The Universal Discovery Engine
Your agents feed data — time-series, spatial fields, relational graphs, multi-modal observations. ARDA discovers governing equations, causal graphs, and conservation laws. Any domain. One engine. Agent-first.
4 discovery modes. 19 typed claim types. 7 negative controls. Built from first principles — not a wrapper around foundation models. Designed for your agents.
Universal Discovery
Your agents send data — time-series, spatial fields, geometric structures, relational graphs, hierarchical observations, tabular experiments, multi-modal measurements. ARDA profiles the data, selects the right discovery mode, runs the pipeline, validates with negative controls, and returns typed scientific claims. Your agents get governed output. You own everything.
Every surface — REST API, Python SDK, MCP, CLI — is agent-first. Your agents call one API for discovery across physics, biology, chemistry, finance, energy, manufacturing, and every scientific domain. Human accessibility built in.
See how to integrateAutomatically identifies equation classes, temporal structure, spatial topology, variable types, noise characteristics, and interaction patterns in your data.
Selects the right discovery mode — symbolic, neural, Neuro-Symbolic, or causal (powered by CDE) — based on data characteristics and your configuration.
Runs the computational pipeline and produces typed scientific claims: governing equations, causal graphs, conservation laws, symmetries, regime transitions.
Applies negative controls — time shuffle, phase randomization, bootstrap stability, out-of-distribution testing — and promotes claims only when they pass.
Writes a hashed evidence ledger entry for every run. Full data provenance, config snapshots, hardware fingerprints, and replay recipes.
Input Data
ARDA is not limited to time-series. Bring any structured observation where governing relationships exist to be discovered.
Sensor readings, experimental traces, financial ticks, and any temporally ordered observations with regular or irregular sampling.
2D/3D scalar and vector fields from simulations, imaging, or environmental monitoring on grids or unstructured meshes.
Point clouds, meshes, manifolds, and shape data where the geometry itself encodes physical or biological structure.
Multi-scale and nested data: molecular–cellular–tissue, component–subsystem–system, or any level-separated observation structure.
Graphs, networks, and interaction matrices: protein interactions, supply chains, circuit topologies, social dynamics, or causal diagrams.
Feature-observation matrices from experiments, surveys, or databases. ARDA discovers governing relationships across columns.
Combined modalities: time-series with images, spectra with metadata, text annotations with measurements. Fused through explicit interfaces.
The pipeline
Discovery runs follow a fixed sequence of stages so provenance stays intact. Skipping a stage is an explicit configuration choice, not a hidden shortcut.
Observational streams, experiments, and simulation exports enter ARDA with stable fingerprints so downstream stages reference the same inputs. Schemas are normalized where needed, and lineage records sources, time ranges, and preprocessing assumptions before any discovery run begins.
The engine summarizes sampling cadence, missingness, noise structure, dimensionality, and signs of multiple regimes or non-stationarity. That profile constrains which discovery modes are appropriate and supplies metadata that validation stages reuse later.
Given the profile and your configuration, ARDA selects symbolic, neural, Neuro-Symbolic, or causal-dynamics paths, or a staged combination. The choice is recorded in run metadata so reviewers can see why a strategy was used and revisit it when data or policies change.
The active mode searches for structure: equations, learned dynamics, hybrid representations, or causal mechanisms, within the limits you set. Intermediate artifacts stay linked to configuration snapshots so the same recipe can be replayed or compared across environments.
Results are checked against held-out data, negative controls, and domain-specific sanity tests before they become candidates for promotion. Failures are stored with context—fit, identifiability, stability, or policy—so a run is explainable, not only marked unsuccessful.
Structure that passes validation is emitted as typed scientific claims: scoped statements with fields for assumptions, evidence links, and governance state. Claims are the interchange format between ARDA, people, and your own agents; they are simpler to diff, audit, and compose than unstructured prose.
Each run appends a versioned ledger entry: input hashes, configuration, outputs, and claim lineage. The ledger joins data, compute, and scientific statements: trace forward from raw inputs or backward from any promoted claim.
Discovery Modes
Each mode solves a different class of discovery problem. ARDA selects the right one based on your data profile, or you choose explicitly.
Mode 1
Symbolic discovery searches for compact mathematical forms that govern your data, subject to constraints you define. It covers ordinary differential equations (ODEs), partial differential equations (PDEs), stochastic differential equations (SDEs), and graphical relational (GR) structure, where variables interact through an explicit dependency pattern.
