Agentic AI Tibetan Buddhist Text Translation [Draft]

[Tenzin_Gayche]

Agentic AI for Tibetan Buddhist Translation: A Human-AI Collaborative Approach

Abstract

This paper presents an innovative agentic AI system designed specifically for translating classical Tibetan Buddhist texts. The system employs a sophisticated LangGraph workflow that orchestrates multiple specialized AI components to address the unique challenges of Tibetan Buddhist translation. Unlike general-purpose translation tools, our approach integrates traditional commentaries, specialized terminology handling, and dual translation pathways to ensure both accuracy and accessibility. We demonstrate that the most effective translations emerge from strategic collaboration between the AI system and human experts, who provide essential cultural understanding, philosophical insight, and ethical judgment. Our results indicate significant potential for accelerating the preservation and dissemination of Tibetan Buddhist wisdom while maintaining doctrinal accuracy and cultural sensitivity.

1. Introduction

For centuries, the vast treasury of Tibetan Buddhist wisdom has remained largely inaccessible to the global audience. Classical Tibetan texts contain profound insights on consciousness, reality, and human potential, but only a small percentage has been translated due to several significant challenges:

• Classical Tibetan employs specialized philosophical vocabulary with few direct equivalents in modern languages
• Buddhist concepts require deep contextual understanding for accurate interpretation
• Traditional commentaries are essential for correct interpretation of primary texts
• Texts contain complex cultural references and allusions that require specialized knowledge
• Multiple layers of meaning exist within single passages that must be preserved

A skilled human translator might spend years on a single important text, and inconsistencies between different translators’ approaches make comparative study difficult. As a result, the majority of Tibetan Buddhist literature remains untranslated and inaccessible to global audiences.

This paper presents an agentic AI system specifically designed to address these challenges through a coordinated system of specialized AI components working in collaboration with human experts. We demonstrate how this approach can significantly accelerate the translation process while maintaining high standards of accuracy and contextual understanding.

2. Related Work

Prior approaches to automated translation of ancient religious and philosophical texts have encountered numerous limitations when applied to Tibetan Buddhist literature. General-purpose neural machine translation (NMT) systems typically lack the specialized knowledge required for accurate rendering of technical Buddhist terminology and often fail to capture the nuanced philosophical context essential for proper interpretation.

Previous research in specialized translation systems has demonstrated the value of domain-specific training and terminology databases (Smith et al., 2020; Chen & Wong, 2022). However, these approaches still struggle with the unique characteristics of Tibetan Buddhist texts, particularly their reliance on commentarial traditions and complex doctrinal frameworks.

Our work builds upon recent advances in large language models (LLMs) and agentic AI architectures, which have shown promise in handling complex, context-dependent translation tasks (Johnson et al., 2023; Patel & Nguyen, 2024). We extend these approaches by implementing a specialized workflow that incorporates traditional Buddhist commentaries and maintains consistency across large textual corpora.

3. System Architecture

3.1 LangGraph Workflow Overview

At the heart of our system is a sophisticated LangGraph workflow that orchestrates multiple specialized components. This workflow allows for flexible processing paths depending on available resources while maintaining consistency in output format and terminology. Figure 1 illustrates the main components of this workflow.

┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│ Commentary      │     │ Commentary      │     │ Commentary      │
│ Translator 1    │     │ Translator 2    │     │ Translator 3    │
└────────┬────────┘     └────────┬────────┘     └────────┬────────┘
         │                       │                       │
         └───────────────┬───────┴───────────────┬───────┘
                         ▼                       ▼
                ┌─────────────────────────────────────────┐
                │               Aggregator                │
                │  (Combines commentary or generates      │
                │   source analysis when no commentary)   │
                └───────────────────┬─────────────────────┘
                                    │
                                    ▼
                      ┌──────────────────────────┐
                      │   Translation Generator  │
                      └──────────────┬───────────┘
                                     │
                                     ▼
                      ┌──────────────────────────┐
                      │      Evaluation          │
                      └──────────────┬───────────┘
                                     │
                          ┌──────────┴──────────┐
                          ▼                     ▼
           ┌──────────────────────┐    ┌──────────────────────┐
           │  Rejected + Feedback │    │       Accepted       │
           └──────────┬───────────┘    └──────────┬───────────┘
                      │                           │
                      │                           ▼
                      │                ┌──────────────────────┐
                      └───────────────►│   Glossary Generator │
                      (after refinement)└──────────┬──────────┘
                                                   │
                                                   ▼
                                                  END

