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** — model weights + inference code

👉 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