This repo will be for the scientific implementation of maths, physics and ML research theoretical or experimental implementations

The core idea will be like: Hypothesis → Plan → Execute → Analyze → Critique → Iterate

🧪 MASL — Multi-Agent Science Lab

MASL is a framework for building autonomous, multi-agent scientific workflows that follow the actual scientific method:

Hypothesis → Experiment → Analysis → Critique → Iteration

Unlike typical “AI agent” systems, MASL is designed to:

• run real experiments (symbolic math, physics simulations, ML training)
• enforce falsification and termination
• separate reasoning from execution
• remain reproducible and inspectable

MASL is not a chatbot.
It is a scientific instrument.

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✨ Key Features

• 🧠 Role-specialized agents

  • Hypothesis Generator
  • Experimental Planner
  • Executor (tool-only, no reasoning)
  • Analyzer (domain-aware)
  • Critic / Peer Reviewer

• 🔌 Pluggable tool system

  • Symbolic mathematics (SymPy)
  • Numerical physics simulators
  • ML training pipelines
  • External APIs (optional)

• 🔁 Critic-driven iteration

  • Experiments stop when falsified
  • Methodological flaws are detected automatically

• 📜 Full experiment ledger

  • Prompts, plans, tool calls, results, critiques
  • Every run is reproducible

• 🧪 Multi-domain by design

  • Mathematics
  • Physics
  • Machine Learning
  • Same engine, different tools

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🧠 Design Philosophy

MASL is built on five non-negotiable principles:

1. Falsification over fluency

   • If a hypothesis cannot fail, it is rejected.

2. Execution ≠ Reasoning

   • LLMs never execute experiments.
   • Tools never reason.

3. Deterministic science

   • Numerical checks beat textual explanations.

4. Critique controls progress

   • The critic decides when to stop or iterate.

5. No agent theatre

   • Every agent must justify its existence.

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🧩 System Architecture