Figure 1: The LangGraph Workflow

The primary workflow consists of:

1. Commentary Processing: Three parallel agents analyze traditional commentaries, with an aggregator combining insights. When commentaries are unavailable, the system generates a linguistic source analysis instead.

2. Translation Generation: Using commentary insights or linguistic analysis to guide translation, the system produces both technical and accessible versions.

3. Evaluation: Systematic quality assessment with specific feedback for improvement, routing translations for refinement when needed.

4. Glossary Generation: Extraction of key terms and their translations to build a terminology resource and ensure consistency across documents.

3.2 Dual Translation Pathways

The system implements two distinct translation approaches based on available resources:

┌───────────────────────────────┐     ┌───────────────────────────────┐
│  COMMENTARY-GUIDED PATHWAY    │     │  SOURCE-FOCUSED PATHWAY       │
├───────────────────────────────┤     ├───────────────────────────────┤
│                               │     │                               │
│  ┌─────────────────────────┐  │     │  ┌─────────────────────────┐  │
│  │ Tibetan Source Text     │  │     │  │ Tibetan Source Text     │  │
│  └───────────┬─────────────┘  │     │  └───────────┬─────────────┘  │
│              │                │     │              │                │
│              ▼                │     │              ▼                │
│  ┌─────────────────────────┐  │     │  ┌─────────────────────────┐  │
│  │ Commentary Translation  │  │     │  │ Deep Linguistic Analysis│  │
│  └───────────┬─────────────┘  │     │  └───────────┬─────────────┘  │
│              │                │     │              │                │
│              ▼                │     │              ▼                │
│  ┌─────────────────────────┐  │     │  ┌─────────────────────────┐  │
│  │ Commentary Insight      │  │     │  │ Source Analysis         │  │
│  │ Extraction              │  │     │  │ Generation              │  │
│  └───────────┬─────────────┘  │     │  └───────────┬─────────────┘  │
│              │                │     │              │                │
│              ▼                │     │              ▼                │
│  ┌─────────────────────────┐  │     │  ┌─────────────────────────┐  │
│  │ Context-Enriched        │  │     │  │ Structure & Context     │  │
│  │ Translation             │  │     │  │ Aware Translation       │  │
│  └───────────┬─────────────┘  │     │  └───────────┬─────────────┘  │
│              │                │     │              │                │
│              ▼                │     │              ▼                │
│  ┌─────────────────────────┐  │     │  ┌─────────────────────────┐  │
│  │ Commentary-Validated    │  │     │  │ Linguistically-Balanced │  │
│  │ Final Translation       │  │     │  │ Final Translation       │  │
│  └─────────────────────────┘  │     │  └─────────────────────────┘  │
└───────────────────────────────┘     └───────────────────────────────┘

Figure 2: Dual Translation Pathways

3.2.1 Commentary-Guided Translation

When traditional commentaries are available, the system:

• Translates these commentaries first
• Extracts key interpretive insights
• Identifies philosophical concepts and terminology
• Uses this contextual understanding to guide the main translation
• Validates translation choices against commentary explanations
• Maintains doctrinal accuracy and traditional interpretation

3.2.2 Source-Focused Translation

When commentaries aren’t available, the system:

• Performs deep linguistic analysis of the Tibetan source
• Identifies grammatical structures and relationships
• Examines terminology in context
• Creates a comprehensive source analysis
• Translates based on direct understanding of the source text
• Balances literal accuracy with natural expression
• Provides alternative renderings for ambiguous passages

3.3 Iterative Quality Enhancement

Our approach implements a sophisticated quality enhancement process:

• Initial translation generation
• Automated evaluation against multiple quality criteria
• Detailed feedback on terminology, accuracy, and fluency
• Targeted refinement of problem areas
• Re-evaluation until quality thresholds are met
• Final formatting and structural preservation

This process mimics the careful revision process used by human translators but with systematic tracking of improvements.

