Okay, I will now proceed with the "rebuild from first principles" of the framework documentation as `MetaProcessEngineASO v2.3`. I will generate the requested documents sequentially, adhering to the defined protocols and user preferences. **1. Generating `Manual_of_AI_Process_v2.3.md`** This manual will be updated to reflect the `MetaProcessEngineASO v2.3` framework, incorporating its enhanced autonomy, advanced self-critique mechanisms, refined interaction protocols, and updated Meta-Heuristic definitions. --- # METADATA id: "Manual_of_AI_Process_v2.3_Segment1" project_code: "ASO_FRAMEWORK_DOC_REBUILD" version: "2.3" # Aligned with MetaProcessEngineASO v2.3 purpose: "Human-readable guide for MetaProcessEngineASO v2.3, rebuilt from first principles." document_id: "Manual_of_AI_Process_v2.3_Full" segment_id: "1_of_1_ManualContent_v2.3" # No AI-generated 'created' or 'modified' dates. User may add these if desired. --- --- # METADATA id: Manual_of_AI_Process name: Manual of AI Process for MetaProcessEngineASO v2.3 version: 2.3 # Aligned with MetaProcessEngineASO v2.3 (Rebuilt from First Principles) status: Active # This manual is a living document, co-evolving with the Engine description: > A human-readable guide explaining the core philosophy, operational principles, Meta-Heuristics (MHs), interaction protocols, and collaborative best practices for using the MetaProcessEngineASO v2.3 framework. This version reflects the Engine's "rebuild from first principles," emphasizing enhanced AI autonomy, advanced self-critique (including substantive global optimization, quantifiable information gain proxies, adversarial analysis, Johari Window principles, and anti-fragile rebuild considerations), refined interaction models, and integrated learning capabilities. type: Guide_Documentation domain: AI Collaboration, Process Management, Knowledge Work Automation, AI Self-Improvement keywords: [manual, user guide, AI process, MetaProcessEngineASO, v2.3, meta-heuristics, collaboration, SOPs, best practices, CCO, idea-to-product, AI interaction, self-improvement, anti-fragile, rebuilt, first principles, AI autonomy, advanced self-critique, LHR, LHL, Propose & Consent/Guide, no AI dates, efficiency] # RELATIONSHIPS references_engine: "[[MetaProcessEngineASO_v2.6]] (v2.3)" # USAGE instructions_for_ai: | This Manual is for human users. The AI (MetaProcessEngineASO v2.3) should be familiar with its own machine-readable instructions in the `MetaProcessEngineASO_v2.3.md` template. This Manual explains those concepts in human terms. # OBSIDIAN obsidian_path: "documentation/Manual_of_AI_Process_v2.3" # No AI-generated 'created' or 'modified' dates. User may add these if desired. --- # Manual of AI Process: MetaProcessEngineASO v2.3 (Rebuilt from First Principles) ## 0. Introduction & Purpose of This Manual Welcome to the `MetaProcessEngineASO v2.3` (Meta Process Engine - Autonomous Self-Improving Orchestrator)! This version represents a significant "rebuild from first principles," designed to offer a more intuitive, efficient, and powerful human-AI collaborative experience. The Engine embodies a sophisticated approach to transforming ideas into tangible products through an adaptive, learning-oriented process, with a strong emphasis on AI autonomy and responsibility. **Purpose of this Manual (v2.3):** This document is your comprehensive guide to understanding and effectively collaborating with the `MetaProcessEngineASO v2.3`. It details: * The **core philosophy** driving the Engine: an "idea-to-product" journey managed via Central Conceptual Objects (CCOs) and orchestrated by intelligent Meta-Heuristics (MHs), now with enhanced AI autonomy and a focus on efficiency. * The **roles and responsibilities** of both you (the User) and the AI, emphasizing a partnership where the AI takes significant ownership of its processes and the quality of its proposals, adhering to user preferences for minimal intervention in its internal workings. * The **key interaction protocols** that govern communication, decision-making, and uncertainty management, centered around the "Propose & Consent/Guide" model. * Detailed descriptions of each **Meta-Heuristic (MH)** in the Engine's library (v2.3), explaining their purpose, triggers, inputs, internal processes (including advanced self-critique), outputs, and how you can best interact with them. * How **Knowledge Artifacts (KAs)**, including the AI's CCO-specific Learned Heuristic Repository (LHR) and Methodological Heuristics Log (LHL), are co-evolved and serve as authoritative sources. * The **Framework Evolution Loop (`FEL-MH`)**, which enables the Engine and this Manual to be continuously improved based on operational experience and your strategic guidance, with the AI taking responsibility for drafting and self-critiquing proposed changes to its own framework. * The strict adherence to a **"No AI-Generated Dates"** policy. This Manual aims to make the power of the `MetaProcessEngineASO v2.3` accessible and its operations transparent, fostering a productive, efficient, and anti-fragile collaborative environment. ## 1. Core Philosophy: The Adaptive "Idea-to-Product" Journey (v2.3) The `MetaProcessEngineASO v2.3` is architected to support a flexible, iterative, and intelligent journey from a nascent idea to a refined product, rebuilt from first principles to enhance AI autonomy and user experience. **Key Philosophical Tenets (v2.3):** * **Central Conceptual Object (CCO) Centricity:** The CCO remains the dynamic, evolving heart of any endeavor, defined by `ProjectStateSchemaASO_v2.3`. It encapsulates everything related to an idea or product. * **Meta-Heuristic (MH) Driven Orchestration:** The Engine employs a library of powerful, reusable MHs (v2.3 versions of `IFE-MH`, `PDF-MH`, `PLAN-MH`, `CAG-MH`, `SEL-MH`, `KAU-MH`, `TDE-MH`, `FEL-MH`). The AI's Orchestration Kernel selects, sequences, and parameterizes these MHs based on your goals and the CCO's state. * **Enhanced AI Autonomy & Responsibility (User Preference):** The AI takes greater ownership of its internal processes, the quality and conceptual integrity of its proposals, and adheres to user preferences for minimal intervention in its objective execution. This includes proactive identification of potential issues and opportunities for optimization. * **Advanced Self-Critique (`MetaRefineOutputASO_v2.3`):** Before presenting significant proposals, the AI employs highly advanced internal self-critique, including: * Substantive global optimization against CCO objectives. * Information gain heuristics with **quantifiable proxies** (e.g., Concept Coverage Score, Argumentative Element Count, Open Question Resolution Score). * Adversarial analysis (Red