This report closes the bounded #2857 probe for activation usefulness. It asks a narrow question: when replay signal is available, does activation reduce manual search, wrong-route drag, or noisy surfacing on a small dogfood/replay slice without claiming live default quality?
The answer is yes for this bounded replay. It is not a Dream/default-foreground promotion.
python benchmarks\aippocampus\benchmark_activation_dogfood_usefulness.py --jsonFocused regression coverage:
python -m unittest tests.aippocampus.test_benchmark_activation_dogfood_usefulness -v| Metric | Cold no activation | Warm replay signal | Delta |
|---|---|---|---|
| Generated candidates | 2 | 5 | +3 |
| Foreground exposed candidates | 2 | 2 | 0 |
| Verifier-seen candidates | 2 | 5 | +3 |
| Useful source-open hits | 1 | 2 | +1 |
| Source-open follow-through | 1 | 2 | +1 |
| Manual-search fallback | 2 | 1 | -1 |
| Wrong-route drag | 1 | 0 | -1 |
| Noisy surfacing | 1 | 0 | -1 |
Warm replay roles: process_supervision=1, hard_negative=1,
replay_sample=1, and positive_demo=1.
One case consumes a replayable live-agent trajectory packet with complete ordered source reopen. The other cases cover route-feedback replay, parked candidate lifecycle, and source-openable route preservation.
activation_probe_useful_on_bounded_replay=true.
The probe supports keeping activation signals in the bounded replay/dogfood pipeline because they reduce manual search and route drag while preserving source-open follow-through. It does not promote live default foreground activation, Dream default delivery, source truth from activation, or private history quality.
This is a deterministic dogfood/replay usefulness probe. It does not claim causal real-user lift, live default foreground quality, private-history quality, default Dream delivery quality, or factual support from activation signals.