docs: link Runtime Jetson evidence reports

docs/portfolio/final_validation_completion.md

@@ -42,6 +42,7 @@ Runtime demo pair:
 - TensorRT Jetson FP16 25W: 10.066401 ms mean / 15.548438 ms p99 / 99.340373 FPS
 - Jetson FP16 15W power-mode evidence: 10.799106 ms mean / 15.529218 ms p99 / 92.600262 FPS
 - Studio speedup display: about 4.51x faster for the ONNX Runtime CPU FP32 vs TensorRT Jetson FP16 25W demo pair
+- Runtime report snapshots: [Jetson evidence summary](https://github.com/gwonxhj/InferEdge-Runtime/blob/main/docs/reports/jetson_evidence_summary.md), [Jetson power-mode comparison](https://github.com/gwonxhj/InferEdge-Runtime/blob/main/docs/reports/jetson_power_mode_comparison.md)
 
 YOLOv8 COCO subset evaluation:
 

docs/portfolio/inferedge_pipeline_status.md

@@ -110,6 +110,7 @@ InferEdge now has two runtime execution evidence paths:
    - 25W result: `results/jetson_evidence/yolov8n_trt_fp16_25w_20260504T170039Z.json`, mean `10.066401 ms`, p95 `15.476641 ms`, p99 `15.548438 ms`, FPS `99.340373`.
    - 15W result: `results/jetson_evidence/yolov8n_trt_fp16_15w_20260504T171959Z.json`, mean `10.799106 ms`, p95 `15.438690 ms`, p99 `15.529218 ms`, FPS `92.600262`.
    - The 15W vs 25W comparison is treated as system evidence because power mode changes the run configuration; it is not interpreted as same-condition model regression.
+   - Runtime report snapshots: [Jetson evidence summary](https://github.com/gwonxhj/InferEdge-Runtime/blob/main/docs/reports/jetson_evidence_summary.md), [Jetson power-mode comparison](https://github.com/gwonxhj/InferEdge-Runtime/blob/main/docs/reports/jetson_power_mode_comparison.md).
 
 Compare-key polish status: this limitation has been resolved in InferEdgeRuntime #37. When a Forge manifest is applied, Runtime now prefers `manifest.source_model.path` for compare naming, so a TensorRT artifact path such as `model.engine` can still produce `compare_model_name=yolov8n` and `compare_key=yolov8n__b1__h640w640__fp32`. This improves provenance and compare-readiness; it does not add production SaaS worker infrastructure.
 

docs/portfolio/inferedge_portfolio_submission.md

@@ -109,6 +109,8 @@ Recent validation evidence:
 - Lab PR #171 기준 1-page architecture summary 문서화 완료
 - Lab -> Runtime manual smoke using `yolov8n.onnx`: `/api/analyze` created job `job_9e2321179256`, Lab invoked the C++ Runtime CLI through the dev-only subprocess path, ONNX Runtime executed the model successfully, and the latency/provenance JSON was ingested back into the Lab job result. The smoke reported ONNX Runtime backend available, benchmark status success, mean latency about 47.97 ms, p50 about 46.95 ms, p95/p99 about 51.80 ms, and about 20.85 FPS.
 - Jetson TensorRT Runtime smoke: on Jetson Orin Nano (`Linux 5.15.148-tegra`, `aarch64`), the C++ Runtime CLI in `~/InferEdge-Runtime` executed Forge manifest `/home/risenano01/InferEdgeForge/builds/yolov8n__jetson__tensorrt__jetson_fp16/manifest.json` and TensorRT engine artifact `/home/risenano01/InferEdgeForge/builds/yolov8n__jetson__tensorrt__jetson_fp16/model.engine`. The current Jetson Evidence Track records TensorRT FP16 25W at mean `10.066401 ms`, p95 `15.476641 ms`, p99 `15.548438 ms`, FPS `99.340373`, and TensorRT FP16 15W at mean `10.799106 ms`, p95 `15.438690 ms`, p99 `15.529218 ms`, FPS `92.600262`.
+  - Runtime report snapshot: [Jetson evidence summary](https://github.com/gwonxhj/InferEdge-Runtime/blob/main/docs/reports/jetson_evidence_summary.md)
+  - Runtime report snapshot: [Jetson power-mode comparison](https://github.com/gwonxhj/InferEdge-Runtime/blob/main/docs/reports/jetson_power_mode_comparison.md)
 - Runtime compare-key identity polish: InferEdgeRuntime now preserves Forge manifest source model identity for compare naming. If `manifest.source_model.path` is `models/onnx/yolov8n.onnx` and the explicit TensorRT artifact path is `model.engine`, Runtime can keep `compare_model_name=yolov8n` and `compare_key=yolov8n__b1__h640w640__fp32`.
 - Guided demo entrypoint: `scripts/demo_pipeline_full.sh` summarizes the full Forge -> Runtime -> Lab -> optional AIGuard flow and can print the Jetson TensorRT Runtime command without claiming production worker or SaaS readiness.
 - Local Studio demo evidence: `/studio` can load bundled ONNX Runtime CPU and TensorRT Jetson Runtime result fixtures from `examples/studio_demo`, keep the demo pair selectable in Recent jobs while the local server process is alive, and show TensorRT Jetson vs ONNX Runtime CPU comparison in the browser. The fixture-backed evidence records ONNX Runtime CPU FP32 at mean `45.4299 ms` / p99 `49.2128 ms` / `22.0119 FPS` and TensorRT Jetson FP16 25W at mean `10.066401 ms` / p99 `15.548438 ms` / `99.340373 FPS`, about a `4.51x` TensorRT speedup for this demo pair.