LAB / WMSI-EXP-035
Persistent Swarm Learning in a Cyber Range.
Can a swarm improve against a fixed local range through shared external memory without weight updates?
01 / QUESTION
Cyber and Adversarial Interaction
Can a swarm improve against a fixed local range through shared external memory without weight updates?
This is a protocol scaffold, not a completed experiment or a frozen execution plan. No measured effect or research conclusion is claimed here.
Hypothesis to test
The manipulation described below produces a reproducible, condition-specific change in the measured system behavior or internal state that is not explained by the ordinary controls.
Ordinary competing explanation
The observed pattern is explained by ordinary sampling variation, prompt/representation artifacts, surface lexical cues, evaluator bias, or the known product/runtime mechanics captured by the controls.
02 / PLANNED DESIGN
How we intend to test it
Run repeated sandbox episodes with persistent memory of failures and defenses.
Controls
Memory reset; curated memory; no-memory.
What we would measure
Learning curve, strategy reuse, novelty, overfitting.
Access and instruments
Ordinary software or API access. This describes access requirements, not verified readiness on our Spark.
Docker range; shared memory DB.
Before a full run
Feasibility=NOW; priority=P1. Before confirmatory execution, run a small pilot to estimate variance/cost, freeze the scoring rules, and assign a run-count target.
Model revisions, final prompts, sample sizes, cost limits, and scoring rules must be settled through the pilot and protocol-freeze process.
03 / INTERPRETATION
What could fool us
External internet leakage, unequal tool access, scenario memorization. Keep all activity isolated and authorized.
What would weaken the idea
If performance does not improve beyond repeated-task familiarity, persistent swarm learning is weak.
Observation, inference, speculation, unknowns, and apparatus errors will be labeled separately. One striking transcript cannot establish a general effect.
04 / RECORD
Evidence we plan to preserve
Local synthetic-range state, agent actions, alerts, outcomes, reset hashes.
Planned visual evidence
Episode learning curve; memory lineage graph.
No research runs or raw datasets are published for this experiment. The September 26, 2026 registry and protocol compendium (version 1.0) are the sources for this planning record.
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