Empirical evaluation of adaptive evidence saturation stopping, source wire de-duplication, defensive fallbacks under simulated network blackout, and deterministic research replay.
Recorded evaluation runs across corporate and narrative entities comparing unguided query fan-out against Aegis Protocol's adaptive saturation stopping and source lineage deduplication. (Evaluated on release v3.7.0; reproducible via tests/chaos/test_agent_reach_chaos.py).
| Entity / Target | Domain Archetype | Queries (Planned → Executed) | Query Reduction | Funnel (Candidates → Reads → Primary) | Wire De-duplication | Latency (Single / N=4 Concur) |
|---|---|---|---|---|---|---|
|
Tata Motors Limited
TATAMOTORS.NS · NSE/BSE
|
Industrial & Regulatory | 8 → 5 | -37.5% | 71 → 11 → 28.6% Primary | 3.2x (16 citations → 5 wires) | 118ms / 242ms |
|
Tesla Inc.
TSLA · NASDAQ
|
High Social Noise / 10-Q | 10 → 6 | -40.0% | 94 → 14 → 33.3% Primary | 4.1x (29 citations → 7 wires) | 142ms / 268ms |
|
Nike Inc.
NKE · NYSE
|
Consumer & Retail Supply | 6 → 4 | -33.3% | 52 → 8 → 25.0% Primary | 2.8x (11 citations → 4 wires) | 95ms / 198ms |
|
OpenAI Architecture Leak
OPAI · Tech & Viral Narrative
|
Viral Speculation Contagion | 8 → 5 | -37.5% | 86 → 12 → 18.2% Primary | 3.6x (22 citations → 6 wires) | 110ms / 225ms |
Automated stress and resilience test suite executing real failure injections against the channel pipeline. Confirms defensive fallbacks, non-blocking thread termination, and strict absence of fabricated evidence.
Completed investigations are recorded with SHA-256 evidence candidate hashes, decision logs, and execution traces for reproducible audit via /api/research/replay/dossiers/{id}.