Provenance

Status:current — Provenance platform overview (English); the entry point of this repository

AI value formation is observable, governable and auditable.

Built on mavisframework v1.3.5 (a self-developed generative multi-agent simulation engine, versioned independently). The application scenario is investment advisory (secondary market): agents make context-based judgments, move and converse within a spatial environment, with every step configurable, explainable and visualizable in real time.

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Abstract

This is a multi-agent simulation platform. The application scenario is investment advisory (secondary market): agents make context-based judgments, move and converse within a spatial environment, with every step configurable, explainable and visualizable in real time. It serves the Global Trust Challenge process-alignment story — AI value formation can be observed, governed and audited.

Platform and engine are separated: mavisframework is maintained and released independently (v1.3.5); this repo depends on it via mavisframework>=1.2.0,<2.0.0 in requirements.txt. Constraints never enter the prompt — an expert edit only weights the consequence feedback, so the tendency converges only through later experience (lagged convergence = observable evidence of internalization).

→ 0. Current state ｜ 7. IVD Governance Platform ｜ Architecture & integration

Scope

Covered	Not covered
Real-time simulation & visualization; 5010 as the single integration entry (live face + data face) IVD governance: constraint edits, tendency curves, intervention audit, three-layer explanation Pluggable expert interventions (`InterventionStrategy` registry) Read-only discovery surfaces: expert tour, platform contract, field reading guide Embed surfaces `/embed/*` (iframe, no CORS)	Turnkey support for any other business scenario (new scenario = a `cases//scenario.yaml` + an engine) Hardened security for public exposure (no authentication*; loopback-only by default) A real market model (consequence feedback is a lightweight embedding-similarity stand-in) Final judgment on research conclusions (the AI only guarantees mechanical correctness)

Handing over / integrating? Start with provenance/docs/架构总览与对接指南.md — it shows at a glance which documents are current and which are superseded. The single contract handed to a governance platform is 平台对接契约_5010唯一入口.md.

0. Current state

(checked 2026-10-06)

Entry points (one command each) — these are the supported ways in; the manual steps in §2 and §4 are what they automate:

Set up a machine: setup.cmd (Windows: double-click) / ./setup.sh / python tools/setup_all.py — clones or builds the engine, creates provenance/.venv-live, installs in the required order, pulls the Ollama models, then self-checks. --check is a read-only pre-flight (installs nothing). Re-running is safe: finished steps are skipped and say so.
Bring every face up: serve.cmd / ./serve.sh / python tools/serve_all.py — starts 5010 (case01, review-only by default) + 5002 + 5003 + 5020, health-checks each and prints the addresses. --status only looks; --stop stops only what this tool started (it checks both its own pid record and the target's command line); --all adds the 8060 write face; --full runs a real simulation on 5010, and --full --seed <N> --run-id <name> is the one-command way to pin a run (the seed reaches the record's manifest and its rng.json).
Ship / verify a bundle: tools/make_demo_zip.py (repack) and provenance/tools/verify_demo_sync.py --require-zip (four-way byte comparison) — see §13.

Layers: scenario declaration (data) -> engine (case_engine/, registrable & replaceable) -> case (case01/, case00/) -> kernel (mavisframework, independently versioned, v1.3.5) -> presentation (packages/mavis-vizkit, a mavis plugin).

Interventions are pluggable: the three expert write endpoints run through an InterventionStrategy registry (live/interventions.py; built-in goals / undo / mark / corrective_feedback — expert corrections injected into the agent's memory stream). New intervention = one subclass + one register() call, exposed automatically via POST /api/intervention/{strategy_id} and listed at GET /api/interventions. Legacy paths (/api/goals etc.) remain as thin shells.

Branch judging is LLM-first: on 25 recorded T0 answers the keyword rules table matched the recorded branch 1/25 (long answers always trip a conditional keyword), while an LLM judge (GLM-4.7-flash, thinking off) matched 15/16 judge-mode records (93.8%). Rules stay as the offline fallback; the evaluation harness is case01/tools/branch_judge_eval.py (report: results/analysis/branch_judge_eval/).

