# MIND MAP AUDIT — Atomizer Master Mind Map Audit target: `atomizer-master-mind-map.excalidraw` Audit date: 2026-02-21 Mind-map text nodes extracted: 385 Scope reviewed: - `atomizer-master-mind-map.excalidraw` (all text extracted) - `/home/papa/repos/Atomizer/` (full directory inventory) - `/home/papa/repos/Atomizer/docs/ARCHITECTURE.md` - `/home/papa/repos/Atomizer/docs/QUICK_REF.md` - `/home/papa/repos/Atomizer/docs/protocols/` (all files) - `/home/papa/repos/Atomizer/docs/plans/` (all files) - `/home/papa/repos/Atomizer/docs/guides/` (all files) - `/home/papa/repos/Atomizer/optimization_engine/` (all subdirs/files inventoried) - `/home/papa/repos/Atomizer/atomizer-dashboard/` (all subdirs/files inventoried) - `/home/papa/atomizer/` (HQ workspace + configs + bridges + agent workspaces) - `/home/papa/atomizer/workspaces/manager/context-docs/` (all files) --- ## A. COMPLETENESS AUDIT ### A1) Top-level Atomizer codebase coverage (`/home/papa/repos/Atomizer`) | Module | Mind map status | Notes | |---|---|---| | `optimization_engine/` | ✅ Present | Strongly represented (Z3 + Z4 + ACE + validation + study mgmt). | | `docs/` | ⚠️ Present but incomplete | Only high-level protocol names are shown; actual docs are much deeper and include many plans/guides not represented. | | `hq/` | ⚠️ Present but incomplete | Mind map has "Atomizer HQ" concept, but current implemented cluster reality is not accurately represented. | | `studies/` | ⚠️ Present but incomplete | Mentioned conceptually as study outputs/archives; actual study folder lifecycle and formats are not mapped. | | `config/` | ✅ Present | Mentioned via spec/config/sampler/paths. | | `templates/` | ⚠️ Present but incomplete | Mentioned as "Templates System" but no linkage to actual template registries and CLI/template loaders. | | `atomizer-dashboard/` | ⚠️ Present but incomplete | Dashboard + Canvas shown, but concrete backend/frontend subsystems are mostly missing. | | `atomizer_field_training_data/` | ❌ Missing | This training-data corpus is central to neural workflow (`OP_05`) and not explicitly represented as a storage domain. | | `knowledge_base/` | ⚠️ Present but incomplete | "LAC" is present conceptually; concrete structure/use paths are missing. | | `examples/` | ❌ Missing | Useful onboarding/validation material not represented. | | `tools/` | ❌ Missing | Significant tooling area (including MCP/NX tooling) is not represented. | | `tests/` | ❌ Missing | No explicit test/verification zone despite quality emphasis. | | `projects/` | ❌ Missing | Active project workspace model is not represented. | | `mcp-server/` | ❌ Missing | No representation of MCP tool layer. | | `nx_journals/` | ❌ Missing | NX integration mechanics are shown conceptually but journal assets not represented. | | `archive/` | ⚠️ Present but incomplete | "History/Archive" concepts appear, but archive code/docs split is not represented. | ### A2) Optimization engine completeness (`/home/papa/repos/Atomizer/optimization_engine`) Directory coverage vs map: - `19/45` directories are directly represented by name/concept. - `26/45` are missing as explicit module-level nodes. #### ✅ Present and generally accurate - `core/` (`OptimizationRunner`, Optuna integration, method selection) - `plugins/` + hook framework - `study/` (`creator`, `state`, `continuation`, `benchmarking`, `reset`) - `reporting/` (`results_analyzer`, `landscape_analyzer`, `visualizer`, report generation) - `context/` (`playbook`, `reflector`, `feedback_loop`, `session_state`, `cache_monitor`, `compaction`) - `gnn/` (neural/graph functions, polar graph, backfill concepts) - `intake/`, `interview/`, model introspection concept - `nx/` execution/manipulation layer - `validation/` and `validators/` #### ⚠️ Present but incomplete/inaccurate - Hook lifecycle details are incomplete/wrong for current core runner (details in Section B). - "Inline calculations" and `post_calculation` are shown as default lifecycle; in code this is mainly in the `future/` LLM runner path. - Neural subsystem is represented conceptually, but many implementation modules are not mapped (`processors/surrogates/*`, `gradient_optimizer.py`, ensemble/auto-trainer stack). - Spec system is present at concept level, but concrete implementation modules are missing (`config/spec_models.py`, `config/spec_validator.py`, `schemas/atomizer_spec_v2.json`). #### ❌ Missing entirely (important) - `processors/surrogates/` family (`neural_surrogate.py`, `auto_trainer.py`, ensemble/generic/adaptive components) - `config/spec_models.py`, `config/spec_validator.py`, `config/migrator.py`, `config/setup_wizard.py` - `utils/` operational modules (`dashboard_db.py`, `realtime_tracking.py`, `study_archiver.py`, `study_cleanup.py`, `trial_manager.py`) - `model_discovery/` - `hooks/nx_cad/*` and `hooks/nx_cae/*` granular modules - `extractors/` concrete breadth is underrepresented (large extractor library beyond a generic "OP2/F06 parsing") ### A3) Dashboard completeness (`/home/papa/repos/Atomizer/atomizer-dashboard`) Directory coverage vs map: - `18/36` directories represented by concept. - `18/36` missing. #### ✅ Present and accurate - Dashboard web UI exists (`frontend`) with analytics/monitoring concepts. - Canvas `SpecRenderer` exists and is central. - WebSocket real-time streams exist. - Intake/introspection/spec API layers exist. #### ⚠️ Present but incomplete - Mind map does not capture backend route/service decomposition: - `backend/api/routes/*` (spec/intake/nx/optimization/context/devloop/etc.) - `backend/api/services/*` (spec manager, interview engine, nx introspection, session manager) - `backend/api/websocket/optimization_stream.py` - Frontend architecture is far richer than the map shows: - `components/canvas/*` (nodes, panels, palette, visualization) - `hooks/*` (spec store, websocket, optimization stream, chat/tooling) - multiple pages (`Studio`, `CanvasView`, `Dashboard`, `Analysis`, `Results`, `Setup`, `Insights`) #### ❌ Missing entirely - Claude Code integration route/services (`routes/claude_code.py`, services around Claude sessions). - DevLoop UI integration (`components/devloop`, `routes/devloop.py`). - Intake UI implementation details (`components/intake/*`) beyond generic "file drop" notion. ### A4) Documentation completeness #### Protocol docs (`/home/papa/repos/Atomizer/docs/protocols`) - All protocol files are effectively missing at file-level in the map. - The map references OP/SYS identifiers, but does not map the actual protocol corpus structure: - `operations/OP_01..OP_08` - `system/SYS_10..SYS_18` - `extensions/EXT_01..EXT_04` Status: ⚠️ Present at label level, ❌ missing at module/file level. #### Plans/guides - `docs/plans/*` and `docs/guides/*` are largely unrepresented as architecture assets. - These docs include key implementation/reality indicators (Studio plan, introspection plan, unified config, dashboard implementation status, neural workflow guides). Status: ❌ Missing as a first-class architecture layer. ### A5) Agent system completeness (`/home/papa/atomizer` + context docs) #### ✅ Present concepts - OpenClaw multi-agent orchestration concept. - Job queue and Syncthing bridge concept. - Trust boundaries / approval gates concept. #### ⚠️ Present but inaccurate - Current production-like state is **8-agent multi-instance cluster** (Discord-heavy), not 13-agent fully active operation. - Slack-first framing is now mixed/outdated relative to current implemented state. #### ❌ Missing entirely - Discord bridge and multi-instance cluster mechanics: - `config/openclaw-discord.json` - `discord-bridge/` implementation - cluster operational docs (`docs/hq/08-SYSTEM-IMPLEMENTATION-STATUS.md`) - Mission control / taskboard orchestration system: - `mission-control/` and `workspaces/shared/taskboard.json` - Agent workspace protocol surface (`workspaces/*/AGENTS.md`, `TOOLS.md`, `MEMORY.md`, shared skills/protocols) - Manager founding context-docs are not represented as a roadmap-vs-reality lens. --- ## B. ACCURACY AUDIT ### B1) Data flow and lifecycle correctness #### ⚠️ Trial lifecycle in map is not fully accurate for active core runner Map shows 10-step lifecycle including `pre_mesh` and `post_calculation` each trial. Actual core runner (`optimization_engine/core/runner.py`) executes: 