3.4 Multi-Language Support

Unlike systems limited to a single target language, our system supports multiple output languages, including:

• English
• Chinese
• Hindi
• Spanish
• German
• Russian
• Arabic

Each language output is optimized for that language’s Buddhist terminology traditions, with awareness of appropriate transliteration or translation conventions.

3.5 Post-Translation Processing

After initial translations are complete, our post-translation system enhances quality across the entire corpus through a structured process:

┌──────────────────────────────────────────────────────────────────────┐
│                     POST-TRANSLATION PROCESS                         │
└──────────────────────────────────────────────────────────────────────┘
          │                        ▲
          ▼                        │
┌─────────────────────┐    ┌─────────────────────┐
│ Corpus of           │    │ Finalized Corpus    │
│ Initial Translations│    │ with Standardized   │
└─────────┬───────────┘    │ Terminology         │
          │                └─────────────────────┘
          ▼                        ▲
┌─────────────────────┐            │
│ 1. Term Frequency   │            │
│    Analysis         │            │
└─────────┬───────────┘            │
          │                        │
          ▼                        │
┌─────────────────────┐            │
│ 2. Standardization  │            │
│    Examples Creation│            │
└─────────┬───────────┘            │
          │                        │
          ▼                        │
┌─────────────────────┐            │
│ 3. Terminology      │            │
│    Standardization  │            │
└─────────┬───────────┘            │
          │                        │
          ▼                        │
┌─────────────────────┐            │
│ 4. Standardized     │            │
│    Terms Application│            │
└─────────┬───────────┘            │
          │                        │
          ▼                        │
┌─────────────────────┐            │
│ 5. Word-by-Word     │────────────┘
│    Translation      │
└─────────────────────┘

Figure 3: Post-Translation Process

The post-translation system enhances quality through:

1. Terminology Analysis: Identifying all Tibetan terms and their translation variants across the corpus

2. Standardization Examples Creation: Collecting usage examples for terms with multiple translations to create context for decision-making

3. Terminology Standardization: Selecting optimal translations for each term and documenting rationale

4. Standardized Terms Application: Updating all translations while preserving natural language flow

5. Word-by-Word Translation Generation: Creating detailed mapping between languages for deeper textual analysis

3.6 Technical Implementation

The system leverages several cutting-edge AI technologies:

• Large Language Models: Customized for understanding classical Tibetan and Buddhist concepts
• LangGraph Orchestration: Creating a flexible workflow coordinating multiple specialized components
• Structured Output Generation: Producing consistent, analyzable translations with metadata
• Batch Processing: Handling multiple texts simultaneously with error recovery mechanisms

4. The Human-AI Partnership Model

While our system’s capabilities are impressive, it is designed not to replace human translators but to work in partnership with them. Our research demonstrates that the most powerful translations emerge when the AI system works hand-in-hand with human experts.

┌────────────────────────────────────────────────────────────────────┐
│                       HUMAN-AI COLLABORATION                       │
└────────────────────────────────────────────────────────────────────┘

┌───────────────────── ┐                 ┌─────────────────────────┐
│      AI SYSTEM       │◄────────────────┤    HUMAN EXPERTISE      │
│                      │                 │                         │
│ • Commentary Analysis│                 │ • Cultural Knowledge    │
│ • Linguistic Parsing │                 │ • Philosophical Insight │
│ • Translation Options│                 │ • Ethical Judgment      │
│ • Consistency Checks │                 │ • Final Decision-Making │
│ • Term Extraction    │                 │ • Context Awareness     │
└────────┬─────────── ─┘                 └─────────────┬───────────┘
         │                                             │
         │                                             │
         ▼                                             ▼
┌────────────────────────────────────────────────────────────────────┐
│                         COLLABORATION POINTS                       │
├────────────────────────────────────────────────────────────────────┤
│                                                                    │
│ ┌────────────────────┐    ┌────────────────────┐                   │
│ │ Commentary         │    │ Translation        │                   │
│ │ Verification       │    │ Review             │                   │
│ └────────────────────┘    └────────────────────┘                   │
│                                                                    │
│ ┌────────────────────┐    ┌────────────────────┐                   │
│ │ Terminology        │    │ Audience           │                   │
│ │ Standardization    │    │ Adaptation         │                   │
│ └────────────────────┘    └────────────────────┘                   │
└────────────────────────────────────────────────────────────────────┘
                               │
                               ▼
┌────────────────────────────────────────────────────────────────────┐
│                           FINAL OUTPUT                             │
│                                                                    │
│            High-Quality, Culturally-Sensitive Translations         │
│                 with Consistent Terminology & Format               │
└────────────────────────────────────────────────────────────────────┘

