved self-edits are used to train a LoRA adapter " |
| "on the aligned model's Q and V projection layers, permanently incorporating the new knowledge " |
| "without modifying the base model weights.") |
|
|
| |
| heading(doc, "Step 5 β Validation and Evaluation", level=2) |
| body(doc, |
| "The complete system is evaluated end-to-end on the NVIDIA A100 cluster. Evaluation metrics " |
| "include training loss curves across all stages, perplexity on held-out validation sets, " |
| "factual accuracy on knowledge incorporation tasks, and a qualitative assessment of the " |
| "model's responses before and after LoRA adaptation.") |
|
|
| rule(doc) |
|
|
|
|
| |
| heading(doc, "6. Expected Outcomes") |
|
|
| body(doc, |
| "Upon successful completion, the project is expected to produce the following outcomes:") |
|
|
| bullet(doc, |
| "A working, trained language model capable of generating coherent, factually grounded responses " |
| "to natural language queries.", |
| bold_lead="Trained Language Model: ") |
| bullet(doc, |
| "A demonstrated self-adaptation pipeline in which the model successfully reads a new passage, " |
| "generates self-edits, and incorporates the new knowledge through LoRA fine-tuning.", |
| bold_lead="Demonstrated Continuous Learning: ") |
| bullet(doc, |
| "Measurable improvement in factual accuracy on domain-specific questions following the " |
| "application of approved LoRA adapters.", |
| bold_lead="Factual Accuracy Improvement: ") |
| bullet(doc, |
| "A validated Human-in-the-Loop safety gate that successfully intercepts, scores, and flags " |
| "high-risk self-edits before any model weights are updated.", |
| bold_lead="Safety Gate Validation: ") |
| bullet(doc, |
| "A scalable, enterprise-ready MLOps architecture demonstrating that the entire pipeline can " |
| "be reproducibly executed on GPU cluster hardware.", |
| bold_lead="Reproducible MLOps Pipeline: ") |
|
|
| rule(doc) |
|
|
|
|
| |
| heading(doc, "7. Deliverables") |
|
|
| numbered(doc, |
| "Complete source code for the end-to-end pipeline, including the Transformer model, " |
| "BPE tokeniser, training services, and self-adaptation engine.", |
| bold_lead="Source Code Repository: ") |
| numbered(doc, |
| "Trained model checkpoints from each stage of the training pipeline (Pretrained, SFT-aligned, " |
| "DPO-aligned, and LoRA-adapted), stored as versioned artefacts.", |
| bold_lead="Model Checkpoints: ") |
| numbered(doc, |
| "A web-accessible Human-in-the-Loop administration dashboard for reviewing, approving, and " |
| "rejecting model-generated self-edits.", |
| bold_lead="Admin Dashboard: ") |
| numbered(doc, |
| "A comprehensive technical report documenting the system architecture, training methodology, " |
| "evaluation results, and a comparison between the SEAL RL approach and the Human-in-the-Loop " |
| "quarantine approach implemented in this project.", |
| bold_lead="Technical Report: ") |
| numbered(doc, |
| "A project presentation and live demonstration of the self-adaptation pipeline, including a " |
| "real-time demonstration of a new passage being incorporated into the model's knowledge base " |
| "through the approval dashboard.", |
| bold_lead="Project Presentation and Demo: ") |
|
|
| rule(doc) |
|
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|
| |
| heading(doc, "References") |
|
|
| refs = [ |
| "[1] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ε., & Polosukhin, I. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems (NeurIPS). arXiv:1706.03762.", |
| "[2] Zweiger, A., Pari, J., Guo, H., AkyΓΌrek, E., Kim, Y., & Agrawal, P. (2025). Self-Adapting Language Models. MIT CSAIL. arXiv:2506.10943.", |
| "[3] Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., & Chen, W. (2022). LoRA: Low-Rank Adaptation of Large Language Models. International Conference on Learning Representations (ICLR). arXiv:2106.09685.", |
| "[4] Rafailov, R., Sharma, A., Mitchell, E., Ermon, S., Manning, C. D., & Finn, C. (2023). Direct Preference Optimization: Your Language Model is Secretly a Reward Model. Advances in Neural Information Processing Systems (NeurIPS). arXiv:2305.18290.", |
| ] |
|
|
| for ref in refs: |
| p = doc.add_paragraph() |
| p.paragraph_format.space_after = Pt(5) |
| r = p.add_run(ref) |
| r.font.size = Pt(10) |
| r.font.color.rgb = RGBColor(0x33, 0x33, 0x33) |
|
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| |
| output_path = "/Users/aaronr/Desktop/DUK/GENAI/self_owned_llm/Capstone_Project_Description.docx" |
| doc.save(output_path) |
| print(f" Saved β {output_path}") |
|
|