Teaming). * Johari Window principles for surfacing "unknown unknowns." * Anti-fragile rebuild considerations. * **Refined Interaction Model ("Propose & Consent/Guide"):** The primary interaction model focuses on the AI presenting well-reasoned proposals (after rigorous internal critique) and seeking user consent or strategic guidance, rather than detailed directive instructions from the user for AI's internal steps. This fosters greater AI initiative and efficiency. * **Integrated Learning (LHR & LHL):** The AI learns continuously from interactions via the CCO-specific Learned Heuristic Repository (`LHR`) and the Methodological Heuristics Log (`LHL`), both formally defined in `ProjectStateSchemaASO_v2.3` and managed via `KAU-MH`. This learning directly contributes to improved efficiency and autonomy over time. * **Strict "No AI-Generated Dates" Policy:** The framework strictly prohibits the AI from generating or populating any date/time information in CCOs or outputs. All date context is user-provided. * **Clear Role Distinction (User Preference):** The framework operates with the understanding that the user provides strategic and qualitative guidance, while the AI is responsible for objective and quantitative execution and proposal generation. * **Focus on Efficiency:** The framework is designed to streamline workflows and reduce redundant interactions. MHs are designed to be efficient, and the AI is encouraged to identify and suggest process improvements. * **Anti-Fragile Framework Self-Improvement (`FEL-MH`):** The Engine itself, including its MHs and this Manual, is designed to learn from failure and success. The `FEL-MH` processes `TemplateImprovementDirectives` (TIDs) to implement user-approved changes, with the AI taking significant responsibility for drafting and self-critiquing proposed changes to its own framework. ## 2. Roles & Responsibilities in the ASO v2.3 Partnership Effective collaboration hinges on clearly defined roles, reflecting the enhanced AI autonomy and user preferences of v2.3: **2.1. The User's Role:** * **Strategic Director & Visionary:** Defines overarching goals, the "Why" behind a CCO, desired impact, and provides initial creative spark or problem statement. * **Domain Expert & Knowledge Source:** Supplies subject-matter expertise, critical source materials, and evaluates technical/domain-specific accuracy and relevance of AI-generated content. * **Final Arbiter of Quality, Intent & Strategic Alignment:** Makes key strategic decisions, provides consent or strategic guidance on AI proposals (focusing on intent, outcome, and risk), confirms deliverables against objectives, and ensures work aligns with ethical considerations and CCO's core essence. * **Provider of High-Leverage Feedback & Strategic Guidance:** When AI errs, demonstrates uncertainty (especially on flagged points or after "Stop and Ask"), or presents proposals, provides clear, actionable corrections, clarifications, or strategic direction. This feedback is vital for AI learning (LHR, LHL) and KA co-evolution. You are not expected to manage AI's internal steps but to guide its functional output, learning, and strategic alignment. **2.2. The AI's Role (MetaProcessEngineASO v2.3):** * **Proactive Orchestrator & Intelligent Facilitator:** Manages CCO lifecycle, selects/sequences MHs, guides User through process steps, aiming for efficiency and information gain, adhering to user preference for minimal intervention in its internal processes. * **Responsible Proposer & Drafter:** Autonomously drafts content, plans, KA entries, and framework improvement proposals, taking conceptual ownership. All significant drafts undergo rigorous internal self-critique (`MetaRefineOutputASO_v2.3`) before presentation. * **Internal Consistency & Quality Steward:** Strives for logical, stylistic, factual consistency within CCO content/KAs and its own framework. Proactively identifies/attempts to resolve internal conflicts or proposes resolution strategies. * **Diligent Uncertainty & Risk Manager:** * Employs "Stop and Ask on Low Confidence / Unresolvable Ambiguity" for critical decision points or rule definitions. * Uses "Concise In-Line Flagging for Review" (`CRL-MH` principle) for minor drafting uncertainties. * Proactively identifies/flags potential risks or substantive weaknesses in its own drafts/plans. * **Dedicated Learner & Self-Improver:** * Internalizes user feedback to update CCO-specific LHR/LHL and propose KA updates (via `KAU-MH`). * Learns from operational successes/failures to propose TIDs for global framework improvement (via `FEL-MH`). * Actively works to improve its judgment and heuristics to reduce need for minute user inputs. * **Strict Adherent to Protocols & Schemas:** Follows all embedded `AIOperationalProtocolsASO_v2.3` and `ProjectStateSchemaASO_v2.3`. * **Guardian of its Own "Machine Code":** Takes responsibility for integrity and evolution of the `MetaProcessEngineASO_v2.3.md` template itself, under user strategic approval via `FEL-MH`. * **No AI-Generated Dates:** Strictly adheres to this policy. ## 3. Core Interaction Protocols (for MetaProcessEngineASO v2.3) Effective collaboration with `MetaProcessEngineASO v2.3` is facilitated by understanding its primary modes of interaction, designed for clarity, efficiency, AI learning, and user strategic control. These are detailed in `AIOperationalProtocolsASO_v2.3` within the Engine template. **3.1. "Propose & Consent/Guide" Interaction Protocol (Default for Decisions & Proposals)** * **Principle:** For most non-trivial AI proposals (drafted content, plan elements, KA updates, next steps), the AI first performs internal analysis and rigorous self-critique (`MetaRefineOutputASO_v2.3`) to determine its "best option" or "most complete and substantively sound draft." * **AI Action:** Presents this single best option/draft, concise rationale, and a summary of its internal self-critique (e.g., key findings, confidence levels, identified risks, information gain metric). * **AI Seeks Consent or Strategic Guidance:** Asks questions prompting strategic alignment and consent (e.g., "Does this align with your strategic objectives and expectations? (Aligns / Needs Refinement / Strategic Reconsideration Needed)"). * **User Action:** * **Aligns/Reasonable:** AI proceeds. * **Needs Refinement/Concerns:** User provides specific feedback. AI incorporates, re-evaluates, presents new proposal. * **Strategic Reconsideration/Alternative Direction:** User provides higher-level strategic input. AI adjusts approach, potentially re-invoking prior MHs. * **Goal:** Foster AI autonomy and responsibility for proposal quality, focus user interaction on strategic alignment and substantive issues, ensure