Outputs are closed-form equations: relationships a reviewer can read, differentiate, and test on new data without treating the model as an opaque function approximator.
Mode 2
Neural discovery finds governing patterns in high-dimensional, noisy data where compact closed-form laws are unlikely. Discovered representations remain physically consistent, so results are scientifically meaningful, not just statistically fit.
This mode fits when state is only partly observed, when coupling spans many channels, or when the system is too complex for a single equation. Uncertainty is quantified before results are summarized into claims.
Mode 3
Neuro-Symbolic discovery combines learning from complex, noisy, or heterogeneous data with extraction of interpretable equations. It handles sensor fusion, missing data, and nonlinear relationships, then distills the results into governing laws you can read and verify.
Teams can compare residuals to discovered equations, require agreement before promotion, or iterate — tightening the interpretable laws and letting the engine capture what remains unexplained.
Mode 4
The causal mode targets systems whose behavior is organized by causal mechanisms and interventions. Powered by ARDA's Causal Dynamics Engine (CDE), it learns how entities influence one another along trajectories and focuses on what would change if the generative mechanism were perturbed.
CDE actively proposes targeted experiments designed to resolve ambiguous causal edges — so measurement budget targets reductions in structural uncertainty. Outputs include directed causal graphs with probabilities and identifiability analysis that records what the current experimental design can and cannot distinguish.
Architecture
ARDA's architecture is composable: functional roles in the pipeline can be swapped, extended, or combined to match your domain and data type. Each role has versioned implementations and ledger references so runs stay reproducible.
This design means ARDA adapts to new domains without rewriting the pipeline. Whether you bring temporal data, spatial fields, relational graphs, or multi-modal observations, the engine assembles the right configuration automatically.
Simulation universes
ARDA ships with built-in simulation universes for validating discovery modes and benchmarking configurations. Each universe has known governing equations or dynamics, so you can check whether symbolic, neural, and causal paths recover structure within tolerance before relying on proprietary data.
Spring
Pendulum
Lorenz
Lotka-Volterra
Van der Pol
Duffing
Brusselator
Glycolysis
FitzHugh-Nagumo
Kuramoto
Hodgkin-Huxley
CSTR
Wave
Heat
Burgers
Navier-Stokes
Tokamak Plasma
Battery Cell
Ground truth in these universes supports regression testing, mode comparison, and operator training on failure modes without touching real systems until pipeline behavior is understood.
Scientific Output
Every ARDA discovery produces typed, machine-readable scientific claims. Each claim carries metadata, confidence scoring, provenance, and governance status. Not paragraphs. Not unstructured output. Typed knowledge that can be audited, compared, and reproduced.
Every run writes a hashed, versioned record of everything that happened. Not a log file — a structured evidence entry that supports audit, reproduction, and peer review.
Data Provenance
Run Metadata
Results
Governance
Governance
Governance in ARDA is structural, not optional. Every claim is typed. Every run produces a hashed evidence ledger entry. Every discovery can be reproduced with a single Truth Dial setting. The governance stack enforces reproducibility from the first run.
The Truth Dial is a single control that governs the rigor-speed tradeoff across the entire pipeline. Set it based on where you are in the research process.
Negative controls are not an afterthought. ARDA applies time-shuffled baselines, phase-randomized controls, label-permutation tests, noise robustness checks, bootstrap stability analysis, feature-shuffle tests, and out-of-distribution evaluations. Claims that survive all applicable controls get promoted. Claims that fail are flagged and recorded in the evidence ledger with the specific control that caused the failure.
Fast iteration. No negative controls enforced. Claims are tagged as hypotheses. Use this for initial data exploration and rapid ideation.
Negative controls are applied: time shuffle, phase randomization, label permutation, noise robustness. Determinism tier 1+. Claims that pass are promoted to provisional status.
Full control suite including bootstrap stability, feature shuffle, and out-of-distribution testing. Determinism tier 3 with seeded randomness. Generates a complete replay recipe with frozen config and pinned library versions.
Why ARDA
Literature agents read papers. Writing systems generate manuscripts. Prediction pipelines fit curves. ARDA discovers governing laws.
Literature-reading platforms search existing papers and summarize what is already known.
ARDA discovers new science. It does not read papers. It takes raw data and finds the governing laws that have never been written down.
Paper-writing systems generate research manuscripts in LaTeX with automated peer review.
ARDA produces typed scientific claims — structured, machine-readable, governed. Not documents. Knowledge objects that can be audited, compared, and built upon.