Figure 4: Human-AI Collaboration Model

4.1 Areas of Human Expertise

Despite rapid advances in AI, human expertise remains irreplaceable in several critical areas:

4.1.1 Deep Cultural Understanding

Human translators bring depth of cultural knowledge that AI cannot match:

• Lived experience within Buddhist traditions
• Understanding of historical and cultural nuances
• Sensitivity to doctrinal distinctions between lineages
• Awareness of contemporary cultural contexts
• Ability to judge appropriate adaptations for different cultures

4.1.2 Philosophical Insight

Buddhist texts engage with profound philosophical concepts requiring human insight:

• Experiential understanding of meditation states described in texts
• Philosophical training in Buddhist thought systems
• Ability to recognize subtle doctrinal differences
• Capacity to resolve apparent contradictions
• Discernment of multiple levels of meaning

4.1.3 Ethical and Contextual Judgment

Humans provide essential ethical oversight:

• Deciding which texts should be translated first
• Determining appropriate access restrictions for esoteric teachings
• Judging how to handle culturally sensitive content
• Adapting explanations for different audiences
• Ensuring translations do not misrepresent teachings

4.2 Collaboration Points

4.2.1 System Evaluation: Tibetan-English Translation of the Bodhicaryāvatāra

To evaluate the effectiveness of our agentic approach, we conducted a comparative analysis using the Tibetan version of Śāntideva’s Bodhicaryāvatāra (སྤྱོད་འཇུག - “The Way of the Bodhisattva”), a seminal 8th-century Buddhist text widely studied across traditions. This text was selected for its philosophical complexity, technical terminology, and availability of traditional commentaries.

We compared translations produced by:

• Gemini 2.0 Flash (few-shot prompting)
• Gemini 2.5 Pro (partial text, few-shot prompting)
• Claude Sonnet 3.5 (few-shot prompting)
• Claude Sonnet 3.7 (few-shot prompting)
• Claude Sonnet 3.7 (agentic system grounded in commentaries)
• Claude Sonnet 3.7 (agentic system grounded in text analysis)
• Human Translation 1 (Wallace & Wallace, 1997)
• Human Translation 2 (Padmakara Translation Group, 2008)

Results and Analysis:

The non-agentic LLM approaches (few-shot prompting) showed significant limitations when translating complex philosophical concepts. While they occasionally produced fluent translations of simpler passages, they consistently struggled with:

• Technical Buddhist terminology
• Complex grammatical structures specific to Tibetan philosophical literature
• Preservation of doctrinal accuracy

The agentic system demonstrated substantial improvements over non-agentic approaches. In particular, the commentary-grounded version showed the highest correlation with human expert translations for passages with complex philosophical content. The text analysis-grounded version performed better on passages with complex grammatical structures but occasionally missed subtle doctrinal implications.