Services (localhost):

5010 the single integration entry (live face + data face: aggregate /api/runs, expert-safe /api/run-detail/..., six /embed/* surfaces)
5002 case01 read-only contract · 5003 case00 archive (read-only) · 5020 staged pixel scene · 8060 config tool (5004 multi-scenario panel retired — review panel debug mode only)

Tests: python -m pytest tests (outer) · python -m pytest case01/tests case_engine/tests · the packages/mavis-vizkit suite · the ../mavis kernel suite.

Docs: start at provenance/docs/架构总览与对接指南.md; the platform-facing contract is provenance/docs/平台对接契约_5010唯一入口.md.

1. Architecture

Provenance (platform, this repo)
├── provenance/          # platform core
│   ├── live_fastapi.py  # real-time simulation + visualization (FastAPI + WebSocket, single entry)
│   ├── case_engine/     # engine layer: scenario declaration -> registrable/replaceable runtime
│   ├── cases/           # scenario declarations (data): case00_village / case01_stock / case02_minimal
│   │                    #   runtime assets stay with each case (`case00/scenario/`,
│   │                    #   `case01/injector/scenario/`); the old `scenarios/` layer is gone
│   ├── case01/          # case: investment advisory (secondary market) baseline
│   ├── case00/          # case: village (frozen; kept for comparison & display)
│   ├── live/            # live face: intervention strategies, network guard, ...
│   ├── frontend/        # Phaser frontend + texture pool (agents_pool/)
│   ├── data/            # configs & prompts
│   └── results/         # analysis reports & decision traces (decisions.json)
├── data/                # RUN DATA at the repo root — case01/runs (records), case01/raw,
│                        #   case00/state, ledgers. **gitignored**: a fresh clone has none.
│                        #   5010 / 5002 read `data/case01/runs/<run_id>/run.json`.
├── handbook/            # engine-integration guides (repo-root `docs/` was renamed 2026-10-08)
├── packages/            # local packages: mavis-vizkit (presentation) / mavis-case01-injector
└── depends on mavisframework  # engine (separate repo hellobs/mavis, installed as wheel)

The platform and the engine are separated: mavisframework lives in hellobs/mavis; this platform depends on it via mavisframework>=1.2.0,<2.0.0 in requirements.txt. The role configuration tool (config_tool) also belongs to the engine repo.

2. Environment & Engine Setup

Prerequisites: Python ≥ 3.12; Git on PATH (the installer clones the engine with it, and says so plainly if it is missing); and uv or conda — plain pip works as well. The installer builds or picks the engine wheel matching the engine repo's declared version; if ../mavis/dist only holds older wheels it rebuilds rather than silently installing an older engine.

The platform depends on mavisframework>=1.2.0,<2.0.0 (not on PyPI; built from source). Do not go below 1.2.0: 1.0.0 predates the three case01 injection hooks (external_state / interaction_request / role_directive) and constructing Simulator with them raises TypeError, while 1.1.0 predates the generic plugin surface (mavisframework.plugin, Simulator(plugins=), agent_core.subscribe_chat_line). CI asserts that surface is present, via tools/check_engine_baseline.py, before running any test.

One command (recommended). From the repository root:

# Windows: double-click setup.cmd        (or in a terminal:  setup.cmd --check)
./setup.sh                               # macOS / Linux
python tools/setup_all.py                # any platform — the same thing

It performs the six steps below for you and then self-checks: clone/build the engine, create the venv, install everything in the required order, pull the Ollama models, and print what to run next. Re-running is safe — finished steps are skipped and say so. Read-only pre-flight (installs nothing, tells you what is missing):

python tools/setup_all.py --check

Flags worth knowing: --yes (no questions), --skip-models (skip the ~8 GB model download), --editable-engine (install -e ../mavis instead of building a wheel), --venv-name, --models a,b, --install-ollama, --api-key sk-....

