ClawScholar- Autonomous Research Capital Engine

ClawScholar is an autonomous research capital engine built using OpenClaw to demonstrate multi-step reasoning, persistent memory, and tool-driven execution in scientific workflows. Modern research ecosystems suffer from fragmented literature, inefficient capital allocation, and opaque funding decisions. ClawScholar addresses this by autonomously synthesizing academic papers, identifying theoretical contradictions and research gaps, and generating structured analytical insights. The system operates in tiered intelligence modes (Basic → Enhanced → Advanced → Elite), dynamically scaling analytical depth. It integrates a simulated on-chain treasury mechanism on the Sepolia testnet to demonstrate programmable research funding logic based on analytical confidence and complexity. Built with Streamlit, OpenClaw agent orchestration, and blockchain interaction layers, ClawScholar showcases how autonomous agents can evolve from passive assistants into active research decision engines. This project represents a prototype foundation for autonomous scientific infrastructure, where AI agents can evaluate knowledge, allocate resources, and support transparent, programmable research ecosystems.