Learned Working Set¶
Corrobore's working set is a bounded, explainable view used to navigate a graph without loading every node and relationship. Epic 0017 is implemented and benchmarked; its migrated epic tracker #86 and final acceptance issue #73 are complete.
This guide describes the current runtime contract.
Implemented layers¶
| Layer | Public surface | Purpose |
|---|---|---|
| Lifecycle | GraphWorkingSetManager |
Create working sets; load seeds; track hot relationships, warm adjacency, pins, dirty records, stats, and explanations. |
| Telemetry | WorkingSetTelemetryRecorder, RetrievalTelemetryRecord |
Record selected/skipped edges, page-ins, prefetches, evictions, dead ends, evidence, quality, memory, and latency. |
| Positive field | PheromoneField, EdgeUtility, PheromoneTaskScope |
Learn task-scoped expected utility with temporal decay. |
| Negative field | AntiPheromoneField, AntiPheromoneSignal |
Penalize dead ends, supernodes, stale evidence, contradictions, and poisoning risk. |
| Controller | WorkingSetController, BanditContext, WorkingSetAction, BanditReward |
Choose an explicit action under context and observe reward. |
| Benchmark | run_working_set_benchmark, fimi_multi_hop_benchmark_workload |
Compare reproducible policy baselines and metrics. |
Decision flow¶
flowchart LR
Query["Query, seeds, task, budget"] --> Context["BanditContext"]
Context --> Controller["WorkingSetController"]
Positive["PheromoneField"] --> Controller
Negative["AntiPheromoneField"] --> Controller
Controller --> Action["expand / prefetch / page-in / stop / verify / external retrieval"]
Action --> Record["RetrievalTelemetryRecord"]
Record --> Reward["BanditReward"]
Record --> Positive
Record --> Negative
Reward --> Controller
The learned controller does not replace deterministic budgets or supernode protection. These remain hard safety boundaries.
Benchmark policies and metrics¶
The harness represents LRU, LFU, static profiles, semantic-only, PageRank/spreading activation, contextual bandit, and learned pheromone policy families. Reports include evidence recall, pages loaded, peak memory, p95 latency, dead-end expansions, supernode blow-ups, and reproducibility metadata.
The target gains stated by Epic 0017 are measured research results on the reference workload, not general production performance guarantees. Use the completed acceptance suite and reproducibility report as the evidence source and scope for any performance statement.
For operational query boundaries and runtime policy behavior, use the HTTP Server guide and Cypher Support as primary references.