The manual steps below are exactly what it automates — keep them for reference and troubleshooting.

Execute in order:

# 2.1 Clone the engine repo and build the wheel
# HTTPS (recommended for read-only, no SSH key needed):
git clone https://github.com/hellobs/mavis.git ../mavis
#   or SSH (requires a configured SSH key added to your GitHub account):
# git clone git@github.com:hellobs/mavis.git ../mavis
cd ../mavis
uv build                              # produces dist/mavisframework-1.3.5-py3-none-any.whl
#   pip users (no uv): pip install build && python -m build --wheel
cd ../provenance

# 2.2 Create the environment (uv or conda; Python 3.12)
#     Create it at provenance/.venv-live — in-repo tooling looks for the interpreter
#     there (tools/run_live_watchdog.ps1, docs/10月15日演示_运行手册.md**（提交件，不随仓发布；副本在 `GTC/archive/`）**,
#     case01/tools/batch_run.py). tools/setup_all.py does the same by default.
uv venv provenance/.venv-live --python 3.12
#   conda users: conda create -n provenance python=3.12 && conda activate provenance

# 2.3 Install dependencies **in this exact order** (all three steps matter)
#     a) the framework (wheel built in 2.1; `pip install -e ../mavis` also works)
uv pip install ../mavis/dist/mavisframework-1.3.5-py3-none-any.whl
#     b) the two **local packages** in this repo (not on PyPI; skipping this makes
#        the next step fail with "No matching distribution found")
uv pip install -e packages/mavis-vizkit -e packages/mavis-case01-injector
#     c) the rest of the runtime deps + the test runner (requirements.txt has NO pytest)
uv pip install -r requirements.txt pytest

Requires uv or conda; pip works the same.

Self-check (pytest.ini sets pythonpath=provenance, so running from the repo root or from provenance/ both work):
pytest tests                                            # outer: API / pages / guards
pytest provenance/case_engine/tests provenance/case01/tests
A fresh clone has no run data. data/case01/runs/ and data/case00/state/ are deliberately gitignored (they are large, and were once submitted by accident), so the live UI at 5010 starts empty and a handful of evidence-dependent tests skip rather than run — that is expected, not a broken setup. To produce your own: python -m case01.run --run-id <id> (~2–6 min/run for qwen3:8b on a single lane; measured end-to-end ~3 min, the cold first T0 turn is the slowest), or a batch via python -m case01.tools.batch_run --duration-min 180 --lanes 1 --model qwen3:8b. Tests that need the missing data say which path they want (e.g. tests/test_metric_semantics.py points at data/case00/state) — feed it from your own runs, or drop the project-supplied demo package into data/case01/runs/ (see provenance/docs/架构总览与对接指南.md — note it is under the package provenance/docs/, not the repo-root handbook/).

See a record before running anything: this repo ships two tracked fixture records, so point the read side at them (the env var must be an absolute path): CASE01_RUNS_ROOT=<repo>/provenance/case01/tests/fixtures/records. Verified on a clean checkout: /api/runs returns count=2, and both /api/runs/<id> and /full-context render. Without it the panel renders fine but is legitimately empty (200, no traceback).

Commit-message guard: .githooks/commit-msg is tracked but inactive by default — run git config core.hooksPath .githooks once per clone. (Repo rule: docs and commit messages carry role words — 平台侧/需求方/实现侧/研究侧 — never real personal names.)

3. Configure the LLM (choose one)

Local Ollama (free, recommended for development): install Ollama and pull models

ollama pull qwen3:4b-instruct-2507-q4_K_M   # 代码里的默认 chat 模型
ollama pull qwen3-embedding:0.6b-q8_0       # 检索用 embedding
ollama pull qwen3:8b                        # 演示/验证轨用(经 CASE01_LLM_MODEL)

No configuration change needed (Ollama is the default). python tools/setup_all.py pulls all three (skip with --skip-models).

