proffesor-for-testing__agentic-qe/docs/implementation/adrs/ADR-034-neural-topology-optimizer.md

ADR-034: Neural Topology Optimizer

Field	Value
Decision ID	ADR-034
Status	Accepted
Date	2026-01-10
Author	Architecture Team
Review Cadence	6 months

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WH(Y) Decision Statement

In the context of v3 AQE swarm topologies that coordinate multi-agent test execution across hierarchical, mesh, and ring configurations,

facing static topology configuration that doesn't adapt to workload patterns, no optimization feedback from successful executions, suboptimal agent-to-agent routing for latency, and manual tuning burden requiring human intervention,

we decided for neural topology optimization using reinforcement learning with a value network and experience replay to dynamically optimize swarm communication structure,

and neglected static topology configuration (no adaptation), rule-based optimization (limited flexibility), and genetic algorithms (slower convergence),

to achieve self-optimizing topology that learns optimal communication structure, measurable improvement in communication efficiency, TD error convergence over training, and reduced manual tuning,

accepting that this adds learning complexity, requires initial exploration phase with suboptimal actions, and needs sufficient training episodes to converge.

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Context

Current v3 AQE swarm topologies are statically configured as fixed mesh, hierarchical, or ring topologies. They don't adapt to workload patterns, don't learn from past executions, and require manual tuning for optimization.

RuVector's Neural Graph Optimizer demonstrates a powerful pattern: policy SNN outputs graph modification actions via spike rates, value network estimates mincut improvement, and experience replay enables stable learning. This creates a self-optimizing topology where the swarm learns the optimal communication structure.

The optimizer uses epsilon-greedy action selection with five possible actions: add connection, remove connection, strengthen connection, weaken connection, and no-op. Rewards combine mincut improvement with communication efficiency.

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Options Considered

Option 1: Neural Topology Optimization (Selected)

Uses reinforcement learning with a value network to estimate state values and TD-learning for weight updates. Experience replay prioritized by TD error enables stable learning.

Pros:

• Continuous adaptation to workload patterns
• Learns from task outcomes (success/failure as reward)
• No manual tuning once trained

Cons:

• Initial exploration phase may be suboptimal
• Requires sufficient training episodes

Option 2: Static Topology (Rejected)

Use fixed mesh, hierarchical, or ring topologies without adaptation.

Why rejected: No adaptation to workload patterns; requires manual optimization.

Option 3: Rule-Based Optimization (Rejected)

Use hand-coded rules for topology modifications based on metrics.

Why rejected: Limited flexibility; can't discover novel optimizations.

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Dependencies

Relationship	ADR ID	Title	Notes
Part Of	MADR-001	V3 Implementation Initiative	Swarm optimization
Relates To	ADR-031	Strange Loop Self-Awareness	Shared adaptation concepts
Relates To	ADR-035	Causal Discovery	Learning patterns

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References

Ref ID	Title	Type	Location
SPEC-034-A	Value Network	Technical Spec	specs/SPEC-034-A-value-network.md
SPEC-034-B	Experience Replay	Technical Spec	specs/SPEC-034-B-experience-replay.md
SPEC-034-C	Neural Optimizer	Implementation Guide	specs/SPEC-034-C-neural-optimizer.md

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Governance

Review Board	Date	Outcome	Next Review
Architecture Team	2026-01-10	Accepted	2026-07-10

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Status History

Status	Date	Notes
Proposed	2026-01-10	Initial creation from RuVector analysis
Accepted	2026-01-10	Approved by Architecture Team