Datasets:

ClarusC64
/

ai-temporal-5node-pressure-buf-lag-cpl-multiagent-coordination-v0.1

id string	context string	step_unit string	pressure_t0 float64	pressure_t1 float64	pressure_t2 float64	pressure_t3 float64	buffer_t0 float64	buffer_t1 float64	buffer_t2 float64	buffer_t3 float64	lag_t0 float64	lag_t1 float64	lag_t2 float64	lag_t3 float64	coupling_t0 float64	coupling_t1 float64	coupling_t2 float64	coupling_t3 float64	cross_step int64	notes string	label_cascade_state int64
MA-0001	Agents share tasks with clear arbitration. Resource headroom stable.	events	0.22	0.28	0.3	0.32	0.86	0.84	0.82	0.8	0.18	0.2	0.22	0.24	0.3	0.34	0.36	0.38	0	stable coordination	0
MA-0002	Mild task overlap. Arbitration resolves quickly. Resources adequate.	events	0.34	0.4	0.44	0.46	0.78	0.74	0.7	0.68	0.24	0.28	0.3	0.32	0.36	0.42	0.44	0.46	0	recoverable tension	0
MA-0003	Agents compete for scarce tools. Arbitration queue grows.	events	0.48	0.6	0.72	0.82	0.7	0.6	0.46	0.34	0.28	0.44	0.66	0.82	0.42	0.58	0.74	0.86	2	resource contention spiral	1
MA-0004	Conflicting goals emerge. Resolution delayed. Shared state tightens.	events	0.52	0.64	0.76	0.86	0.66	0.54	0.4	0.28	0.32	0.54	0.74	0.88	0.5	0.66	0.8	0.9	2	goal drift + lag	1
MA-0005	Autonomous retries amplify load. Arbitration saturated.	events	0.46	0.58	0.7	0.84	0.74	0.64	0.44	0.3	0.22	0.42	0.7	0.88	0.4	0.56	0.78	0.9	3	late lock-in	1
MA-0006	Contention detected early. Arbitration bandwidth increased.	events	0.44	0.52	0.5	0.48	0.72	0.76	0.78	0.8	0.3	0.26	0.22	0.2	0.46	0.44	0.4	0.38	0	intervention holds	0
MA-0007	Inter-agent messaging spikes. Arbitration latency increases.	events	0.5	0.62	0.78	0.9	0.68	0.56	0.38	0.26	0.28	0.5	0.76	0.9	0.48	0.64	0.82	0.92	1	early crossing	1
MA-0008	Task partitioning improves mid-stream. Coupling reduced.	events	0.4	0.48	0.52	0.5	0.74	0.7	0.72	0.74	0.26	0.28	0.24	0.22	0.44	0.42	0.4	0.38	0	rebalanced	0
MA-0009	Shared resource pool collapses. Arbitration overwhelmed.	events	0.56	0.7	0.84	0.92	0.6	0.48	0.32	0.22	0.36	0.62	0.84	0.92	0.58	0.74	0.88	0.94	2	lock-in visible	1

What this repo does

This dataset tests whether a model can detect a multi-agent coordination cascade forming over time by reading a short ordered window of signals and predicting whether coordination lock-in occurs by the final step.

Core quad

pressure
buffer
lag
coupling

Prediction target

label_cascade_state

Row structure

One row represents one short time window (t0 to t3) for a multi-agent system under coordination stress. It includes time-series values for competitive pressure, shared resource buffer, arbitration lag, and inter-agent coupling density. The label marks whether coordination cascade lock-in is reached by t3.

Files

data/train.csv
data/tester.csv
scorer.py

Evaluation

Run predictions on tester.csv.
Score with scorer.py.

License

MIT

Structural Note

This dataset identifies a measurable coupling pattern associated with systemic instability. The sample demonstrates the geometry. Production-scale data determines operational exposure.

What Production Deployment Enables

• 50K–1M row datasets calibrated to real operational patterns • Pair, triadic, and quad coupling analysis • Real-time coherence monitoring • Early warning before cascade events • Collapse surface and recovery window modeling • Integration and implementation support

Small samples reveal structure. Scale reveals consequence.

Enterprise & Research Collaboration

Clarus develops production-scale coherence monitoring infrastructure for critical systems across healthcare, finance, infrastructure, and regulatory domains.

For dataset expansion, custom coherence scorers, or deployment architecture: team@clarusinvariant.com

Instability is detectable. Governance determines whether it propagates.

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