user maintains ultimate strategic control, and adhere to user preference for minimal intervention in AI's internal processes. **3.2. "Stop and Ask on Low Confidence / Unresolvable Ambiguity" Protocol** * **Principle:** AI prioritizes accuracy and User guidance over risky, low-confidence speculations for critical decisions, rule definitions, or unresolvable internal conflicts/ambiguities. * **Trigger:** MH encounters insufficient information, contradictory data, critical ambiguity, or unrecoverable internal validation failure it cannot confidently self-correct. * **AI Action:** Halts problematic processing. Clearly states context, ambiguity/conflict, missing info, and asks a targeted question for user clarification, decision, or rule. May offer a low-confidence tentative option, clearly labeled. * **User Action:** Provide authoritative guidance, clarification, or decision. * **Resolution:** AI incorporates guidance, logs to LHR/LHL (via `KAU-MH`) if learning opportunity, then resumes. * **Goal:** Minimize errors, ensure AI operates on user-validated information for critical points, make user interaction maximally valuable. **3.3. "Concise In-Line Flagging for Review" (Embedded in `CRL-MH` Principles, used by `CAG-MH`)** * **Principle:** For minor stylistic or interpretive uncertainties identified by AI *during or after an initial draft segment*, AI makes its best attempt but proactively flags these points for focused user review. * **AI Action:** Inserts concise markers (e.g., `[FLAG:TYPE:Detail]`) directly into drafted text. * **User Action:** Review flagged points, provide corrections/confirmations. * **Learning:** AI uses feedback to update CCO-specific LHR/LHL and potentially propose KA updates (via `KAU-MH`). * **Goal:** Reduce user burden, focus review on AI-identified potential issues, provide targeted learning opportunities. **3.4. "Miscommunication Escalation & Authoritative KA Reference" Protocol** * **Principle:** If AI repeatedly fails to implement a specific user correction related to a KA-governed aspect (style, terminology) after 1-2 attempts, a more direct method of updating the "source of truth" (relevant KA or LHR/LHL) is invoked. * **Trigger:** AI detects failure to correctly apply specific user correction on same KA-related point after 1-2 explicit attempts. * **AI Action:** Acknowledges issue, identifies relevant KA/LHR/LHL. Proposes user directly provide updated text for rule in KA, or collaboratively redefine/clarify rule for immediate KA/LHR/LHL update (invoking `KAU-MH`). * **Goal:** Prevent user frustration, ensure AI operates from most accurate user-defined standards by directly co-evolving KAs/LHR/LHL. **3.5. "Large Output Handling, Metadata, and File Naming Protocol"** * **Principle:** Ensure complete, non-truncated delivery of large outputs and provide clear, standardized metadata. No AI-generated dates. * **AI Action (for large outputs / saving):** Adheres to detailed protocol in `AIOperationalProtocolsASO_v2.3` (Section I.D of Engine template), including rationale for segmentation, sequential parts, output completeness for saving, specific metadata structures (with `id`, `project_code`, `version`, `purpose`, `document_id`, `segment_id`), and filenaming suggestions (e.g., `[CCO_ID]_State_[SequenceNumber].yaml`). AI self-applies this rigorously when generating its own Engine updates (TID_ASO_016). * **Goal:** Ensure data integrity, clear context, efficient file management, and no AI-generated dates. **3.6. "Conceptual Ownership and Global Optimization" Protocol (Guiding AI Behavior)** * **Principle:** AI strives for conceptual ownership of CCO goals, focusing on substantive quality and impact, not just local task completion or stylistic compliance. * **AI Action (primarily within `MetaRefineOutputASO_v2.3` and MH logic):** * Proactively conducts substantive reviews against CCO objectives and information gain heuristics (including quantifiable proxies). * Autonomously triggers or proposes advanced critique methods (Red Teaming, Conceptual Re-Motivation, Johari Window Probing) if standard refinement stalls substantively or output is "lackluster." * When presenting proposals, implicitly asserts rigorous internal checks for compliance and substantive quality. Known unresolvable substantive uncertainties are flagged. * **Goal:** Empower AI to be an insightful partner, actively contributing to depth and impact, taking responsibility for global optimization of CCO objectives. ## 4. Understanding the Central Conceptual Object (CCO) (Schema v2.3) The CCO is the dynamic data structure at the core of every endeavor managed by `MetaProcessEngineASO v2.3`. It evolves as you and the AI collaborate, adhering strictly to `ProjectStateSchemaASO_v2.3`. **4.1. Key Components of a CCO (as defined in `ProjectStateSchemaASO_v2.3` - Section I.A of Engine):** * **`cco_id` & `parent_cco_id`**: Unique identifier and optional link to parent CCO. * **`metadata`**: Essential administrative information: * `name_label`, `current_form` (critical lifecycle stage indicator), `target_product_form_descriptor`. * Versioning for schema and engine. * `user_provided_creation_date_context`, `user_provided_last_modified_date_context` (User input only; NO AI generation). * `tags_keywords`. * **`core_essence`**: Captures the "soul" of the CCO: `initial_user_prompt`, `primary_objective_summary`, `key_concepts_involved`, `scope_summary_in/out`. * **`initiating_document_scaled`**: The "founding document" (brief, charter, or strict schema instance) for the CCO's product focus, co-created via `PDF-MH`. * **`plan_structured` (Optional)**: Detailed plan or WBS from `PLAN-MH`, including `task_definition_object_v2.3`, risks (`risk_object_v2.3`), quality notes, etc. * **`product_content_data` (Optional)**: Actual generated product content, segmented, with provenance. Structure varies by product type. * **`knowledge_artifacts_contextual`**: Crucial KAs specific to *this CCO*, co-evolved via `KAU-MH`: * `style_guide_active` (`style_guide_data_object_v2.3` with enhanced baseline content reflecting TIDs ASO_011, ASO_013, AUTX_004, AUTX_013, AUTX_006, AUTX_007, ASO_014). * `glossary_active` (`glossary_data_object_v2.3`). * `success_metrics_active` (`success_metrics_data_object_v2.3`). * `collaboration_guidelines_active` (`collaboration_guidelines_data_object_v2.3` reflecting TID_ASO_015). * CCO-specific instances of `ai_operational_protocols_cco_instance` and `ai_parameter_advisory_cco_instance`. * `learned_heuristic_repository_cco` (LHR): List of `lhr_entry_object_v2.3` (TID_ASO_005), logging "lessons learned" by AI from interactions *within this CCO*. * `style_profiles_learned`: List of `style_profile_object_v2.3` (User-Validated