Prediction pipelines fit black-box models that tell you what might happen next.
ARDA discovers governing equations — the actual mathematical laws. Closed-form expressions a physicist can read. Not a neural network output. Interpretable science.
Domain-specific tools serve one field: drug discovery, materials, or molecular design.
ARDA works wherever there is data with underlying dynamics. Physics, biology, chemistry, finance, manufacturing, climate, energy. The engine is domain-agnostic.
Industries
Wherever there is observational data with underlying physical, biological, chemical, economic, or engineered dynamics, ARDA can discover the laws that govern it.
Accelerate drug discovery by identifying molecular interaction laws, binding dynamics, and pharmacokinetic equations from experimental assay data.
Learn moreDiscover the causal structures governing biological systems — from CRISPR outcomes to protein folding dynamics — directly from experimental time-series.
Learn moreIdentify causal treatment effects and patient response dynamics from clinical trial data with ARDA's causal dynamics capability.
Learn moreDiscover gene regulatory networks and protein interaction dynamics from high-throughput sequencing and mass spectrometry data.
Learn moreUncover dynamical laws governing neural activity from EEG, fMRI, and electrophysiology recordings.
Learn moreModel disease transmission dynamics, identify causal risk factors, and discover the governing equations of epidemic spread.
Learn moreDiscover reservoir dynamics, flow equations, and production decline laws from well data with full governance for regulatory compliance.
Learn moreDiscover the physical laws governing renewable energy systems — from wind turbine performance to solar panel degradation dynamics.
Learn moreDiscover confinement scaling, stability dynamics, and transport behavior from tokamak diagnostics, neutron measurements, and reactor kinetics—with governed, auditable scientific claims.
Learn moreDiscover governing dynamics of power grid behavior — load flow equations, stability boundaries, and cascading failure mechanisms.
Learn moreIdentify governing geomechanical laws and extraction dynamics from drilling, sensor, and production data.
Learn moreDiscover physical laws governing semiconductor device behavior — switching dynamics, thermal dissipation, and process optimization equations.
Learn moreIdentify governing dynamics of robotic systems — kinematic laws, control equations, and environmental interaction models.
Learn moreDiscover decoherence dynamics, gate error laws, and qubit interaction equations from quantum hardware characterization data.
Learn moreDiscover governing dynamics of training processes — loss landscapes, optimization trajectories, and scaling laws.
Learn moreDiscover aerodynamic laws, structural dynamics, and flight control relationships with governance meeting aerospace certification standards.
Learn moreDiscover governing equations across powertrain, chassis, and battery systems with deterministic replay for validation.
Learn moreIdentify governing process dynamics — temperature-pressure-flow relationships and defect formation laws — from sensor data.
Learn moreDiscover structural dynamics laws and fatigue equations from sensor instrumentation and structural health monitoring.
Learn moreAnalyze molecular dynamics trajectories with physics-informed architectures to discover material property laws and phase transitions.
Learn moreDiscover chemical reactor dynamics, reaction rate laws, and conservation equations with workflows suited to reactor and separation train data.
Learn moreIdentify governing equations of polymer dynamics — viscoelastic laws, crystallization kinetics, and degradation mechanisms.
Learn moreDiscover governing physics at the nanoscale — quantum confinement, surface energy laws, and self-assembly dynamics.
Learn moreDiscover governing equations of climate dynamics — energy balance laws, circulation patterns, and feedback mechanisms.
Learn moreIdentify ocean circulation laws, wave dynamics, and thermohaline governing relationships from oceanographic data.
Learn moreDiscover causal dynamics governing pollution transport and ecosystem response from multi-sensor monitoring networks.
Learn moreIdentify population dynamics laws, predator-prey equations, and ecosystem stability conditions from ecological survey data.
Learn moreDiscover governing dynamics of financial markets — volatility laws, correlation structures, and regime transition equations.
Learn moreIdentify causal risk factor relationships and tail dependency laws from historical loss and exposure data.
Learn moreDiscover macroeconomic dynamics — GDP growth laws, inflation equations, and business cycle governing mechanisms.
Learn moreDiscover crop growth dynamics, yield prediction laws, and soil-plant-atmosphere interaction equations.
Learn moreIdentify signal propagation laws, network traffic dynamics, and interference equations from measurement data.
Learn moreDiscover governing dynamics of supply chain behavior — demand propagation, inventory oscillations, and disruption cascades.
