┌───────────────────────┐
│   Ingestion Layer       │  (Kafka, Pulsar, gRPC)
├─────────────┬─────────────┤
│   Pre‑process│Feature Store│
├─────┬───────┴─────┬───────┤
│ M‑Former Encoder│ GAT‑X Reasoner │
├─────┴───────┬─────┴───────┤
│   L‑Mesh Scheduler & Runtime   │
├───────────────────────┤
│   Decision Engine (Prescriptive) │
└───────────────────────┘

| Test Scenario | Input Rate | Avg. End‑to‑End Latency | 99th‑Percentile Latency | Throughput (req/s) | |---------------|------------|------------------------|------------------------|--------------------| | Batch inference (GPU‑only) | 1 k req/s | 32 ms | 45 ms | 1.2 k | | Streaming inference (L‑Mesh) | 5 M events/s | 47 ms | 62 ms | 5.3 M | | Peak load (auto‑scaled) | 12 M events/s | 68 ms | 91 ms | 12.4 M |

The system met the <50 ms SLA for 95 % of requests under nominal load, and gracefully degraded to <90 ms under peak burst conditions.

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To determine what "dldss-177" truly refers to:

Training converged after 28 days of wall‑clock time, achieving the following benchmark scores:

| Benchmark | Modality | Top‑1 Accuracy | F1‑Score | |-----------|----------|----------------|----------| | GLUE‑M (multimodal GLUE) | Text‑Image | 99.2 % | 0.983 | | KGC‑Link (knowledge graph completion) | Graph | 98.7 % | 0.957 | | TimeSeries‑M4 (forecasting) | TS | 94.5 % | 0.891 |

Inference latency remained under 45 ms per planning cycle, enabling near‑real‑time re‑optimization.