Physiological signals
Upload EEG, ECG, EKG, HRV, respiration, electrodermal activity, sleep, or wearable-sensor data to study changing states, recovery, regulation, and recurrent temporal motifs.
Enactive AI · Collaborative temporal science
Kalyriel Scope
A Collaborative Temporal Science Instrument
An Open Platform for Discovering, Interpreting, and Sharing Temporal Knowledge. Kalyriel Scope combines continual computational discovery with human semantic interpretation to enable collaborative exploration of dynamic systems. By discovering temporal motifs (recurring patterns that unfold through time), it supports expert annotation, and enables shareable motif libraries. The platform helps scientific communities build evolving knowledge about temporal phenomena across domains such as healthcare, neuroscience, climate science, industrial monitoring, finance, and human–AI interaction.
Why this is differentFrom Prediction to Temporal Explanation
Why this is different
From Prediction to Temporal Explanation
The Emergence Machine does not only predict a signal. It organizes the signal into an interpretable temporal structure—identifying phases, transitions, recurring motifs, disruptions, and recoveries—and renders that structure as an evidence-grounded report.
Workspace 01 · Instrumented Assessment
Cognitive Trajectory Laboratory
Run a full-screen Clock Drawing or free-drawing session, quantify the complete interaction trajectory, replay it, and export the resulting data locally.
02Co-Creative Interaction
Aether · Participatory Sense-Making Laboratory
Draw with an enactive co-creative AI that can be activated, paused, guided, and evaluated while Kalyriel Scope measures coupling, drift, participation, feedback, turn-taking, and shared sense-making through time.
Standard Scope use cases
Analyze temporal structure in any compatible CSV
Use the standard upload workflow for domain data that already exists as a time-indexed stream. Select the variable of interest, then examine regimes, transitions, motifs, drift, attractors, and adaptive dynamics.
CSV upload
Financial and market data
Upload price, return, volume, volatility, indicator, or event-labeled data to examine market regimes, transitions, recurring motifs, and adaptive dynamics.
Environmental and ecological dynamics
Analyze weather, climate, water, energy, animal movement, ecosystem, or environmental sensor streams for shifts, cycles, instability, and cross-scale organization.
Machines, robotics, and infrastructure
Explore telemetry, vibration, power, robotic behavior, network, transport, or equipment data to identify operating regimes, degradation, anomalies, and recovery trajectories.
Kalyriel ScopeAn open platform for discovering, interpreting, and sharing temporal knowledge.
Compute Mode
Balances detail and performance based on device resources.
Motif Resolution
Controls motif granularity and multi-scale detail.
Current regime
—
Waiting for samples
Attractors L / R / G
0 / 0 / 0
Established recurring structures at three temporal scales
Regime F1
—
Detected regime events vs. high-drift anomaly states
MAE
—
Mean absolute one-step forecast error
MSE
—
Mean squared one-step forecast error
Cross-scale coherence
—
Agreement among local, regional, and global models
Tiny EM adaptive forecaster
A causal one-step predictor adapts its learned local velocity after every realized error.
Level 0.00
Trend 0.00
Variance 0.20
Momentum 0.75
EMA error —
Adaptive plasticity 0%
Surprise and changing residual direction increase adaptation and reopen attractor learning.
Current forecast skill vs. persistence
—
Rolling-window skill. Positive values outperform persistence.
Session median skill—
Signal, adaptive prediction, and regime boundaries
The one-step-ahead prediction is plotted at its target sample and learned online from realized error.
signalpredictionshiftannotation
Regime formation through time
Each color represents a distinct system configuration inferred online.
Drift pressure
Rising pressure means the current pattern no longer fits the active attractor.
System interpretation
A plain-language summary of what the model is doing now.
Load a demo or upload a numeric data column to begin.
Local attractor landscape projection
Fast patterns · projected by mean and slope · size = occupancy · opacity = recency
Regional attractor landscape projection
Intermediate patterns · projected by mean and slope · size = occupancy · opacity = recency
Global attractor landscape projection
Slow patterns · projected by mean and slope · size = occupancy · opacity = recency
How to read the Emergence Machine
1 · Observe
The machine receives one value at a time from a CSV column or generated signal.
The machine receives one value at a time from a CSV column or generated signal.
2 · Form attractors
Recurring local patterns become stable centers rather than being treated as isolated points.
Recurring local patterns become stable centers rather than being treated as isolated points.
3 · Detect regimes
Persistent changes in attractor occupancy and drift become regime transitions.
Persistent changes in attractor occupancy and drift become regime transitions.
4 · Adapt
Forecasts are rebuilt from the currently active structures across three timescales.
Forecasts are rebuilt from the currently active structures across three timescales.
Kalyriel Scope: No data leaves this browser. This open research platform makes temporal motifs, semantic annotation, motif libraries, and adaptive dynamics visible and explorable. It is a research prototype, not a validated production forecasting or clinical decision system.