We build autonomous agents that reason over distributed, high-stakes data (Bio & Finance) without ever centralizing it. We replace trust with cryptography and replace correlation with causal/structural reasoning.

If you are interested in any of this project, please feel free to contact me. :)

Causal & Geometric Reasoning

We move from “Probabilistic Token Prediction” to “Structural Reasoning.” Our agents generate solutions constrained by geometric symmetry (Physics) and causal graphs (Logic).

The Structural Inference Engine (SIE)

The Moonshot: Standard LLMs are probabilistic—they ‘guess’ the next token. We are building the Deterministic Structural Engine—an LLM that generates data (molecules, contracts, causal graphs) that is mathematically guaranteed to obey the strict schema and semantic constraints of the real world.

The Hard Problem: In high-stakes domains, “mostly correct” is a failure. A synthetic patient record cannot have disjoint medical codes (e.g., “Viral Pneumonia” without “Infectious Disease”). A financial contract cannot violate regulatory schemas. Current methods (RAG or Fine-tuning) offer “soft” guidance but cannot enforce hard logical consistency.

• The Blueprint Layer(Factor Graph Priors): Instead of sampling tokens directly, the engine first samples a target subspace (a configuration of attributes) using a Probabilistic Factor Graph.
• The Constraint Compiler (The Hard Mask): Compile the domain’s rigid rules into Deterministic Finite Automata (DFA). During inference, these automata project a “Hard Mask” onto the LLM’s vocabulary. Any token that would violate the schema is mathematically assigned zero probability.
• The Semantic Verifier (The Loop): A token-level verifier runs in parallel with the generation. It checks candidate facts (Subject-Relation-Object) against a retrieved Knowledge Graph in real-time.

The Inter-Graph Reasoning Protocol (IGRP)

The Moonshot: The “Internet of Knowledge”—A protocol where agents can “walk” across millions of decentralized, private Knowledge Graphs (KGs) to discover hidden causal chains, without ever moving the data to a central server.

The Hard Problem: Knowledge is fragmented. Hospital A has a patient graph. University B has a gene-regulatory graph. Pharma C has a chemical-structure graph. Currently, to discover a cure, you have to centralize all three (Impossible due to Privacy/IP). Standard Agents cannot “reason” across these silos; they hallucinate connections that don’t exist.

• The “Bridge” Nodes (Entity Alignment) Privacy-Preserving Entity Alignment (hashed embeddings) to identify “Bridge Nodes” (e.g., Gene TP53 exists in both the Hospital KG and the University KG). The system creates a virtual “Hyper-Edge” connecting these disparate graphs, creating a unified logical surface without data sharing.
• The “Walker” Agent (Neural Pathfinding) Instead of RAG (retrieving text), the agent executes a Multi-Hop Reasoning Walk.
• The Compositional Proof The Agent bundles these distributed hops into a Cryptographic Trace. It presents the final answer (“Molecule Y cures Patient X”) along with the verified path of reasoning, which can be audited without revealing the full graphs.