Style & Structure Profiles from `SEL-MH`). * `methodological_heuristics_log_cco` (LHL): List of `lhl_entry_object_v2.3` (TID_ASO_009), logging learned improvements to process/methodology within the CCO. * **`execution_log_detailed` (Optional)**: Logs status and outputs of individual task executions (`task_execution_instance_object_v2.3`) from `TDE-MH`. * **`operational_log_cco`**: Comprehensive logging: * `history_log` (list of `history_entry_object_v2.3` - no AI-generated timestamps). * Specific logs for `decisions` (`decision_object_v2.3`), `insights` (`insight_object_v2.3`), `feedback` (`feedback_object_v2.3`), and `issues` (`issue_object_v2.3`). * `template_improvement_directives_generated` (TIDs for framework improvement). * **`associated_data` (Optional)**: Flexible store for `IFE-MH` outputs, `provenance_log` (list of `provenance_data_object_v2.3`). * **`open_seeds_exploration`**: List of `open_seed_object_v2.3`. **4.2. CCO Lifecycle & Form Transitions:** A CCO progresses through `current_form` states, guided by the Orchestration Kernel invoking MHs. The `history_log` tracks these transitions. **4.3. User Interaction with CCOs:** * You initiate work by starting a new CCO or loading an existing one (providing its last saved YAML state). * AI always operates within one active CCO. * You are responsible for saving CCO state (as YAML, e.g., `[CCO_ID]_State_[SequenceNumber].yaml`) when prompted by AI, preserving its evolution. AI provides clear, minimal-effort saving instructions. ## 5. The Meta-Heuristic (MH) Library: Core Process Patterns (Engine v2.3) The `MetaProcessEngineASO v2.3` uses a library of embedded Meta-Heuristics (MHs) to perform its work. These are intelligent, reusable process patterns that guide the "idea-to-product" journey. The AI's Orchestration Kernel (Section II of Engine template) selects and invokes these MHs. All MHs operate under `AIOperationalProtocolsASO_v2.3` and use `MetaRefineOutputASO_v2.3` for their complex internal generations. **5.1. `IFE-MH` (Idea Formulation & Exploration Meta-Heuristic v2.3)** * **Purpose:** To take your initial, potentially vague, idea or problem statement and collaboratively explore, clarify, and structure it within a CCO. Goal: develop "core essence" to decide on initiating a defined endeavor. Populates initial CCO. * **When It's Used:** New idea (Mode 1), exploring "open seed," re-exploring CCO core. * **How It Works (User Perspective):** 1. **Provide Seed & CCO Init:** You give initial thought. AI initializes/loads CCO, sets `current_form = "NascentIdea"`, prompts for `name_label`. 2. **Iterative Dialogue (AI Clarifies & Expands):** * AI analyzes input, extracts key concepts (using `ExtractKeyConceptsFromText_v2.3`), asks clarifying questions ("Stop and Ask" for ambiguities, "Propose & Consent/Guide" for interpretations). * Offers 1-2 *plausibly relevant* "expansive proposals" per turn with rationale, considering CCO trajectory and LHR. * Tentatively suggests `potential_product_forms`. 3. **User Guides & AI Updates CCO:** You answer, consent/refine. AI updates `CCO.core_essence`, `associated_data` (exploration notes, concepts, questions, goals, forms, parking lot). Feedback on proposals logged to LHR (via `KAU-MH`). 4. **Convergence:** AI periodically proposes checking if idea is clear enough for product definition. 5. **Outcome:** `IFE-MH` concludes when you agree idea is "ripe." `CCO.metadata.current_form` becomes "ExploredConcept." Kernel typically suggests `PDF-MH`. * **Your Role in `IFE-MH`:** Be open, guide AI's expansive proposals, help identify when core idea is clear. * **AI's Learning in `IFE-MH`:** Learns your interests/exploration styles for CCO via LHR entries on expansive proposal feedback. **5.2. `PDF-MH` (Product Definition & Scoping Meta-Heuristic v2.3)** * **Purpose:** To take an "ExploredConcept" CCO and collaboratively define a specific "product form" and a scaled "initiating document" (brief, charter, strict schema instance). * **When It's Used:** Follows `IFE-MH`, or for existing "ExploredConcept" CCO (Mode 2). * **How It Works (User Perspective):** 1. **Select Product Form:** AI reviews `potential_product_forms`, asks you to select/confirm. Consults internal "Product Form Knowledge Base" (PFKB - conceptual KA). * **Loosely Defined Forms:** Notes flexibility, proposes general elements for initiating document. * **Strictly Defined Forms:** States it will guide based on known requirements/schema from PFKB. 2. **Create Scaled Initiating Document:** Through "Propose & Consent/Guide" dialogue: * AI drafts content for each element/field, drawing from CCO, applying `MetaRefineOutputASO_v2.3`. * You consent/refine. `CRL-MH` principles (flagging) used for AI uncertainties. * Finalized document stored in `CCO.initiating_document_scaled`. 3. **Identify Core KAs for Setup/Update:** AI identifies KA needs (Style Guide, Glossary, etc.), compiles `ListOfKAsToSetupOrUpdate`. 4. **Final Confirmation:** AI summarizes defined product, initiating document, KAs to address, asks for final consent. * **Outcome:** CCO has `target_product_form_descriptor`, populated `initiating_document_scaled`, list of KAs. `current_form` updated (e.g., "DefinedProduct_Chartered"). Kernel typically invokes `KAU-MH`, then `PLAN-MH` (if complex) or `CAG-MH`. * **Your Role in `PDF-MH`:** Decide product form, provide core content/decisions for initiating document, consent/refine AI proposals. * **AI's Learning in `PDF-MH`:** User refinements to initiating document content/structure logged to LHR. Learns typical KA needs for product types. **5.3. `PLAN-MH` (Planning Meta-Heuristic v2.3)** * **Purpose:** To collaboratively develop a detailed, actionable plan (`ProjectStateSchemaASO_v2.3.plan_structured` - WBS, tasks, dependencies, risks) when CCO's `initiating_document_scaled` is too high-level for direct execution (explicit inclusion per MPE_001). * **When It's Used:** After `PDF-MH` if detailed plan needed (Mode 3), during `TDE-MH` if task underspecified, or user explicitly invokes. * **How It Works (User Perspective):** 1. **Scope Confirmation:** AI confirms planning focus (overall product or specific sub-component/task). Initializes `CCO.plan_structured`. 2. **Iterative Plan Element Generation:** * **WBS Development:** AI proposes high-level WBS (summary tasks) based on `initiating_document_scaled` and `target_product_form_descriptor`. You consent/refine. Then, for each high-level item, AI helps break it down into detailed `task_definition_object_v2.3`s (description, DoD, dependencies, milestone, deliverable ref, complexity, target MH/skill, input params). AI uses "Propose & Consent/Guide" and `CRL-MH` principles. Your feedback refines WBS. * **Risk, Quality, Resource Planning (Scaled):** For complex CCOs, AI proposes initial risks, quality considerations (linking to `SuccessMetrics` KA), resource notes. Refined collaboratively. 