NECoRT: Nash-Equilibrium Chain of Recursive Thoughts

NECoRT is a framework for multi-agent recursive reasoning inspired by game theory. It builds on Chain of Recursive Thought (CoRT) by introducing multi-agent deliberation and Nash equilibrium convergence to surface robust, self-consistent answers from language models.

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🧠 Concept

Layer	Description
🧩 CoRT	A single model recursively evaluates, refines, and verifies its own answers
🤖 NECoRT	Multiple agents generate and critique responses, converging on consensus
🎯 Nash Layer	Final response is only accepted if no agent can unilaterally improve it

The result is a form of negotiated truth — answers that survive scrutiny from multiple angles and stabilize into a response that’s not just correct, but resilient.

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🚀 Why It Matters

Traditional LLMs are one-shot, error-prone, and overly confident.

Method	Pros	Cons
LLM	Fast	Shallow, brittle answers
CoRT	Thoughtful, iterative refinement	Still single perspective
NECoRT	Diverse, verified, stable reasoning	Higher compute cost

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🔧 How It Works

1. Agents propose solutions
2. Utility Matrix is built by agents critiquing each other’s answers
3. Nash Equilibrium is reached when no agent can unilaterally improve the outcome
4. Final answer is extracted from the most stable agent response

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📁 Project Structure

File / Folder	Purpose
nash_recursive_thinking.py	Core NECoRT logic — agent loop, evaluation, equilibrium checks
necort_web.py	Web interface backend for NECoRT
recthink_web.py	Web interface for classic CoRT
frontend/	React-based UI
start-necort.bat	Launch NECoRT locally (Windows)
requirements.txt	Python dependencies
rec.PNG, non-rec.png	Visual examples of CoRT vs non-CoRT outputs

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💻 Quickstart

# Windows
start-necort.bat

# Linux / Mac
pip install -r requirements.txt
cd frontend && npm install && npm start
cd ..
python necort_web.py