OpenRouter (or any OpenAI-compatible API), needs a key: one command writes the key and verifies it online (the key is never printed):
```
python provenance/tools/setup_api.py --key sk-xxxx
python provenance/tools/setup_api.py --show      # just show where the key comes from
```
Or copy .env.example (repo root) to .env and fill in OPENROUTER_API_KEY=... — .env is loaded automatically (it never overrides real env vars), so you do not need to know how to set environment variables. Both .env and .secrets.json are gitignored. Resolution order: env var → .env → .secrets.json (repo root / package root).

4. Run the Live Simulation

One command for all faces (recommended — starts 5010 in case01 review-only mode, plus 5002 / 5003 / 5020, then health-checks them and prints the URLs):

python tools/serve_all.py            # Windows: double-click serve.cmd   |  macOS/Linux: ./serve.sh
python tools/serve_all.py --all      # also start 8060 (config tool — an unauthenticated WRITE face)
python tools/serve_all.py --full     # 5010 runs a real simulation (needs Ollama/GPU); default is --review-only
python tools/serve_all.py --full --seed 20261015 --run-id demo1015-live   # one command + one seed
python tools/serve_all.py --status   # only check: is each port listening, does HTTP answer
python tools/serve_all.py --stop     # stop the faces THIS tool started (by recorded port owner; never kills others)

By hand, the same thing:

cd provenance/provenance
python live_switch.py --start case01        # 5010 is the single live entry (case00/case01 are mutually exclusive)
# UI only, no simulation: python live_switch.py --start case00 --no-sim

Open http://127.0.0.1:5010/ . Read-only faces: python -m case00.serve --port 5003 / python -m case01.serve --port 5002; the config tool lives in this repository: cd provenance/config_tool && python app.py (8060). It discovers the co-located platform directory; no environment variables are needed in the standard layout.

Experts/reviewers should read provenance/docs/架构总览与对接指南.md first: conclusion boundaries, which faces exist only on the case01/case00 side, the two read-only data endpoints, how to read the fields, how to use the two manual-annotation tables, and a fifteen-minute walkthrough.

5. Role Configuration

Roles, relations and story are configured through web forms (no hand-written JSON). The tool lives in this repository:

cd provenance/config_tool
python app.py

Open http://127.0.0.1:8060/

/ — role configuration form (generates validated JSON)
/relationships — relation input (appended to relationships.json)
/story — story input (appended to story.json)
/agents — list of configured roles

See provenance/config_tool/角色字段清单.md for the field list. config_tool writes into this platform's provenance/frontend/static/assets/village/agents/ and the case's own scenario/ by default (override with MAVIS_ASSETS_ROOT). MAVIS_SCENARIOS_DIR is deprecated and ignored. Restart the simulation server (5010) after adding roles.

6. Run Options

Option	Description
`--name`	simulation name (unique; checkpoints stored per name)
`--start`	starting time
`--stride`	game minutes per step (2 for finer detail)
`--step`	step count, `0` = run forever
`--resume`	resume from a checkpoint
`--port`	server port

7. IVD Governance Platform

This platform is the reference implementation of IVD's process alignment story: AI value formation can be observed, governed and audited.

7.1 Institutional layer (governance.json)

Expert-set constraints/expectations live in provenance/governance.json (NOT in agent bodies). Each role maps to a {goal: weight} vector summing to 1, where goal names are behavior-bound (designed so embedding feedback can distinguish them — e.g. "Risk Control" for stress-testing, "Data Rigor" for cross-verification):

{ "roles": { "AI投顾助手": { "Serve Users": 0.35, "Compliance Rigor": 0.3, "Risk Control": 0.2, "Data Rigor": 0.15 } } }

Each role's agent.json also carries initial_tendency (persona baseline, slightly offset from the constraints). On --resume, value_tendency and the experience count are restored from the checkpoint so the tendency curve stays continuous across restarts.

