docs: align InferEdgeEnv portfolio positioning

README.ko.md

@@ -16,6 +16,9 @@ ONNX model
 -> InferEdgeLab analysis/API/job/deployment_decision
 -> optional InferEdgeAIGuard deterministic diagnosis evidence
 -> deploy / review / blocked decision
+
+Supporting sidecar:
+InferEdgeEnv -> local-first run evidence registry / comparability checker
 ```
 
 ## Summary
@@ -24,6 +27,7 @@ ONNX model
 - Real device execution: Jetson TensorRT + ONNX Runtime CPU
 - Structured comparison: latency, accuracy, validation evidence
 - Deployment decision: deployable / review / blocked
+- Sidecar evidence registry: InferEdgeEnv는 Lab decision과 분리된 local benchmark evidence와 comparability를 기록
 - Local Studio: inference validation을 브라우저에서 확인하는 local-first workflow UI
 
 ## What Makes InferEdge Different?
@@ -109,6 +113,9 @@ bash scripts/demo_pipeline_full.sh --run-jetson-command-print
 - **InferEdge-Runtime:** Forge artifact 또는 Lab worker request를 받아 C++ 실행/검증 결과 JSON을 생성합니다.
 - **InferEdgeLab:** 결과를 비교/리포트/API/job/deployment decision으로 정리하는 owner입니다.
 - **InferEdgeAIGuard:** provenance mismatch나 suspicious result를 rule/evidence 기반으로 진단하는 optional evidence layer입니다.
+- **InferEdgeEnv:** Edge AI inference benchmark result를 local artifact와 SQLite registry로 고정하고 비교 가능성을 판정하는 local-first run evidence registry입니다.
+
+포트폴리오 경계: InferEdgeLab은 validation / decision layer이고, InferEdgeEnv는 run evidence registry / comparability layer입니다. InferEdge는 모델이 배포 가능한지 검증하고, InferEdgeEnv는 benchmark evidence가 신뢰 가능하고 비교 가능한 형태로 기록됐는지 관리합니다.
 
 ## 현재 범위와 future work
 

README.md

@@ -17,6 +17,7 @@ Language: English | [한국어](README.ko.md)
 - Real device execution: Jetson TensorRT + ONNX Runtime CPU
 - Structured comparison: latency, accuracy, and validation evidence
 - Deployment decision: deployable / review / blocked
+- Sidecar evidence registry: InferEdgeEnv records local benchmark evidence and comparability separately from Lab decisions
 - Local Studio: interactive workflow UI for inference validation
 
 ## What Makes InferEdge Different?
@@ -44,6 +45,9 @@ ONNX model
 -> InferEdgeLab compare / API / job workflow / deployment_decision
 -> optional InferEdgeAIGuard provenance diagnosis
 -> deploy / review / blocked decision
+
+Supporting sidecar:
+InferEdgeEnv -> local-first run evidence registry / comparability checker
 ```
 
 Repository roles are deliberately split:
@@ -52,6 +56,9 @@ Repository roles are deliberately split:
 - **InferEdgeRuntime:** C++ execution, profiling, result export, and worker response boundary.
 - **InferEdgeLab:** compare/report/API/job workflow and final deployment decision ownership.
 - **InferEdgeAIGuard:** optional rule + evidence based failure and provenance diagnosis.
+- **InferEdgeEnv:** local-first run evidence registry and comparability checker for Edge AI inference benchmark results.
+
+Portfolio boundary: InferEdgeLab is the validation / decision layer. InferEdgeEnv is the run evidence registry / comparability layer. InferEdge validates whether a model is deployable; InferEdgeEnv records whether benchmark evidence can be trusted and compared.
 
 Implemented today: Lab API response contract, `/api/compare`, `/api/analyze` in-memory jobs, worker request/response mappings, Runtime dry-run validation/export, Forge worker/runtime summary, AIGuard provenance mismatch diagnosis, Lab decision/report evidence smoke coverage, dev-only Lab -> Runtime ONNX Runtime smoke using `yolov8n.onnx`, manual Jetson TensorRT Runtime smoke using a Forge manifest plus TensorRT engine artifact, and Runtime source-model identity preservation for compare-ready TensorRT engine results.
 

docs/portfolio/inferedge_1page_architecture.md

@@ -4,6 +4,8 @@
 
 InferEdge is an end-to-end Edge AI inference validation pipeline that builds deployment artifacts, runs edge inference, compares results, diagnoses provenance issues, and produces deployment decisions.
 
