Future Directions of TNH-Scholar¶

Explores long-horizon possibilities for TNH-Scholar and related systems—non-committal scenarios informed by the project’s philosophy, architecture, and trajectory.

TNH-Scholar is intentionally designed as a foundational system — a clean corpus, structured text models, provenance-rich transformations, agent-ready pipelines, and a pattern-driven GenAI interface. This foundation enables not only the current scholarly workflows, but also a number of long-horizon possibilities.

The sections below outline potential future evolutions.¶

flowchart TD

    A[Foundational System<br/>Clean Corpus • Structured Text • Provenance • Patterns] --> B[Semi-Autonomous Agent Loops]
    A --> C[Autonomous Corpus Pipelines]
    A --> D[Intelligent Scholarly Assistants]
    A --> E[Corpus-Aware Model Training / Evaluation]
    A --> F[Agentic Application Framework]
    A --> G[Ecosystem-Level Integrations]

    B --> B1[Code Maintenance Agents<br/>Refactor • Test • Evaluate • Plan]
    B --> B2[Research / Data Agents<br/>Cleaning • Sectioning • Alignment]

    C --> C1[Continuous Ingest + Cleanup]
    C --> C2[Metadata + Sectioning Pipelines]
    C --> C3[Translation + Evaluation Loops]

    D --> D1[Semantic Research Companion]
    D --> D2[Interactive Dharma Exploration]

    E --> E1[Domain-Specific Models]
    E --> E2[Corpus-Aligned Evaluation Loops]

    F --> F1[General Codebase Agents]
    F --> F2[Document / Data Transformation Systems]

    G --> G1[Advanced UX Layers<br/>VS Code • Web • Jupyter]
    G --> G2[Distributed Scholarly Tools<br/>APIs • Collaborators • Multi-modal]

    style A fill:#fdf6e3,stroke:#b58900,stroke-width:2px
    style B fill:#eee8d5,stroke:#b58900
    style C fill:#eee8d5,stroke:#b58900
    style D fill:#eee8d5,stroke:#b58900
    style E fill:#eee8d5,stroke:#b58900
    style F fill:#eee8d5,stroke:#b58900
    style G fill:#eee8d5,stroke:#b58900

1. Semi-Autonomous, Long-Running Agent Loops¶

A natural evolution of the GenAIService + PromptCatalog + provenance system is the creation of long-running, semi-autonomous agents that execute sequences of tasks with human oversight.

These loops could support:

1.1 Code-oriented agents (descendant projects)¶

Agents that can:

Parse ADRs and design docs
Generate or refactor code patches
Evaluate quality using pattern-based evaluation prompts
Run tests
Detect architectural drift
Open pull requests
Summarize changes for humans
Make plans like:
“new-problem-encountered”
“design revision required”
“refactor recommended”
“evaluation failure—request human review”

This forms the basis of:

An AI-augmented software engineering assistant capable of maintaining complex codebases using structured, documented intent.

A direct descendant of TNH-Scholar could be a general-purpose agentic software engineering platform using these same abstractions.

2. Autonomous Corpus Processing Pipelines¶

TNH-Scholar’s data layer (OCR → structured text → metadata → alignment) can be expanded into autonomous pipelines that continuously refine the corpus.

These pipelines could¶

Automatically detect new scans or materials
Run cleanup/normalization stages
Apply sectioning & metadata tagging patterns
Align bilingual or trilingual segments
Evaluate translation quality
Surface anomalies or inconsistencies for human review
Trigger model updates or fine-tuning rounds

This becomes:

A living, evolving scholarly corpus with transparent, traceable transformations and continuous improvement.

3. Intelligent Scholarly Assistants¶

Once the corpus is structured and richly annotated, future systems could support:

3.1 Semantic research companions¶

Agents that:

Trace a concept (e.g., interbeing, emptiness, mindfulness) across decades of talks
Construct cross-lingual concept graphs
Surface related sutras, commentaries, and historical contexts
Link concepts across Vietnamese, English, Chinese, Pali, Sanskrit, Tibetan sources
Generate reading paths, study plans, or commentary maps

3.2 Interactive Dharma exploration¶

Higher-level interfaces could enable:

Interactive Q&A grounded in verifiable citations
Multilingual guided meditation or sutra explanations
Diachronic examination of teachings over time
Timeline exploration of Thích Nhất Hạnh’s writings and talks

This brings the tradition into rich conversation with practitioners and scholars, with accuracy and transparency.

4. Model Training & Corpus-Aware AI Systems¶

TNH-Scholar could become the foundation for:

4.1 Domain-specific models¶

Multilingual Buddhist embedding models
Custom translation models fine-tuned on Plum Village sources
Topic-specific summarizers
Dialogue systems grounded in verifiable citations

4.2 Corpus-aligned model evaluation loops¶

With provenance and pattern-driven evaluation, you could build:

Continuous training pipelines
Regression tests for translation or summarization accuracy
Style- and lineage-aware evaluation criteria
Model quality dashboards

These models would not replace human teachers but enhance research, translation, and accessibility.

4.3 Training Pipeline Research Direction¶

Status: Research spike planned (see GitHub Issue #6)

The processed content generated by TNH Scholar's AI workflows could serve as training data for model fine-tuning:

Research Questions:

How to effectively extract training pairs from processed content?
What fine-tuning approaches are most suitable (OpenAI fine-tuning, open source alternatives)?
What are resource requirements for training?
How to evaluate training effectiveness?
What infrastructure is needed?

Potential Approaches:

Extract human-reviewed translation pairs for fine-tuning
Use sectioning outputs as examples for structure-aware models
Create domain-specific evaluation datasets from validated outputs
Develop feedback loops between model performance and corpus quality

Considerations:

Balance between prototype phase priorities and long-term research
Resource constraints (compute, storage, API costs)
Quality assurance for training data
Community involvement in evaluation and validation

This research direction aligns with the long-term vision of corpus-aware AI systems while remaining grounded in current prototype capabilities.

5. Agentic Application Development Framework¶

TNH-Scholar’s architecture (patterns → GenAIService → provenance → structured data) could generalize to:

A modular agentic automation framework for any domain.

Possible future descendant projects:

A codebase-maintaining agent system
A domain-specific document-processing AI
A pattern-driven data transformation engine
A provenance-preserving automation fabric

The philosophical and architectural foundations of TNH-Scholar (structured data, documented intent, provenance-first, pattern-based prompting) make it an ideal parent project for a broader agentic ecosystem.

6. Ecosystem-Level Integrations¶

Future possibilities include:

6.1 Advanced UX layers¶

VS Code development agent integration
In-browser corpus exploration environments
Interactive bilingual study interfaces
Multi-panel JVB + text + translation + metadata views
Notebook-based agent workflows (e.g., Jupyter, VS Code notebooks)

6.2 Distributed scholarly tools¶

APIs for universities or monasteries
Collaborative annotation environments
Integrations with digital humanities platforms
Cross-repository semantic search
Multi-modal study tools for audio/video/text composites

7. Long-Term Vision¶

Many of these horizons converge into a singular possibility:

A living, evolving, transparent, agent-assisted repository of Plum Village teachings and related Buddhist sources — continually cleaned, translated, aligned, evaluated, and enriched, with humans guiding the meaning and quality.

This is the highest vision of TNH-Scholar:

A bridge between ancient wisdom and modern AI practice.
A platform that supports, rather than automates, interpretation.
A system that grows with care, clarity, and purpose.

This document is intentionally speculative.
As the project matures, some directions will solidify into real designs; others may remain guiding inspirations.
It should be updated when major new horizons emerge or when certain horizons become active workstreams.