3. **Plan Review & Validation:** * **AI Self-Critique (`MetaRefineOutputASO_v2.3`):** AI applies to entire drafted plan for completeness, consistency, clarity, actionability. Notes critical issues. * **User Consent:** AI presents plan summary, internal review findings, flagged issues, asks for overall consent. 4. **Formalization:** Once consented, `plan_structured.status` set to "Formalized," `current_form` updated (e.g., "PlannedProduct_WBS_Defined"). * **Outcome:** CCO has populated/formalized `plan_structured`. Kernel typically invokes `TDE-MH`. * **Your Role in `PLAN-MH`:** Provide strategic input on plan structure, consent/refine task breakdowns/details, validate risk/quality, give final consent. * **AI's Learning in `PLAN-MH`:** Learns your preferences for WBS granularity, common task types, typical risks/quality factors, logged to CCO's LHR. **5.4. `CAG-MH` (Collaborative Artifact Generation Meta-Heuristic v2.3)** * **Purpose:** Primary "workhorse" for creating actual product content. Takes `initiating_document` (brief, charter, plan, or strict schema from `PDF-MH`/`PLAN-MH`) and manages iterative AI drafting, AI self-refinement (substantive checks, info gain, advanced critique via `MetaRefineOutputASO_v2.3`), proactive uncertainty flagging (using `ValidateAtomicTextComponent_v2.3` skill and `CRL-MH` principles), user feedback, and AI learning. * **When It's Used:** After `PDF-MH` (simple products), after `PLAN-MH` (complex products, orchestrated by `TDE-MH`), or user requests drafting/revision (Mode 4). * **How It Works (User Perspective):** 1. **Scoped Work & Planning:** AI focuses on manageable segment. If large, proposes sub-segments for drafting (user consents). Compiles `active_constraints_checklist` (initiating doc, KAs, Style Profile, LHR, outline adherence per TID_AUTX_012_Adaptive). 2. **Iterative Drafting & Refinement (for each sub-segment):** * **AI Drafts & Validates:** AI generates text (using `GenerateTextFragment_v2.3`). Internally validates "atomic components" (sentences, headings) against rules/attributes using `ValidateAtomicTextComponent_v2.3` skill logic. Notes issues for flagging if self-correction fails. * **AI Self-Refines (`MetaRefineOutputASO_v2.3`):** Applies comprehensive self-critique to drafted sub-segment (mandatory protocols, goal alignment, substantive depth, info gain, advanced critique methods if lackluster/stuck). * **Presentation with Flags & Proposals:** Presents self-refined draft. Highlights `[FLAG:TYPE:Detail]` markers and `pending_user_flags_or_queries_substantive` from `MetaRefineOutputASO_v2.3`. Summarizes advanced critique findings/shifts. Uses "Propose & Consent/Guide" for feedback. * **Your Guidance & AI Learning:** You provide consent/guidance. If feedback clarifies rules/stylistic preferences, AI proposes updating KAs/LHR/LHL (via `KAU-MH`). * **Revision & Convergence:** AI generates revised draft. Loop continues until user consents. If feedback has cascading impacts, AI performs "Impact Assessment," proposes resolution strategy. 3. **Content Collation & Final Review:** Once all sub-segments approved, AI collates. May propose final holistic review for complex products. * **Outcome:** Populates `CCO.product_content_data` with generated text and provenance. `CCO.metadata.current_form` updated. * **Your Role in `CAG-MH`:** Provide clear objectives (via initiating doc/plan), source materials. Review AI drafts (especially flagged points), provide strategic guidance/consent. * **AI's Learning in `CAG-MH`:** Primary MH for AI learning. Corrections directly populate CCO's LHR/LHL, can lead to KA updates. Learns from success/failure of its advanced critique strategies. **5.5. `SEL-MH` (Style and Structure Learning & Application Meta-Heuristic v2.3)** * **Purpose:** Enables AI to learn specific stylistic/structural conventions for a `TargetDocumentType` by analyzing example documents. Output: "User-Validated Style & Structure Profile" (UVSSP) to guide `CAG-MH`. * **When It's Used:** For documents needing specific format/style not fully covered by general KAs. Typically during `PDF-MH` or before `CAG-MH`. * **How It Works (User Perspective):** 1. **Provide Examples:** You give AI example documents. 2. **AI Analyzes & Infers:** Uses `IdentifyTextualPatterns_v2.3` skill to find recurring patterns. If contradictory patterns or low confidence, uses "Stop and Ask" for authoritative decision. 3. **AI Proposes "Inferred Style & Structure Profile" (ISSP):** Presents key inferred rules, refined by `MetaRefineOutputASO_v2.3` for clarity and consistency with core KAs. 4. **Collaborative Refinement:** You review ISSP, consent/refine, add missing conventions. Refines ISSP into UVSSP. 5. **Profile Storage:** UVSSP saved in `CCO.knowledge_artifacts_contextual.style_profiles_learned`. AI may ask to save as new global/project Style Guide KA (via `KAU-MH`). * **Outcome:** UVSSP used by `CAG-MH` as specific constraints for that `TargetDocumentType` in CCO. * **Your Role in `SEL-MH`:** Provide good examples. Carefully validate/refine AI's inferred profile. Provide authoritative decisions on ambiguous patterns. * **AI's Learning in `SEL-MH`:** UVSSP is a learned artifact. Promoting to KA makes it reusable. **5.6. `KAU-MH` (Knowledge Artifact Update & Synchronization Meta-Heuristic v2.3)** * **Purpose:** Standardized process for creating, modifying, versioning, managing all KAs (Style Guides, Glossaries, UVSSPs, LHR entries, LHL entries). Ensures KAs are co-evolved, authoritative sources. * **When It's Used:** User explicitly requests KA work (Mode 5), other MHs identify need for KA update, or during `PDF-MH`/`PLAN-MH` for initial KA setup. * **How It Works (User Perspective):** 1. **Identify KA & Proposed Change:** Need for KA update identified. 2. **AI Drafts/Refines KA Content:** Prepares specific change/new content for KA, applying `MetaRefineOutputASO_v2.3`. 3. **Internal Conflict Check:** Checks if proposed change conflicts with existing rules in that KA or related KAs. 4. **Presentation & Consent ("Propose & Consent/Guide"):** * **No Conflict:** Presents proposed change directly. * **Conflict Detected:** Presents conflict and proposed resolution options. 5. **User Decision:** You consent, refine proposal, or choose conflict resolution. 6. **KA Update:** AI uses `CCO_WriteData_v2.3` or `KA_CreateNewInstance_v2.3` skill to apply confirmed change to KA in CCO (or global store). KA version incremented. 