+Supporting sidecar: InferEdgeEnv is a local-first run evidence registry and comparability checker for Edge AI inference benchmark results.
+
 PDF-ready portfolio draft: [InferEdge Portfolio Submission](inferedge_portfolio_submission.md). Local PDF export uses pandoc + xelatex through `bash scripts/export_portfolio_pdf.sh`.
 
 ## Problem
@@ -23,6 +25,9 @@ ONNX model
 -> optional InferEdgeAIGuard
 -> rule + evidence provenance diagnosis
 -> deploy / review / blocked decision
+
+Supporting sidecar:
+InferEdgeEnv -> local-first run evidence registry / comparability checker
 ```
 
 ## Repository Roles
@@ -31,6 +36,9 @@ ONNX model
 - **InferEdgeRuntime:** C++ execution/result export layer. Validates or runs model/artifact inputs, measures runtime latency, exports Lab-compatible result JSON, and dry-run exports worker responses.
 - **InferEdgeLab:** analysis/API/job/deployment decision owner. Compares Runtime results, generates reports, exposes API/job workflow contracts, preserves optional guard evidence, and owns the final `deployment_decision`.
 - **InferEdgeAIGuard:** optional rule + evidence diagnosis layer. Detects provenance/artifact/config mismatches and returns deterministic `guard_analysis` evidence for Lab to consume.
+- **InferEdgeEnv:** run evidence registry / comparability checker. Records benchmark artifacts, SQLite registry entries, evidence bundles, and comparability judgement without owning Lab deployment decisions.
+
+Portfolio boundary: InferEdgeLab is the validation / decision layer. InferEdgeEnv is the run evidence registry / comparability layer.
 
 ## Implemented Evidence
 

docs/portfolio/inferedge_pipeline_status.md

@@ -6,6 +6,8 @@ This document summarizes the current portfolio status of the InferEdge multi-rep
 
 InferEdge is an end-to-end edge AI inference validation pipeline. It is designed to turn an ONNX model into deployment evidence by connecting artifact build provenance, runtime profiling, Lab comparison/reporting, optional rule-based diagnosis, and a final deployment decision.
 
+Supporting sidecar: InferEdgeEnv is the local-first run evidence registry / comparability checker. InferEdgeLab remains the validation / decision layer; InferEdgeEnv records whether benchmark evidence can be trusted and compared.
+
 For a compressed recruiter/interviewer entry point, see [InferEdge 1-Page Architecture Summary](inferedge_1page_architecture.md).
 For the current portfolio completion checkpoint, see [InferEdge Final Validation Completion Pass](final_validation_completion.md).
 
@@ -23,6 +25,9 @@ ONNX model
 -> InferEdgeLab compare / API / job workflow / deployment_decision
 -> optional InferEdgeAIGuard provenance diagnosis
 -> deploy / review / blocked decision
+
+Supporting sidecar:
+InferEdgeEnv -> local-first run evidence registry / comparability checker
 ```
 
 The goal is not only to measure latency. The goal is to create reproducible evidence that can support deployment review.
@@ -77,6 +82,18 @@ Current role:
 - diagnoses artifact/source hash mismatch, precision/shape/backend/target mismatch, and missing provenance
 - emits `guard_analysis` that Lab can preserve in report/API bundles and reflect in deployment decisions
 
+### InferEdgeEnv
+
+Env owns local run evidence registry and comparability judgement.
+
+Current role:
+
+- stores Edge AI benchmark runs as local artifacts and SQLite registry rows
+- preserves result bundles through manifest/checksum based export/import
+- records sampler/resource metadata as supplemental evidence
+- judges same-condition / conditional / no comparability without producing a leaderboard or composite score
+- stays separate from Lab's validation / decision ownership
+
 ## Implemented Connections
 
 The current cross-repository loop is covered by documentation, fixtures, and smoke tests:

docs/portfolio/inferedge_portfolio_submission.md

@@ -6,6 +6,8 @@ InferEdge는 edge AI 모델을 변환, 실행, 비교, 진단하고 최종 배
 
 InferEdge is not a benchmarking tool, but an end-to-end validation pipeline that connects artifact provenance, runtime behavior, and deployment decisions.
 
+InferEdgeEnv complements this pipeline as a local-first run evidence registry and comparability checker. Lab remains the validation / decision layer; Env records whether benchmark evidence can be trusted and compared.
+
 이 프로젝트는 단순 latency benchmark가 아니라 artifact provenance, runtime result compatibility, deployment decision까지 연결한다. 목표는 "빠른 숫자"를 보여주는 것이 아니라, 어떤 모델과 산출물이 어떤 환경에서 실행되었고 그 결과를 배포해도 되는지 검토 가능한 evidence로 남기는 것이다.
 