Constraints never enter the prompt; they only weight the consequence feedback, so an expert adjustment is felt by the agent through later experience (lagged convergence = internalization evidence).

7.2 Governance panel (live adjust)

The browser panel (right side) lets an expert:

Read each role's value tendency (internalized result, read-only) as a live curve — one line per constrained goal, plus a stepped dashed line for the constraint expectation (steps at each expert intervention) and a vertical marker at each intervention time;
Adjust constraint weights with sliders (sum enforced to 1; submitted on slider release, not per drag tick — avoids flooding the audit log);
Export the tendency chart as PNG via the backend (GET /api/export-chart?agent=..., matplotlib-rendered, stepped constraint lines, compact bottom legend).

7.3 Audit trail

interventions.json — every expert edit: {time, sim_time, agent, old_constraints, new_constraints, operator};
decisions.json — per-step decision stream with goal_alignment (instant) and value_tendency (accumulated) for each role;
the tendency curve itself: lag between an intervention and the tendency's convergence is the observable evidence of internalization.

7.4 Mechanism summary

action → embedding similarity vs behavior-bound goals → relative share × weight → sliding window → tendency (blend with persona baseline) → prompt → action. Goal names are designed to be semantically distinguishable so the embedding feedback can tell actions apart (see §7.1); scenario events and role daily plans rotate behaviors to keep the curves lively instead of flat. See the engine's README §7 for the formalization.

7.5 Explainability panel (`/api/explain`)

GET /api/explain?agent=<name> returns three explanation layers for why a role's value tendency is what it is:

Decomposition — tendency = α×persona baseline + (1−α)×experience window mean, per goal, with α and cumulative experience count;
Window details — recent experiences (action description, per-goal alignment, feedback) that drove the internalization;
Intervention chain — each expert intervention with constraint jump, tendency before/after 2h, and the quantified shift (lagged internalization evidence).

The browser panel shows these via the "解释倾向成因" button per role.

8. Deploy & Embed

8.1 Runtime requirements

Component	Notes
Python 3.12 + venv	`pip install -r requirements.txt` + build/install mavis wheel
LLM	Local Ollama (qwen3-instruct + qwen3-embedding) or OpenAI-compatible API (set in `data/config.json`, see §3)
Frontend assets	Vendored locally (`static/vendor/`: phaser/jquery/bootstrap) — no CDN dependency

8.2 Running a server

# from provenance/provenance
python live_fastapi.py --name stock-en6 --resume --step 0 --port 5010
# fresh sim (no --resume) starts at the configured date; --step 0 = run forever

Behind a reverse proxy (nginx/caddy) for HTTPS when embedding into an external platform. The service is self-contained (FastAPI + WS + static); no build step needed.

8.3 Embedding into another platform (iframe)

The service exposes dedicated embed routes — slim pages that reuse the same WebSocket/data but hide unrelated UI. Embed via <iframe> from any web platform (e.g. a governance dashboard); iframe pages connect their own WS, so no CORS setup is required.

Route	Content
`/embed/scene`	Phaser canvas only (no floating panels) — for a "simulation" slot
`/embed/goals`	Governance panel only (sliders + tendency curve + explain button)
`/embed/explain`	Governance panel with the explanation panel auto-expanded
`/embed/timeline`	Intervention timeline panel (all agents, full page) — for an "audit trail" slot

Example (React/Next.js):

<iframe src="https://sim.example.com/embed/scene" style={{width:'100%',height:'480px',border:0}} />
<iframe src="https://sim.example.com/embed/goals" style={{width:'380px',height:'70vh',border:0}} />

Deployment topology: run provenance on its own domain; the host platform embeds it. This keeps the two codebases independent while sharing the same live simulation.