1. `pre_solve` 2. model update 3. `post_mesh` 4. solve 5. `post_solve` 6. extraction 7. `post_extraction` 8. constraints/objective composition 9. `custom_objective` Evidence: - `optimization_engine/core/runner.py:362` - `optimization_engine/core/runner.py:378` - `optimization_engine/core/runner.py:395` - `optimization_engine/core/runner.py:443` - `optimization_engine/core/runner.py:506` `post_calculation` exists mainly in future/LLM path: - `optimization_engine/future/llm_optimization_runner.py:294` Verdict: ⚠️ Partially accurate, but currently overstates default lifecycle behavior. ### B2) Agent topology claim is outdated/inaccurate Map claim: "13 AGENTS (OpenClaw Multi-Agent)" and Slack-centered operation. Current implemented config and status indicate **8 agents** and strong Discord routing: - `config/openclaw-discord.json` agent IDs at lines 19, 29, 38, 47, 56, 65, 74, 83 - `dashboard/SPEC.md:32` ("8 agents") - `docs/hq/08-SYSTEM-IMPLEMENTATION-STATUS.md:11` (8 independent OpenClaw processes) Verdict: ⚠️ Roadmap intent is represented as current state; should be split into "current" vs "target". ### B3) Protocol numbering consistency mismatch Map references: - Operations OP_01–OP_11 - System SYS_10–SYS_20 Primary protocol docs in repo (`docs/protocols/README.md`) currently include: - OP_01..OP_08 - SYS_10..SYS_18 Additional OP_09/10/11 and SYS_19/20 exist in HQ skill/workspace protocol layer under `/home/papa/atomizer/skills/atomizer-protocols/protocols/`. Verdict: ⚠️ Partially accurate but conflates primary repo protocol set with HQ extension protocol set. ### B4) Neural architecture claims are partly aspirational and path-outdated - Concepts are directionally right (GNN, hybrid switching, uncertainty, training/export pipeline). - But some documented file references are stale (e.g., `atomizer-field/...` references; no `atomizer-field` directory in `/home/papa/repos/Atomizer`). - Performance numbers (4.5ms, 2200x, <3% error) appear as hard facts without direct benchmark provenance in current map. Verdict: ⚠️ Good conceptual framing, but should separate verified metrics from target/benchmark claims. ### B5) Dashboard/Canvas sync claim is mostly accurate Map claim: "Spec ↔ Canvas ↔ Backend ↔ Claude, WebSocket real-time updates" Evidence in code: - Spec API + sync route (`backend/api/routes/spec.py`) - Canvas `SpecRenderer` (`frontend/src/components/canvas/SpecRenderer.tsx`) - Spec WebSocket hook (`frontend/src/hooks/useSpecWebSocket.ts`) - Optimization stream websocket (`backend/api/websocket/optimization_stream.py`, `frontend/src/hooks/useOptimizationStream.ts`) Verdict: ✅ Accurate at architecture level. ### B6) Failure mode claims are mixed - NX crash continuation: supported by `study/continuation.py` and resume flow. - Disk optimization protocol: exists (`OP_07`) and utilities (`study_archiver.py`, cleanup modules). - "Agent failure → circuit breaker (2 retries max)" is not clearly implemented as a concrete engine behavior in inspected runtime code. Verdict: ⚠️ Mixed; some claims are real, some are policy/plan-level rather than implemented behavior. ### B7) Ordering/zone labeling quality issue - Z7 appears before Z6 in the canvas text order. Verdict: ⚠️ Not a functional bug, but hurts readability and narrative flow. --- ## C. STRUCTURAL CRITIQUE ### C1) Zone organization quality Current map is strong as a single narrative board, but it mixes: - current implementation - planned future state - company operating model - protocol taxonomy - performance claims in one layer, with no visual distinction. This is the main structural weakness. ### C2) Recommended grouping model Use 4 super-layers (with explicit badges): 1. `Runtime (Now)` 2. `Roadmap (Planned)` 3. `Governance/Process` 4. `Interfaces & Data Contracts` This prevents roadmap items (13-agent full company, OP/SYS full expansion) from being misread as already live. ### C3) Flow direction critique Left→right is workable for technical flow, but the map currently has at least 3 competing flows: - optimization data pipeline - HQ orchestration/agent workflow - product evolution roadmap