SynthMind
Multi-modal reasoning engine for complex document analysis.
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The Thesis
Market Context
The intelligent document processing market is projected to reach $12B by 2028. Current solutions handle simple extraction but fail on reasoning-heavy tasks — cross-referencing clauses, identifying contradictions, or synthesizing across multiple documents.
Hypothesis
Current LLMs fail at multi-step reasoning over long, visually complex documents. We hypothesize that a pipeline combining specialized vision models with chain-of-thought prompting can achieve 3x better accuracy on document QA benchmarks.
Technical Challenges
Long-Context Reasoning
Documents exceeding 100 pages overflow context windows. We built a hierarchical summarization pipeline that creates document graphs, enabling targeted retrieval of relevant sections for each reasoning step.
Visual Layout Understanding
Tables, charts, and multi-column layouts confuse standard OCR. We fine-tuned a layout-aware vision model to segment document regions before text extraction, improving table accuracy from 62% to 94%.
System Design
- 01. Ingestion: PDF/Image Parser + Layout Segmentation
- 02. Vision: Fine-tuned LayoutLM + Custom Table Extractor
- 03. Reasoning: LangGraph Multi-Step Pipeline
- 04. Output: Structured JSON with Source Provenance
Outcomes
Achieved 87% accuracy on multi-hop document QA benchmarks, outperforming GPT-4V baseline by 23%. Processing 500+ pages/minute with structured output.
Research Roadmap
Benchmark suite and baseline models
Multi-document reasoning pipeline
API launch and SDK release
Other Experiments
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