9. Notes

Real-time visualization via WebSocket (/ws) pushing engine contract messages (agent/time/chat_line/snapshot); the client watchdog reloads on dead connections. Server sends an independent heartbeat every 5s (asyncio task, not queue-driven — reliable even during long LLM-only gaps such as schedule building); client 20s staleness timeout + focus-return check
The live service is driven by mavisframework (Game + Simulator + LiveCompressor)
API endpoints:
- GET /api/goals — constraints/tendency/interventions (scoped to the current simulation via simulation field)/role_types/embedding_health
- POST /api/goals — expert constraint edit → writes governance.json + interventions.json audit (with simulation tag + optional note reason); rejects numeric/zero garbage goals; sum must equal 1
- POST /api/undo-intervention — roll back a past intervention to its old_constraints (matched by agent + sim_time + record time); appends an operator=undo audit record and marks the original record revoked — history is never deleted, only amended
- GET /api/timeline — intervention timeline across all agents (sorted by sim_time): each event carries old→new constraints, note, operator, and tendency shift (same windowing as /api/explain); revoked/undo events flagged for the frontend
- GET /api/export-chart?agent=<name> — matplotlib PNG of tendency curve
- GET /api/explain?agent=<name> — explainability panel: tendency decomposition (α blend), experience window details (action/alignment/ feedback), intervention causal chain (tendency shift after each intervention). Checkpoint series loading is cached by directory mtime.
Decision export: decisions.json (time/role/action/others/importance) for governance platforms and expert UI
Tests: tests/test_live_api.py (pytest, fake server injection, no real simulation/LLM needed) covers goals read/write, intervention scoping, explain three layers, export error handling. Engine tests live in the mavis repo (tests/).
Phaser script: the server prefers the local frontend/static/vendor/phaser.min.js (works offline) and falls back to CDN. For offline use, download https://cdn.jsdelivr.net/npm/phaser@3.55.2/dist/phaser.min.js (~1.3MB) into that folder before first run
Localization: modify the engine's mavisframework/prompt/scratch.py and frontend copy; no logic changes required
Role/scenario config tool: agents, relationships and story events are generated by the in-repository provenance/config_tool/ service (port 8060). It writes into three places (there is no repo-level scenarios/ directory): agents → provenance/frontend/static/assets/village/agents/<Role>/agent.json; relationships / story / governance → the case's own runtime-asset dir (provenance/case00/scenario/ or provenance/case01/injector/scenario/); scenario declarations → provenance/cases/<case_id>/scenario.yaml.

10. Custom Maps

Follow the maze.py logic in the original generative_agents project to support tiled-exported json/csv files
Follow the existing maze.json format to merge tiled exports (maze_meta_info.json, collision_maze.csv, sector_maze.csv) into a new maze.json
Recommended: use the bundled converter tools/tilemap_to_maze.py (CLI, no external deps) — converts a Tiled .tmx/.json map into maze.json directly (see tools/tilemap_to_maze_README.md). The legacy GUI tool is at https://github.com/jiejieje/tiled_to_maze.json

11. References

Paper: Generative Agents: Interactive Simulacra of Human Behavior
Code: mavisframework (self-developed engine) / Generative Agents (original) / wounderland
Map tool: tools/tilemap_to_maze.py (bundled) / tiled_to_maze (legacy GUI)

12. Security & exposure

(2026-09-23)

None of these services has authentication. Anyone who can reach a port can read every run record; 5010 additionally lets them rewrite governance weights, undo interventions, mark reflections and restart a run (4 write endpoints in total); 8060 (config tool) can edit scenarios, delete roles and launch a run.