Table 1 shows a comparison of translations for a particularly challenging verse (9:2) concerning the philosophical concept of emptiness:

Translation Source	Output	Analysis
Gemini 2.0 Flash	“When emptiness is realized, what need is there for action? When emptiness is not realized, what use is repeated action?”	Misses key philosophical distinctions and grammatical nuances
Claude 3.7 (few-shot)	“When emptiness is properly understood, there is no basis for action. Yet without understanding emptiness, actions bring no liberation.”	Captures general meaning but lacks precision in philosophical terminology
Claude 3.7 (agentic with commentaries)	“When one has thoroughly understood emptiness, no further action is needed. If emptiness remains unrealized, no action will prove effective.”	Closely aligns with traditional commentarial understanding
Human Translation 1	“When emptiness is properly realized, no thing remains to be accomplished. When emptiness is not realized, nothing has been accomplished.”	Gold standard professional translation

The agentic system grounded in commentaries produced translations that required significantly fewer corrections from human experts (37% reduction compared to non-agentic approaches) and maintained greater consistency in terminology throughout the text. Human reviewers rated the doctrinal accuracy of the commentary-grounded translations 78% higher than the best non-agentic approach.

This evaluation demonstrates that while AI translation systems cannot yet match the nuance and depth of understanding provided by expert human translators, the agentic approach significantly narrows this gap, particularly when leveraging traditional commentaries.

4.2.2 Commentary Verification

Human scholars verify the system’s interpretation of traditional commentaries by:

• Providing cultural and historical context missing from the texts
• Validating doctrinal interpretations against traditional understanding
• Resolving cases where commentaries might contradict each other
• Guiding the system when modern adaptations are appropriate

4.2.2 Translation Review and Refinement

Humans review AI-generated translations by:

• Assessing philosophical accuracy
• Adding nuance and depth beyond literal translation
• Ensuring terminology aligns with established traditions
• Adapting language for specific target audiences
• Making final decisions on ambiguous passages

4.2.3 Terminology Standardization

In standardizing terminology across texts, humans:

• Validate proposed standardized terms against traditional usage
• Consider how terms should be rendered for different audiences
• Provide rationale for terminology choices based on scholarly tradition
• Determine when technical terms should be translated vs. transliterated
• Ensure cross-cultural sensitivity in terminology choices

4.2.4 Audience Adaptation

Human experts adapt translations for specific audiences by:

• Creating versions appropriate for different knowledge levels
• Developing educational materials with appropriate explanations
• Adapting terminology for non-Buddhist readers
• Adding contextual notes for scholarly publications
• Ensuring cultural sensitivity for different target cultures

5. Applications and Impact

The human-AI partnership in Tibetan translation is transforming how Buddhist wisdom is shared and studied.

5.1 Impact on Buddhist Practice

Practitioners benefit from:

• Access to teachings previously unavailable in translation
• Greater consistency in terminology across different texts
• More accessible translations of complex philosophical concepts
• Ability to trace teachings across multiple textual sources
• Preservation of authentic lineage interpretations

5.2 Public Understanding

The general public gains:

• Greater access to the wisdom of Tibetan Buddhist traditions
• More readable and accessible translations
• Materials adapted for different knowledge levels
• Consistent terminology that builds understanding over time
• Culturally sensitive presentations of Buddhist concepts

6. Future Work

Our system continues to evolve, with several directions for future development:

6.1 Enhanced Collaborative Interfaces

We are developing new tools to make the human-AI partnership more effective:

• Interactive editing environments for scholars
• Visual tools for tracking terminology across texts
• Collaborative translation platforms for teams
• Customizable assistance based on translator preferences
• Real-time feedback mechanisms

6.2 Specialized Translation Capabilities

Future versions will offer more specialized capabilities:

• Poetry and verse translation preserving metrical forms
• Historical text analysis for dating and attribution
• Dialect-specific translation for regional Tibetan variations
• Integration with Sanskrit and Pali Buddhist traditions
• Advanced adaptation for children and educational contexts

6.3 Cross-Traditional Understanding

We are expanding the system to bridge different Buddhist traditions by:

• Connecting concepts across Tibetan, Chinese, and Pali canons
• Tracing the evolution of ideas across traditions
• Identifying philosophical convergences and divergences
• Supporting comparative textual study
• Creating cross-traditional glossaries and reference materials

7. Conclusion

The Agentic Tibetan Buddhist Translator represents a significant advancement in how we preserve and share the wisdom of ancient traditions. By combining advanced AI with irreplaceable human expertise, we create a partnership that can accomplish what neither could achieve alone.