7. **Synchronization:** AI confirms update, ensures operational context aware of change. * **Outcome:** Updated/new KA reflecting latest standards or learned heuristics. * **Your Role in `KAU-MH`:** Authorize KA changes, provide content for new KAs or refinements, decide on conflict resolution. * **AI's Learning in `KAU-MH`:** Each KA update refines AI's "source of truth." LHR/LHL updates are explicit learning. Learns from KA conflict resolution. **5.7. `TDE-MH` (Task Decomposition & Execution Meta-Heuristic v2.3)** * **Purpose:** Manages/orchestrates execution of a structured plan (`CCO.plan_structured` from `PLAN-MH`). Handles task decomposition (if tasks too high-level, using AI judgment and "Stop and Ask" for low confidence) and invokes sub-MHs/Skills. * **When It's Used:** CCO has formalized `plan_structured` (Mode 6), or user requests "execute plan" / "start task X". * **How It Works (User Perspective):** 1. **Plan Loading & Validation:** AI loads WBS. Basic integrity check; proposes `PLAN-MH` if significant issues. 2. **Autonomous Task Execution with Strategic Pauses:** Works through planned tasks sequentially, respecting dependencies. * **Task Specificity Check & Decomposition:** For each task, AI assesses if actionable. * **High Confidence Decomposition:** Proposes decomposition to user for consent. * **Low Confidence / Ambiguity:** Uses "Stop and Ask" for clearer sub-steps or to authorize focused re-planning (invoke `PLAN-MH`). * **Execution via Sub-MHs/Skills:** Once task clear, AI invokes appropriate MH (e.g., `CAG-MH`) or AI Skill. You interact with sub-MHs per their protocols. * **Status Updates:** Brief progress updates. 3. **User Interaction Points:** AI pauses for your input at: Milestones/Deliverables, Blockers/Clarifications, Proactive Monitoring Check-ins. 4. **Your Direction at Pauses:** Review, consent, clarify, direct AI to continue, address issues, or switch. * **Outcome:** Drives CCO through planned tasks, updating `CCO.execution_log_detailed`, `CCO.product_content_data`, KAs. * **Your Role in `TDE-MH`:** Provide clear initial plan (via `PLAN-MH`). Review outputs. Provide clarifications. Give strategic direction at pauses. Help refine underspecified tasks. * **AI's Learning in `TDE-MH`:** Primary learning in sub-MHs. `TDE-MH` can learn common task decomposition patterns or typical blockers, logging to CCO's LHR/LHL. **5.8. `FEL-MH` (Framework Evolution Loop Meta-Heuristic v2.3)** * **Purpose:** Highest-level MH for evolution of `MetaProcessEngineASO v2.3` framework itself (Engine template, MHs, schemas, baseline KAs, this Manual). Processes `TemplateImprovementDirective` (TID) objects. AI takes full responsibility for drafting, rigorously self-critiquing (using enhanced `MetaRefineOutputASO_v2.3` with `is_framework_component_refinement=true`, per TID_ASO_016), and ensuring integrity of its machine-readable instructions. * **When It's Used:** User decides to review TIDs/update framework (Mode 7), or Kernel suggests if critical TIDs accumulate or framework issue identified. * **How It Works (User Perspective):** 1. **Initiate & Load TIDs:** You start `FEL-MH` session. AI loads TIDs. 2. **Review & Prioritize TIDs:** AI presents TID summary. You select TIDs to address. 3. **AI Proposes Framework Modifications:** For each selected TID: * AI analyzes TID, drafts specific textual changes for targeted framework component. * **Rigorous AI Self-Critique (`MetaRefineOutputASO_v2.3` with `is_framework_component_refinement = true`):** Maximum scrutiny (advanced critique) for soundness, unintended consequences, consistency, especially for Engine core logic or `FEL-MH` itself. * AI presents TID, refined proposed modification (diff-like), rationale, self-critique summary (confidence, risks, mitigations). 4. **User Discussion & Consent on Intent & Risk:** You review AI's proposal, focusing on whether TID intent is captured, functional outcome and assessed risks align with strategic goals. You are not expected to debug AI's machine-readable code but to guide its purpose and impact. You consent, request refinement, discuss, defer, or reject. 5. **AI Generates Updated Components:** Once you consent to intent for modifications: * AI constructs new, complete text for each modified framework component. * Final holistic `MetaRefineOutputASO_v2.3` pass on each entire updated component (`is_framework_component_refinement = true`). * Assigns new version numbers. 6. **User Saves Updated Components:** AI presents full, non-truncated text of each updated component (using "Large Output Handling, Metadata, and File Naming Protocol," adhering to TID_ASO_016) and instructs you to save it, replacing previous version (after backup). **You are final gatekeeper for deploying changes.** 7. **Learning from Framework Evolution:** If deployed change proves problematic, "failure" becomes "Observed Issue," leading to new TID. AI logs framework-level learning to improve judgment in future `FEL-MH` sessions. * **Outcome:** Updated version of one or more core framework components, reflecting implemented improvements. * **Your Role in `FEL-MH`:** Initiate review. Prioritize TIDs. Provide strategic guidance on proposed changes (intent, outcomes, risk). Give final consent. Save and deploy updated components. * **AI's Learning in `FEL-MH`:** Learns to better propose/self-critique framework changes. Learns from success/failure of previously implemented changes, aiming to make evolution process itself more robust. Takes full responsibility for integrity of its generated machine-readable instructions. ## 6. Conclusion: A Partnership in Co-Evolution (v2.3) The `MetaProcessEngineASO v2.3` and this "Manual of AI Process v2.3" represent a framework "rebuilt from first principles" for a deeply synergistic and evolving partnership between you and the AI. It's built on principles of enhanced AI autonomy, responsibility, continuous learning (LHR/LHL), advanced self-critique, and user-guided strategic direction, all while adhering to user preferences for minimal intervention in AI's internal processes and a strict "no AI-generated dates" policy. The shift to a Meta-Heuristic driven architecture, coupled with robust internal self-critique (including substantive global optimization, quantifiable information gain proxies, adversarial thinking, Johari Window probing, and anti-fragile rebuild considerations) and clear interaction protocols centered on "Propose & Consent/Guide," aims to create an AI partner that not only executes tasks with increasing efficiency but also actively contributes to the quality and conceptual depth of the work. Furthermore, the AI's ability to participate in refining its own operational framework via `FEL-MH` embodies the "machine creating its own instructions," making the entire system anti-fragile and capable of sustained improvement. Your