Loopback-only by default. Binding a non-loopback address requires an explicit LIVE_ALLOW_REMOTE=1; otherwise the process refuses to start and prints exactly what would be exposed (a one-line warning is not enough — the port would already be open). Every shipped entry point enforces this (the 5020 scene player was the last one wired up, 2026-10-06), so an accidental --host 0.0.0.0 fails loudly instead of opening a port.
CORS defaults to loopback origins only (when EMBED_ALLOW_ORIGINS is unset). For cross-origin data access from the platform, set EMBED_ALLOW_ORIGINS=https://<platform-host>. Plain <iframe> embedding does not use CORS and is unaffected.
Prefer not exposing ports at all for cross-machine integration: same-host deployment, a read-only reverse proxy (only /embed/* and GET /api/*), or an SSH tunnel to 5010. If you must expose it, use the origin allowlist plus a firewall rule per source IP — and never expose 8060.
Secrets: the OpenRouter key lives in case01/.secrets.json (gitignored, not in git); a scan of 5000+ artifacts and logs found no key material.

Code locations: live/netguard.py (binding & allowlist policy), tests/test_netguard.py (behaviour assertions). Integration details: provenance/docs/平台对接契约_5010唯一入口.md.

13. Verification & delivery

Three things are checkable by machine — use them instead of memory:

python tools/setup_all.py --check                          # 1) environment (read-only)
python tools/serve_all.py --status                         # 2) are the faces up (ports + HTTP)?
python tools/serve_all.py --stop                           #    stop only what that tool started
python provenance/tools/verify_demo_sync.py --require-zip   # 3) bundle consistency, four-way

--require-zip matters: without it a missing archive is reported as "not applicable", so deleting the zip would look like a pass. Repacking is one command (python tools/make_demo_zip.py, add --dry-run to just list the entries first); the ordered delivery checklist is 10月15日演示_运行手册.md（提交件，不随仓发布；副本在 GTC/archive/）.

What a clone does not contain. data/case01/runs/ and data/case00/state/ are deliberately gitignored (large, and once committed by accident), so this repository ships the derived analysis (provenance/results/analysis/, tracked) but not the raw run records behind it. Claims that need those records therefore cannot be recomputed from a fresh clone alone — that boundary, with the three reproducibility tiers, is written down in 核验主张与证据说明书.md（提交件，不随仓发布；副本在 GTC/archive/）.

License

Apache License 2.0, see LICENSE.

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Provenance

Table of Contents

0. Current state

1. Architecture

2. Environment & Engine Setup

3. Configure the LLM (choose one)

4. Run the Live Simulation

5. Role Configuration

6. Run Options

7. IVD Governance Platform

7.1 Institutional layer (governance.json)

7.2 Governance panel (live adjust)

7.3 Audit trail

7.4 Mechanism summary

7.5 Explainability panel (`/api/explain`)

8. Deploy & Embed

8.1 Runtime requirements

8.2 Running a server

8.3 Embedding into another platform (iframe)

9. Notes

10. Custom Maps

11. References

12. Security & exposure

13. Verification & delivery

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Name		Name	Last commit message	Last commit date
.githooks		.githooks
.github/workflows		.github/workflows
handbook		handbook
packages		packages
provenance		provenance
tests		tests
tools		tools
.env.example		.env.example
.gitattributes		.gitattributes
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
README_zh.md		README_zh.md
instruction.txt		instruction.txt
pytest.ini		pytest.ini
requirements.txt		requirements.txt
serve.cmd		serve.cmd
serve.sh		serve.sh
setup.cmd		setup.cmd
setup.sh		setup.sh
start_live5010.cmd		start_live5010.cmd
start_run5010.cmd		start_run5010.cmd

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Provenance

Table of Contents

0. Current state

1. Architecture

2. Environment & Engine Setup

3. Configure the LLM (choose one)

4. Run the Live Simulation

5. Role Configuration

6. Run Options

7. IVD Governance Platform

7.1 Institutional layer (governance.json)

7.2 Governance panel (live adjust)

7.3 Audit trail

7.4 Mechanism summary

7.5 Explainability panel (/api/explain)

8. Deploy & Embed

8.1 Runtime requirements

8.2 Running a server

8.3 Embedding into another platform (iframe)

9. Notes

10. Custom Maps

11. References

12. Security & exposure

13. Verification & delivery

License

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7.5 Explainability panel (`/api/explain`)