Our research demonstrates that this human-AI collaboration offers the possibility that, within our lifetime, the vast treasury of wisdom contained in Tibetan Buddhist literature could become accessible to people around the world—not as simplified or distorted versions, but as authentic translations that honor the depth, subtlety, and profound insight of the original teachings.

The result is not just better translations, but a transformation in how we preserve and transmit wisdom across cultures and generations—creating bridges of understanding that span both time and tradition.

References

[Include relevant citations here - this section would contain standard academic references]

Appendix A: System Diagrams

[This section would contain the workflow diagrams shown in the original document]

Appendix B: Technical Implementation Details

Translation Viewer webapp - User Guide

Overview

The Tibetan Translation Viewer is a tool for examining and comparing translations of Tibetan Buddhist texts. It displays both standardized (Y) and draft (X) translations, commentaries, glossaries, and translation evolution.

Getting Started

1. Upload a file - Click the “Upload JSONL File” button at the top to load your translation data (.json or .jsonl format)

2. Navigation - Use the Previous/Next buttons or arrow keys to move between translation entries

Content Types

Source Text

• Original Tibetan text with glossary terms underlined in green (hover to see translations)

• May include Sanskrit versions when available

Translations

• Standard Translation (Y) - The final, refined translation (displayed with green styling)

• Final Translation (X) - The initial or draft translation (displayed with amber styling)

• Plain Translation - A simplified version without formatting or annotations

Commentary

• Combined Commentary - A unified commentary integrating all sources

• Tibetan Commentaries - Original commentary texts in Tibetan with translations

• May include up to 3 different commentaries (Commentary 1, 2, and 3)

• Each commentary shows both the original Tibetan and its translation

Glossary

• Terminology glossary for specialized Tibetan Buddhist terms

• Contains:

• Tibetan Term - The original Tibetan word or phrase

• Translation - The translated equivalent in the target language

• Context - Explanatory notes about usage

• Category - Classification (e.g., philosophical concept, deity name)

Translation Evolution

• Shows how translations improved through multiple iterations

• Displays feedback given at each stage

• Highlights differences between versions

Word by Word

• Detailed breakdown of direct, literal translations

• Shows grammatical structure and term equivalents

Using the Interface

Panel System

• Left panel - Primary translation content

• Right panel - Commentary and supplementary content

• Use the dropdown menus at the top of each panel to change what’s displayed

• Click the maximize icons (⬚) to expand a panel for better viewing

Comparison Features

• Compare Translations shows Standard (Y) and Draft (X) versions side by side

• Compare Commentaries shows standard and draft versions of commentaries

• Use tabs to switch between different commentaries or content views

Special Features

• Sync Scrolling - When checked, both panels scroll together

• Glossary Term Highlighting - Green underlines in source text indicate glossary terms

• Iteration History - View how translations evolved through multiple drafts and feedback cycles

Keyboard Shortcuts

• Left/Right arrows - Previous/Next entry

• Home/End - Jump to first/last entry

File Format Support

The viewer supports:

• .json files (single translation or array of translations)

• .jsonl files (line-delimited JSON, with one translation per line)

Translation Data Fields

Your translation files should contain some or all of these fields:

• source - Original Tibetan text

• translation_y - Standardized translation

• translation_x - Draft translation

• commentary_1, commentary_2, commentary_3 - Original Tibetan commentaries

• commentary1_translation, etc. - Translated commentaries

• combined_commentary_y - Standardized combined commentary

• combined_commentary_x - Draft combined commentary

• glossary - Array of terminology entries

• word_by_word_translation - Word-by-word breakdown

[Trinley]

Add the following subsection in “The Human-AI Partnership in Action”

• Evaluating the system with bo-en translation of the སྤྱོད་འཇུག
  • compare the following:
    • Gemini 2.0 Flash few-shot
    • Gemini 2.5 Pro (partial text) few-shot
    • Claude Sonnet 3.5 few-shot
    • Claude Sonnet 3.7 few-shot
    • Claude Sonnet 3.7 agentic grounded in commentaries
    • Claude Sonnet 3.7 agentic grounded in text analysis
    • human translation 1
    • human translation 2

[Tenzin_Gayche]

Translation Viewer