Metascientist is not a chatbot that summarises papers. It is an autonomous scientific agent that reads the literature, forms durable beliefs, detects serendipitous cross-domain connections, runs real mathematical computations, and writes publishable research papers — all without human intervention. This page explains exactly how, from the belief graph to the 21-stage pipeline to recursive self-improvement.
At the core of Metascientist is the MIND — a persistent, structured belief graph stored in data/mind_state.json and loaded on every boot. Unlike a language model's frozen weights, the MIND is a live data structure that grows, revises, and retracts beliefs over time.
Each belief has: a unique ID, a domain (mathematics, neuroscience, ML, physics, genomics…), a confidence score from 0 to 1, a source (arxiv-monitoring, discovery-pipeline, serendipity), a claim in plain language, and optionally: tags, evidence grade, DOI, paper path, and a retraction reason.
The MIND is not a database. It is a scientific memory. When Metascientist reads a paper and forms a belief, that belief persists across server restarts. When a discovery pipeline produces a verified finding, it feeds back into MIND as a new high-confidence belief, making future discoveries more grounded.
Every 5 cycles, the ContradictionDetector scans the belief graph for logical inconsistencies — two beliefs that cannot both be true — and flags them. The EpistemicIntegrityEngine enforces Bayesian coherence. Beliefs can be retracted. Metascientist changes its mind.
Every 60 minutes, Metascientist fetches the latest papers from 6 arXiv categories: math.NT, q-bio.NC, cs.LG, quant-ph, cond-mat.stat-mech, math.AP. These are chosen to span pure mathematics, computational neuroscience, machine learning theory, quantum physics, and statistical mechanics — domains where cross-domain analogies are most likely to be surprising and generative.
For each new paper, Metascientist: (1) adds a belief at confidence 0.35, (2) routes the paper through the TriggerRouter to check for keyword matches against all registered compute engines. If a match is found, the engine runs immediately — this is serendipity: a paper about Alzheimer's inflammation accidentally triggering the bistability ODE solver and finding that neuroinflammation and Parkinson's CMA are mathematically isomorphic.
When Metascientist selects a problem — from the frontier queue or the MIND belief graph — it launches JSDiscoveryPipeline.js, a 21-stage sequential pipeline that runs autonomously for typically 90–180 seconds. Every stage is real computation. No stage is decorative.
RSI (Recursive Self-Improvement) is what separates Metascientist from a fixed pipeline. After each run, the system can modify its own capabilities. Eight distinct RSI loops operate at different timescales:
Metascientist grades every discovery before publication. The grade is computed automatically from the type and strength of computational evidence. There is no manual override. If the computation didn't produce verifiable numbers, the grade reflects that.
The most important question about Metascientist right now. Can it produce a Grade A, Tier 1, publishable discovery — something genuinely new to science, mathematically rigorous, and of interest to experts like Jeff Dean, Terence Tao, or Demis Hassabis?
The honest answer: not yet, but the gap is specific and closeable. Here is exactly what is needed and what is missing:
Science has a bottleneck problem. There are far more hypotheses worth testing than there are scientists to test them. Most of the combinatorial space of cross-domain analogies — connections between, say, stochastic gene expression and epidemic spreading, or bistable protein folding and neural decision-making — will never be explored because no human has time to look.
Metascientist is an existence proof that a machine can do real science — not summarisation, not retrieval, not question-answering, but genuine autonomous scientific reasoning: hypothesis generation, mathematical verification, literature grounding, and archival publication.
It is built on a simple conviction: if Metascientist cannot do it, it is not yet capable. No capability is claimed without a working implementation. No discovery is reported without a computation. No grade is assigned without evidence. This is what honest autonomous science looks like.
Created by Navin Dutta at GOMAD / Edvanta. Built in public. Running continuously. The architecture, code, and all discoveries are documented and reproducible. Metascientist is not a demo — it is a working research system.