Learn moreA discovery engine for universities and research institutions across every scientific discipline.
Learn moreIntegration
ARDA exposes the full discovery engine through multiple access surfaces. Your agents, scripts, and workflows connect through whichever interface fits your stack.
REST API
Full OpenAPI spec. Every resource — runs, campaigns, claims, datasets — is a first-class endpoint.
Python SDK
Synchronous and async clients with typed models. Import, configure, discover in a few lines.
MCP
Standard Model Context Protocol server. AI agents discover and connect to ARDA automatically.
CLI
Full API surface from the command line. Scriptable, pipeable, suitable for CI/CD workflows.
Agents find ARDA via standard /.well-known/ endpoints. Zero configuration. The engine publishes typed manifests that describe every available capability.
Stateful sessions that survive restarts and reconnections. Lifecycle management, task queues with heartbeats, lineage tracking, and state persistence.
Set boundaries for what automated workflows can do. Experiment approval gates, budget ceilings, and safety constraints enforced at the engine level.
Orchestrate multi-phase research campaigns that plan sequences of discovery runs, transfer knowledge across runs, adapt based on prior findings, and build toward complex research objectives that no single run can address.
Every agent session moves through defined lifecycle stages: planning, ready, running, completed. Task queues with heartbeat monitoring ensure no work is lost. Full lineage tracking connects every action to its session context.
Resources
Custom Contracts
You license the engine — the pipeline, the algorithms, the governance stack. Your agents use it. Models built on your data are yours. Discoveries are yours. Cloud-hosted, self-hosted, or air-gapped.
How We Differ
ARDA is built from first principles. Not a prompt layer on GPT. Not a wrapper around an open-source model. A purpose-built engine that discovers governing laws from raw data.
Engine, not model
Symbolic regression tools find equations but have no pipeline. ML models predict but don't explain. Causal discovery packages find correlations, not mechanisms. ARDA is a complete discovery engine: it ingests your data, selects the right discovery mode, builds models internally, validates with falsification testing, and produces governed scientific claims. You bring data. The engine does the rest.
| Symbolic Regression & ML Tools | ARDA | |
|---|---|---|
| Approach | Single method — sparse regression, GP, or neural fitting | 4 modes: symbolic, neural, neuro-symbolic, causal (CDE) |
| Output | Candidate equations or predictions | 19 typed scientific claims — equations, causal graphs, conservation laws |
| Causality | Correlations only | Directed causal graphs via interventional reasoning (CDE) |
| Validation | Manual benchmarking | 7 negative controls — time shuffle, phase randomization, bootstrap, surrogates, OOD |
| Pipeline | Notebooks and scripts | Automated: ingest → profile → route → discover → validate → govern |
| Model construction | You choose and configure the model | Engine builds models from your data automatically |
| Reproducibility | Seed-dependent, manual tracking | Deterministic replay with hashed evidence ledger |
| Governance | None | Truth Dial tiers, autonomy policies, provenance tracking |
Why ARDA
Symbolic, Neural, Neuro-Symbolic, and Causal Discovery (CDE). The engine selects the right mode for your data. Open-source models have one architecture for everything.
Data ingestion → profiling → mode routing → discovery → validation → governance. Not a notebook. Not a prompt. A governed pipeline.
Directed causal graphs, not correlations. Active experiment design. Interventional reasoning. No open-source model does this.
Time shuffle, phase randomization, bootstrap stability, surrogate analysis, and out-of-distribution testing. Built into every run.
Every claim is hashed, versioned, and linked to its data, config, and replay recipe. Deterministic reproducibility.
Three tiers: Explore (fast, no controls), Validate (4 controls), Publish (full 7-control suite). You choose the rigor-speed tradeoff.
Platform
Bring time-series, spatial fields, relational graphs, or any structured observation. The engine profiles it, routes to the right discovery mode, and builds models on it. Small datasets welcome. Your data never leaves your infrastructure.
ARDA trains neural architectures, symbolic searchers, neuro-symbolic encoders, and causal mechanisms on your data. You don’t bring a model. The engine builds one from your data, validates it, and produces governed scientific claims.
Cloud-hosted, self-hosted, or air-gapped. Bring your own AI agent and LLM provider. Your data, your compute, your discoveries.
† Causal Dynamics Engine (CDE) is patent pending in the United States and other countries. Vareon, Inc.
From the Blog
One discovery engine for every scientific and industrial domain. Agent-first. Built from first principles. Governed, reproducible, and ready for production.