 채용 포트폴리오용 5줄 요약:
@@ -26,6 +28,9 @@ ONNX model
 -> InferEdgeLab compare / API / job workflow / deployment_decision
 -> optional InferEdgeAIGuard provenance diagnosis
 -> deploy / review / blocked decision
+
+Supporting sidecar:
+InferEdgeEnv -> local-first run evidence registry / comparability checker
 ```
 
 ## 2. Problem Statement
@@ -44,16 +49,17 @@ InferEdge는 이 질문들을 CLI, JSON schema, report, API contract, worker bou
 
 ## 3. System Architecture
 
-InferEdge는 4개 repository를 하나의 pipeline으로 분리한다.
+InferEdge는 4개 core repository를 하나의 validation/decision pipeline으로 분리하고, InferEdgeEnv를 supporting run evidence sidecar로 둔다.
 
 ```text
 Forge = build / provenance
 Runtime = C++ execution / result export
 Lab = compare / report / API / deployment decision
 AIGuard = optional rule + evidence diagnosis
+Env = local run evidence registry / comparability checker
 ```
 
-이 구조의 핵심은 responsibility boundary다. Forge는 artifact를 만들고 provenance를 남긴다. Runtime은 실제 실행과 profiling evidence를 만든다. Lab은 결과를 비교하고 report/API bundle과 deployment decision을 생성한다. AIGuard는 optional evidence로 provenance mismatch나 failure signal을 진단한다.
+이 구조의 핵심은 responsibility boundary다. Forge는 artifact를 만들고 provenance를 남긴다. Runtime은 실제 실행과 profiling evidence를 만든다. Lab은 결과를 비교하고 report/API bundle과 deployment decision을 생성한다. AIGuard는 optional evidence로 provenance mismatch나 failure signal을 진단한다. InferEdgeEnv는 Lab decision과 분리된 local benchmark artifact, registry row, evidence bundle, comparability judgement를 관리한다.
 
 ## 4. Repository Roles
 
@@ -65,6 +71,7 @@ AIGuard = optional rule + evidence diagnosis
 | InferEdge-Runtime | C++ runtime execution and result export layer for ONNX Runtime/TensorRT edge inference validation. |
 | InferEdgeLab | Analysis/API layer for end-to-end Edge AI inference validation, reports, jobs, and deployment decisions. |
 | InferEdgeAIGuard | Optional deterministic diagnosis layer for provenance mismatch and suspicious inference result evidence. |
+| InferEdgeEnv | Local-first run evidence registry and comparability checker for Edge AI inference benchmark results. |
 
 **InferEdgeForge**  
 Build/provenance layer. ONNX 모델을 TensorRT/RKNN 등 edge deployment artifact로 변환하고, `metadata.json`, `manifest.json`, `worker_runtime_summary`로 source hash, artifact hash, backend, target, precision, shape, preset 정보를 보존한다.
@@ -78,6 +85,9 @@ Analysis/API/job/deployment decision owner. Runtime result JSON을 비교하고
 **InferEdgeAIGuard**  
 Rule + evidence diagnosis layer. Forge summary, Runtime worker_response, Lab result를 기반으로 artifact/source hash mismatch, backend/target/precision/shape mismatch, insufficient provenance 등을 deterministic detector로 진단한다. AIGuard는 LLM 추측이 아니라 rule + evidence 기반 detector 구조다.
 
+**InferEdgeEnv**
+Run evidence registry / comparability checker. Edge AI inference benchmark result를 local artifact와 SQLite registry로 고정하고, same-condition / conditional / no comparability judgement를 제공한다. Env는 deployment decision을 소유하지 않으며, Lab의 validation / decision layer와 분리된 evidence portability boundary다.
+
 ## 5. Key Implemented Features
 
 - Lab API response contract
@@ -94,7 +104,8 @@ Rule + evidence diagnosis layer. Forge summary, Runtime worker_response, Lab res
 - AIGuard worker provenance mismatch diagnosis
 - AIGuard guard_analysis preservation in Lab deployment decision/report smoke
 - Local Studio browser workflow for Run, Import, Jetson command helper, demo evidence replay, Compare View, and Lab-owned Deployment Decision inspection
-- 4개 repository README pipeline summary sync
+- InferEdgeEnv run artifact bundle, SQLite registry, export/import, sampler metadata, resource lookup, and comparability-first report UX
+- Core repository README pipeline summary sync plus InferEdgeEnv sidecar positioning
 
 ## 6. Validation Evidence