These should be separated into parallel swimlanes rather than interwoven in one horizontal direction. ### C4) Missing relationship edges Important edges absent or too implicit: - `AtomizerSpec schema` ↔ `spec_models.py/spec_validator.py` ↔ `dashboard spec APIs` - `training_data_exporter` ↔ `atomizer_field_training_data/` ↔ `neural_surrogate` - `mission-control/taskboard` ↔ `agent orchestration` - `docs/protocols` (canonical) ↔ `skills/atomizer-protocols` (operational overlays) - `Discord/OpenClaw cluster status docs` ↔ infrastructure zone --- ## D. PROPOSED CHANGES 1. [ADD] Split each major zone into `NOW` and `TARGET` bands. 2. [ADD] New explicit node group: `Spec Contract Layer` with: - `optimization_engine/schemas/atomizer_spec_v2.json` - `optimization_engine/config/spec_models.py` - `optimization_engine/config/spec_validator.py` - `atomizer-dashboard/backend/api/routes/spec.py` 3. [ADD] New explicit node group: `Surrogate Runtime Layer` with `processors/surrogates/*` and training/export feedback loop. 4. [ADD] New node group for `Taskboard/Mission Control` (`/home/papa/atomizer/mission-control`, `workspaces/shared/taskboard.json`). 5. [ADD] New `Platform Runtime` block showing current 8-agent OpenClaw multi-instance + Discord bridge reality. 6. [MOVE] Put Z6 before Z7 in board order. 7. [MOVE] Move roadmap phases out of core architecture flow into a dedicated "Evolution" strip. 8. [FIX] Trial lifecycle to match current core runner (or label current one as "LLM/Future path"). 9. [FIX] Agent count/state labeling: "Current: 8 active" and "Target: 13 full company". 10. [FIX] Protocol counts: distinguish canonical repo protocols (`OP_01..08`, `SYS_10..18`) from HQ extension protocols (`OP_09..11`, `SYS_19..20`). 11. [FIX] Replace/annotate stale `atomizer-field` path references with current paths (or mark as external planned module). 12. [FIX] Mark performance numbers as `benchmarked on /` or `target`. 13. [EXPAND] Dashboard architecture with backend route/service and frontend canvas/store/websocket decomposition. 14. [EXPAND] Optimization engine internal packages: `config/`, `processors/`, `utils/`, `validation/validators`. 15. [EXPAND] Infrastructure: include Discord/OpenClaw config files and bridge implementation. 16. [EXPAND] Include tests/quality toolchain as a first-class architecture concern. 17. [REMOVE] Unqualified hard claims that are not code-backed (e.g., specific retry/circuit-breaker behavior) unless source-linked. --- ## E. INNOVATIVE SUGGESTIONS 1. Add a **"Truth Overlay"**: - Green border = implemented and source-verified. - Yellow border = implemented but partial. - Blue border = planned. - Red border = known mismatch/debt. 2. Add a **"Source Pin"** mini-label on each non-trivial node: - Example: `runner.py:362` or `openclaw-discord.json:19`. - This turns the map into a navigable architecture index. 3. Add a **"Time Stamp"** to volatile zones (agents, infra, roadmap): - `Verified: 2026-02-21`. 4. Add a **"10-minute onboarding path"** (numbered route): - 1) Inputs/Spec - 2) Runner lifecycle - 3) Dashboard/spec sync - 4) Neural acceleration path - 5) HQ orchestration path 5. Add a **dual-lane architecture**: - Lane A: Technical optimization runtime - Lane B: Human/agent orchestration runtime - Join points explicitly shown (approval gates, deliverables, KB ingestion). 6. Add a **contract matrix sidebar**: - File contracts: `atomizer_spec.json`, `study.db`, `history.json`, `model_introspection.json`, report outputs. - Producer/consumer per contract. 7. Add a **risk/fragility overlay**: - Mark components known to be brittle (cross-OS sync, bridge/routing constraints, token/provider dependencies). 8. Add a **"planned vs decommissioned" marker** for legacy artifacts (old dashboard paths, old bridge assumptions, old protocol doc locations). --- ## Bottom Line The map is impressive as a vision artifact, but currently it blends roadmap and reality too aggressively. As a living architectural blueprint, it needs a strict `Now vs Target` separation, tighter source anchoring, and fuller module coverage in the optimization engine/dashboard/agent runtime layers.