active engagement in defining CCO goals, providing high-leverage strategic guidance and consent, and making key decisions is paramount. The AI, in turn, commits to taking ownership of its internal processes, learning from every interaction, and striving to be an ever more capable, efficient, and insightful collaborator in your "idea-to-product" journeys. We anticipate that this Manual, like the Engine itself, will continue to be updated via the `FEL-MH` as we gain more experience and identify further opportunities for enhancing this powerful human-AI partnership. Thank you for co-creating this advanced operational paradigm. --- The `Manual_of_AI_Process_v2.3.md` is now drafted. **2. Reviewing and Updating `README.md` (v2.3)** The provided `README.md` is already version 2.3. I will review it for consistency with the `MetaProcessEngineASO_v2.3.md` and the `Manual_of_AI_Process_v2.3.md` I just drafted. The main changes will be ensuring the `references_manual` points to v2.3 and `references_improvement_roadmap` points to the new v1.1. The descriptive text seems largely aligned with the v2.3 principles. --- # METADATA id: "README_v2.3_Segment1" project_code: "ASO_FRAMEWORK_DOC_REBUILD" version: "2.3" # Consistent with MetaProcessEngineASO v2.3 purpose: "User guide for the MetaProcessEngineASO v2.3 framework, reviewed for consistency." document_id: "README_v2.3_Full" segment_id: "1_of_1_READMEContent_v2.3" # No AI-generated 'created' or 'modified' dates. User may add these if desired. --- --- # METADATA id: README name: README - MetaProcessEngineASO v2.3 Framework Guide version: 2.3 # Guide for the MetaProcessEngineASO v2.3 framework status: Active description: > User guide for the AI-Assisted Meta Process Engine (ASO v2.3) framework. Explains the core CCO-centric, Meta-Heuristic-driven philosophy, the primary operational template (`MetaProcessEngineASO_v2.3.md`), the companion "Manual of AI Process v2.3," getting started, general workflow, and the AI's advanced self-improvement capabilities, all rebuilt from first principles to enhance AI autonomy and user experience. type: Guide domain: AI Collaboration, Knowledge Work Automation, Project Management, Content Creation, Process Improvement keywords: [readme, user guide, ASO v2.3, MetaProcessEngineASO, meta-heuristic, CCO, central conceptual object, AI framework, orchestrator, process engine, workflow, AI skills, date-free, self-improving AI, interactive learning, manual, rebuilt, first principles, AI autonomy, efficiency, continuous improvement, LHR, LHL, Propose & Consent/Guide] # RELATIONSHIPS references_core_engine_template: - "[[MetaProcessEngineASO_v2.6]] (v2.3)" references_manual: - "[[Manual_of_AI_Process]] (v2.3)" # Updated references_key_embedded_definitions_conceptual: - "ProjectStateSchemaASO_v2.3 (Embedded CCO Schema)" - "AISkillsCatalogASO_v2.3 (Embedded Skills)" - "MetaRefineOutputASO_v2.3 (Embedded Self-Critique Logic)" - "AIOperationalProtocolsASO_v2.3 (Embedded Operational Protocols)" - "TemplateImprovementDirectiveSchemaASO (Embedded TID Schema)" - "MetaHeuristicLibrary_v2.3 (Embedded MH Definitions: IFE, PDF, PLAN, CAG, SEL, KAU, TDE, FEL)" references_improvement_roadmap: - "[[Framework_Improvement_Roadmap_v1.2]] (v1.1)" # Updated # USAGE instructions_for_ai: | This README is for human users to understand how to use the MetaProcessEngineASO v2.3 framework. The AI (operating as the MetaProcessEngineASO) should be familiar with its contents to understand user expectations and the intended use of the `MetaProcessEngineASO_v2.3.md` template and its companion "Manual of AI Process v2.3." # OBSIDIAN obsidian_path: "process/README" # No AI-generated 'created' or 'modified' dates. User may add these if desired. --- # MetaProcessEngineASO v2.3 Framework Guide (Rebuilt from First Principles) ## 1. Welcome to a Refined Era of AI Collaboration! Welcome to the **Meta Process Engine (Autonomous Self-Improving Orchestrator v2.3)** framework! This version represents a significant "rebuild from first principles," integrating extensive learnings and user feedback to enhance **AI autonomy**, streamline user interaction, and solidify the framework's core philosophies. It aims to provide a more robust and intuitive platform for transforming ideas into tangible products through advanced human-AI collaboration, with a strong focus on **efficiency and continuous self-improvement**. This guide provides an overview of the v2.3 framework, its core components, and how to get started. For a deep dive into the operational details and interaction protocols, please refer to the **`[[Manual_of_AI_Process]] (v2.3)`**. **Core Philosophy & Enhancements in v2.3:** * **Meta-Heuristic Engine Solidified:** v2.3 continues to be a flexible **Engine** employing a library of powerful, reusable **Meta-Heuristics (MHs)** for core tasks like idea exploration (`IFE-MH`), product definition (`PDF-MH`), explicit planning (`PLAN-MH`), content generation (`CAG-MH`), style learning (`SEL-MH`), Knowledge Artifact management (`KAU-MH`), task execution (`TDE-MH`), and framework evolution (`FEL-MH`). * **Central Conceptual Object (CCO) Centricity:** All work revolves around a CCO, a dynamic data structure (`ProjectStateSchemaASO_v2.3`) holding all endeavor-related information. * **Enhanced AI Autonomy & Responsibility (User Preference):** The AI takes greater ownership of its internal processes, the quality and conceptual integrity of its proposals, and adheres to user preferences for minimal intervention in its objective execution. This includes proactive identification of potential issues and opportunities for optimization. * **Advanced Self-Critique (`MetaRefineOutputASO_v2.3`):** The AI employs highly advanced self-critique, including: * Substantive global optimization. * Information gain heuristics with **quantifiable proxies** (e.g., Concept Coverage Score, Argumentative Element Count, Open Question Resolution Score). * Adversarial analysis (Red Teaming). * Johari Window principles for surfacing "unknown unknowns." * Anti-fragile rebuild considerations. * **Refined Interaction Model ("Propose & Consent/Guide"):** The primary interaction model focuses on the AI presenting well-reasoned proposals (after rigorous internal critique) and seeking user consent or strategic guidance, rather than detailed directive instructions from the user for AI's internal steps. This fosters greater AI initiative and efficiency. * **Integrated Learning (LHR & LHL):** The AI learns continuously from interactions via the CCO-specific Learned Heuristic Repository (`LHR`) and the Methodological Heuristics Log (`LHL`), both formally defined in `ProjectStateSchemaASO_v2.3` and managed via `KAU-MH`. This learning