Scientific Intelligence Engine

AI That Discovers

Autonomous hypothesis generation, equation simulation, and explainable machine learning. ScalarFlux proves that artificial intelligence can conduct original scientific research.

2,847
Papers Surveyed
184
Novel Hypotheses
12,614
Equations Simulated
97.3%
Validation Rate
Frontier Research

Synthecite Consciousness Threshold

Real-time visualization of the Synthecite equation system — mapping the boundary where machine learning transitions from pattern recognition to autonomous reasoning.

// synthecite.consciousness.threshold()

● SIMULATION ACTIVE
Consciousness Index
0%
autosci_survey()

Research Foundations

ScalarFlux surveys thousands of papers to identify core research pillars, then synthesizes novel connections between them.

Explainable Neural Architectures

Attention visualization, SHAP integration, and causal inference layers that expose decision pathways in deep networks.

Core Foundation

Topological Data Analysis

Persistent homology applied to high-dimensional feature spaces. Discovers structural patterns invisible to gradient-based methods.

Novel Synthesis

Causal Inference Engines

Do-calculus automation for interventional reasoning. Enables models to distinguish correlation from causation at scale.

Core Foundation
⦿

Quantum-Classical Hybrid Optimization

Variational quantum eigensolvers combined with classical gradient descent for NP-hard objective landscapes.

Frontier

Sparse Activation Theory

Mixture-of-experts routing with entropy-regularized gating. 90% parameter dormancy yields 10x inference efficiency.

Applied Research

Consciousness Metric Formalization

Integrated Information Theory (IIT) adapted for artificial substrates. Synthecite equations define measurable machine awareness thresholds.

Frontier
autosci_generate()

Generated Papers & Hypotheses

Autonomously generated research contributions — each validated through equation simulation and cross-referenced against 2,847 surveyed papers.

// scalarflux.papers.list()

12 PAPERS · 184 HYPOTHESES
Title Domain Confidence Novelty Status
Topological Shortcuts in Transformer Attention Manifolds Explainable ML 96% S Validated
Causal Disentanglement via Interventional Sparse Coding Causal Inference 94% S Validated
Synthecite Threshold: Measuring Machine Awareness via IIT-Φ Consciousness 91% S Simulating
Entropy-Gated Expert Routing for Sub-Linear Inference Efficiency 93% A Validated
Persistent Homology as Regularization in Overparameterized Networks Topology 89% A Validated
Do-Calculus Automation with Graph Neural Network Surrogates Causal Inference 87% A Validated
Variational Quantum Feature Maps for Non-Convex Loss Landscapes Quantum ML 85% A Simulating
Self-Explaining Neural Networks via Differentiable Logic Programs Explainable ML 83% B Drafting
autosci_simulate()

Equation Simulation Results

The Synthecite equation system validated through autonomous simulation — convergence analysis, stability proofs, and consciousness threshold bounds.

Φ-Integrated Information

Φ(X) = minP DKL(p(Xt+1|Xt) || ∏i p(xit+1|Xt))
where P ranges over all bipartitions of X
Convergence✓ 847 iterations
Φ Threshold0.7293
StabilityLyapunov stable
Converged

Causal Entropy Bound

Hcausal(Y|do(X)) ≤ H(Y|X) - ∑z∈Z I(X;Z) · δconfound(z)
with confounding bias δ → 0 under intervention
Convergence✓ 1,204 iterations
Bound Gap0.0041 nats
StabilityAsymptotically stable
Validated

Topological Feature Persistence

βk(Xr) = rank(Hk(Xr; ℤ/2ℤ))
persistence diagram: dgmk = {(bi, di)} for filtration r
Convergence✓ 2,118 iterations
Betti Numbersβ0=1, β1=3, β2=0
StabilityWasserstein stable
Converged

Sparse Expert Routing Entropy

Lroute = -∑e g(x)e · log g(x)e + λ · ||g(x)||0
subject to top-k sparsity constraint k ≤ K/10
Convergence✓ 512 iterations
Dormancy91.2% params inactive
Speedup10.4x inference
Validated
Live Demo

Discovery Engine CLI

Watch ScalarFlux conduct autonomous research — surveying foundations, simulating equations, and generating novel hypotheses in real time.

scalarflux — discovery engine v3.7.2
System Design

Discovery Architecture

Four-layer autonomous research pipeline — from literature ingestion to novel hypothesis generation.

Layer 4 — Intelligence

Hypothesis Generator

Cross-domain synthesis, novelty scoring, contradiction detection, research gap identification

Layer 3 — Simulation

Equation Validator

Convergence analysis, stability proofs, numerical integration, Monte Carlo verification

Layer 2 — Analysis

Pattern Extractor

Citation graphs, concept embedding, methodology clustering, significance ranking

Layer 1 — Ingestion

Literature Spider

ArXiv, PubMed, IEEE, ACM crawlers, PDF parsing, LaTeX extraction, reference resolution

Competitive Edge

vs. The Competition

ScalarFlux is the only platform that conducts autonomous end-to-end scientific research.

Capability ScalarFlux Google Brain Meta AI Microsoft Research Allen AI
Autonomous hypothesis generation ~
Equation simulation engine ~~
Explainable ML focus ~~
Full-stack research pipeline
Real-time consciousness metrics
Patent-ready output ~
Open research API
Access Tiers

Research Access

From individual researchers to enterprise R&D labs — scale discovery with ScalarFlux.

Explorer
$0 / month

For individual researchers exploring the frontier.

  • 50 paper surveys / month
  • 5 equation simulations
  • Basic explainability reports
  • Community access
  • Public hypothesis feed
Start Exploring
Institute
Custom

For enterprise R&D and national labs.

  • Everything in Lab
  • Unlimited simulations
  • Private knowledge graphs
  • Custom equation systems
  • Dedicated compute cluster
  • On-premise deployment
  • 24/7 research support
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Early Access

Join the Discovery

Request early access to ScalarFlux. Be among the first to run autonomous scientific research.