AI research has long treated mathematics as the foundation of reasoning. A quieter school of thought — through Gödel, Friedman and others, often overlooked in computing — works directly with logic for reasoning. In this view, you can reason about an idea before the mathematics to express it exists. Ideas can be true before we have the math to represent them — even Ramanujan would agree.
Bhārat (India) has long treated logic, not mathematics, as more foundational — and invested deeply in it. Not one school but many: Kapila's samkhya of transformations, Gautama's nyāya of inference, Rishabhanatha's syādvāda of context-dependent attribution, Siddhartha's madhyamika approach to catuṣkoṭi (true, false, both, neither), and many more. Debated and sustained across decades, centuries, millennia, and often ages through living traditions and institutions with generations of inquiry and commentaries. Never assuming that questions must collapse cleanly into true or false. Continuity, uncertainty, contradiction, and context are treated as objects of study rather than problems to be eliminated.
Meanwhile, the modern world with newfound wealth developed computing around binary logic, from Boole and Shannon. That choice shaped history. But it was a choice.
Today, the world is churning again.
AI keeps hitting the limits of true/false in representing judgment, nuance and partial truth. At the same time, computing is moving beyond digital assumptions — toward dynamical, continuous and stateful substrates. While logical frameworks used in ancient knowledge traditions show ways to reason using more than just true and false.
We see convergence.
Ancient knowledge. Artificial intelligence. Dynamic computation.
Modern computing came from one logical tradition. As AI and new computational paradigms expose the limits of inherited assumptions, we are interested in what becomes possible when a broader landscape of logic is brought back into the conversation.
The space between them is worth researching. We are inviting researchers to an open, multi-horizon research-to-build program — to write in the gaps, connect the a priori to the present, publish, invite criticism, and build the theses that bridge these domains. Philosophy studies logic, AI studies systems, computing studies implementation. We are interested in what emerges when those boundaries disappear.
From Bhārat to the world.
3 Tracks
Knowledge Theory
Logical frameworks practiced in Bhārat — like nyāya, syādvāda, madhyamika — treat many-valued and graded truth as native, not as noise to resolve. These are highly structured logical foundations for reasoning in their own right. Most AI research focuses on improving models, algorithms, and mathematics. Much less attention is paid to the logical assumptions underneath them. We are interested in that layer. In what becomes possible when alternative logical foundations are treated not as philosophy, but as engineering.
Artificial Intelligence Active: 1
Most AI systems today inherit their foundations from statistical mathematics, and with it the assumptions of binary logic. We ask whether foundations can start from logic instead, with mathematics as its representation — including whether frameworks like catuṣkoṭi and syādvāda give rise to stable evaluative structures and how embedding them might change what AI can represent, detect and optimize.
Dynamic Computation
If logic shapes mathematics and mathematics shapes computation, different logic may imply different forms of computation. We investigate architectures for non-transistor substrates like photonics and networks — and what they reveal about intelligence itself.
How We Work
We write. In the blank spaces. Publish. Share notes. Speak. Present. Debate. Until we know enough.
Then we build. 🚧