Document-to-Training Pipeline

Vision

User uploads a document (PDF, book, manual). LARS automatically: 1. Extracts content to Corpus 2. Generates training data from content 3. Trains itself via verified loop 4. Can answer questions from both training AND retrieval

The Two-System Approach

System A: Immediate Access (RAG)

PDF → NLM Ingestor → Corpus (stable ID) → LARS queries at runtime

• Instant availability
• Exact quotes and page numbers
• No training required
• Limited to what's in context window

System B: Deep Learning (Training Loop)

Corpus Content → Generate Q&A pairs → Nexus Training Loop → LARS internalizes

• Takes time (background process)
• Concepts become part of LARS's weights
• Reasoning and synthesis capabilities
• No context window limit for learned concepts

Combined at Inference

User Question → LARS
                  ├→ Trained knowledge (concepts, reasoning)
                  └→ Corpus retrieval (exact quotes, citations)
                  → Synthesized Answer

Pipeline Steps

Step 1: Document Ingestion

• PDF uploaded to docs environment
• NLM Ingestor extracts text, maintains structure
• Content stored in Corpus with stable ID
• Metadata: page numbers, chapters, sections

Step 2: Dataset Generation

• AI (Claude or trained LARS) reads Corpus content
• Generates Q&A pairs covering:
• Factual recall (What does chapter 5 discuss?)
• Comprehension (Summarize the main argument)
• Application (How would you apply this concept?)
• Citation (Where is X mentioned?)
• Output: Training dataset linked to stable ID

Step 3: Training Loop

• Nexus Training Loop processes dataset
• Claude evaluates LARS responses
• Corrections generated for failures
• Loop until 98%+ accuracy

Step 4: Verification

• Test suite generated from document
• LARS must pass before considered 'trained'
• Store verification results with document ID

Honest Limitations

What Training CAN Do

• Learn concepts and relationships
• Understand document's ideas deeply
• Reason about content
• Connect ideas across chapters
• Answer synthesis questions

What Training CANNOT Do

• Exact positional recall ('4th word on page 98')
• Perfect verbatim quotes without retrieval
• Remember every detail equally

Solution: Hybrid Approach

For exact recall → Query Corpus For understanding → Use trained knowledge User sees unified experience

Proprietary Differentiator

"Dynamic Knowledge Integration with Verified Learning Loops"

• Not just RAG (retrieval)
• Not just fine-tuning (one-shot training)
• Continuous learning with verification
• Document becomes part of AI, not just reference material
• Every piece of learned knowledge is validated

Implementation Requirements

1. NLM Ingestor integration (exists)
2. Corpus storage with stable IDs (exists)
3. Q&A generation from documents (needs building)
4. Training loop orchestrator (needs building)
5. Verification test generator (needs building)
6. Hybrid inference router (needs building)