README.md

# EVAM Lab 🪷

**Ancient logic. Modern AI. Built to know what it doesn't know.**

We implement Buddhist formal logic as ML infrastructure — rigorous epistemological frameworks developed over 1,500 years, now running on GPUs.

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## Phase A: Apoha — Available Now

> *"A concept is not defined by what it includes, but by what it excludes."*
> — Dignāga, c. 480–540 CE

**Apoha** is a few-shot classifier that defines concepts by their **exclusion sets** — examples of what they are NOT. Inputs that don't clearly belong anywhere receive `UNCERTAIN` instead of a forced label. OOD rejection is built into the architecture, not bolted on after.

| Benchmark | Apoha OOD Rejection | Best Baseline | Improvement |
|-----------|-------------------|---------------|-------------|
| CLINC150 (30 intents) | 84.6% | 15.1% (InfoNCE) | **5.6×** |
| Banking77 (50 intents) | 29.1% | 0.0% (InfoNCE) | **∞** |
| HWU64 (40 intents) | 24.9% | 0.2% (InfoNCE) | **166×** |
| Cybersecurity (15 TTPs) | 45.2% | 1.2% (InfoNCE) | **38×** |

👉**[apoha-bge-small-en-v1.5](https://huggingface.co/EVAMLab/apoha-bge-small-en-v1.5)** — model weights + inference code

👉 **[apoha-demo](https://huggingface.co/spaces/EVAMLab/apoha-demo)** — live interactive demo

📄 **Preprint:** ArXiv *(coming soon)*

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## Roadmap

| Phase | Module | What it does |
|-------|--------|-------------|
| **A ✅** | Apoha | Rejects OOD *inputs* — classifier knows what it doesn't know |
| **B 🔨** | Hetuchakra | Verifies LLM *outputs* — formal argument gate |