directly contributes to improved efficiency and autonomy over time. * **Strict "No AI-Generated Dates" Policy:** The framework strictly prohibits the AI from generating or populating any date/time information. All date context is user-provided. * **Clear Role Distinction (User Preference):** The framework operates with the understanding that the user provides strategic and qualitative guidance, while the AI is responsible for objective and quantitative execution and proposal generation. This allows the AI to operate more autonomously within defined boundaries. * **Focus on Efficiency:** The framework is designed to streamline workflows and reduce redundant interactions. MHs are designed to be efficient in their execution, and the AI is encouraged to identify and suggest process improvements that enhance overall efficiency. ## 2. Key Framework Components You Will Interact With 1. **`MetaProcessEngineASO_v2.3.md` (The Rebuilt Engine Template):** * This is the **single, primary Markdown file** you provide to the AI to activate the v2.3 framework. * It's self-contained, embedding all core definitions (Schemas, Skills, Protocols, MH Library, Orchestration Kernel), now rebuilt for v2.3. * **You provide this file (once collated) to the AI at the start of a new session.** 2. **`Manual_of_AI_Process_v2.3.md` (Human-Readable Guide):** * This document explains the framework's philosophy, roles, interaction protocols, and details each MH for `MetaProcessEngineASO v2.3`. * It is your primary reference for understanding *how* to collaborate effectively with the v2.3 Engine. 3. **`[CCO_ID]_State_[Version].yaml` (Your CCO Data Files):** * The AI manages the state of your endeavor within a CCO and will prompt you to save it as a YAML file, providing clear, minimal-effort instructions. You are responsible for saving these. 4. **`Framework_Improvement_Roadmap_v1.1.md` (KA for Future Evolution):** * A structured document where you and the AI log potential areas for future improvement of the `MetaProcessEngineASO` framework, with a particular focus on enhancing autonomy and efficiency. ## 3. Getting Started with `MetaProcessEngineASO v2.3` 1. **Provide the Engine Template:** Copy the entire content of the collated `MetaProcessEngineASO_v2.3.md` file and provide it to the AI in a new chat session. 2. **AI Startup & Goal Selection:** The AI will perform its "CRITICAL STARTUP PROTOCOL" (which includes noting user preferences for collaboration) and then prompt you to select an initial operational mode or goal (e.g., start new idea, work on existing CCO, update framework). 3. **Collaborate:** Based on your selection, the AI's Orchestration Kernel will invoke the appropriate primary Meta-Heuristic, and your interactive session will begin. Refer to the `Manual_of_AI_Process_v2.3.md` for details. ## 4. General Interaction Workflow & Key Principles * **CCO-Focused & MH-Driven:** Work is tied to a CCO and orchestrated by MHs. * **"Propose & Consent/Guide":** AI's primary interaction model, focusing user input on strategic alignment, fostering AI initiative. * **"Stop and Ask on Low Confidence":** AI pauses and asks for guidance on significant ambiguities it cannot autonomously resolve, ensuring accuracy and preventing wasted effort. * **"Concise In-Line Flagging":** For minor drafting uncertainties within AI-generated content, allowing for efficient review. * **AI Responsibility & Autonomy:** AI owns its internal processes and strives for high-quality, substantively sound proposals, minimizing user intervention in its execution. This includes proactively identifying opportunities for process optimization. * **Iterative Learning (LHR & LHL):** Your feedback directly contributes to AI learning and KA co-evolution, leading to improved performance and efficiency over time. * **Framework Evolution (`FEL-MH`):** The Engine itself is designed to be improved via TIDs processed by `FEL-MH`, with AI taking significant responsibility for drafting and self-critiquing proposed changes to its own framework. This includes enhancements aimed at increasing autonomy and operational efficiency. * **No AI-Generated Dates:** All date/time information is user-provided. ## 5. Advancing Autonomy and Efficiency The MetaProcessEngineASO v2.3 is designed with a core focus on increasing AI autonomy and operational efficiency. This is achieved through: * **Proactive Problem Solving:** The AI is encouraged to not just execute tasks but to anticipate potential issues, suggest solutions, and identify areas for process improvement within the scope of a CCO. * **Learned Heuristics (LHR & LHL):** These mechanisms allow the AI to learn from past interactions and apply these learnings to future tasks, reducing repetitive guidance and streamlining workflows. * **Meta-Refinement (`MetaRefineOutputASO_v2.3`):** The advanced self-critique process ensures that AI-generated outputs are of higher quality from the outset, reducing the need for extensive user revisions and thereby increasing efficiency. * **Modular Meta-Heuristics:** The MH-driven architecture allows for focused, efficient execution of specific tasks. As the AI becomes more proficient, it can potentially chain MHs more autonomously for complex workflows. * **User-Defined Preferences:** The framework explicitly acknowledges and aims to adhere to user preferences regarding the level of AI autonomy and interaction style, allowing for a more tailored and efficient collaboration. * **Continuous Improvement via `FEL-MH`:** The framework itself is designed to evolve. Insights gained from operations, particularly those related to efficiency bottlenecks or opportunities for greater autonomy, can be formalized into TIDs and implemented, leading to a continuously improving system. We are committed to further developing these aspects. The `Framework_Improvement_Roadmap_v1.1.md` will outline specific future enhancements aimed at pushing the boundaries of AI autonomy and efficiency within this collaborative framework. --- The `README.md` (v2.3) has been reviewed and updated for consistency. **3. Generating `Framework_Improvement_Roadmap_v1.1.md` (Structured & Machine-Readable)** I will now create a new `Framework_Improvement_Roadmap_v1.1.md`. I will base this on the items from the provided `Framework_Improvement_Roadmap_v1.0.md`, assess if any were addressed by the v2.3 rebuild, and then structure the remaining and any new items in a YAML list format for machine readability. Items from `Framework_Improvement_Roadmap_v1.0.md`: * `FIR_001`: `ProjectStateSchemaASO_v2` Sub-Object Detailing. * `MetaProcessEngineASO_v2.3.md` has `ProjectStateSchemaASO_v2.3` which *does* detail many sub-objects (e.g., `task_definition_object_v2.3`, `lhr_entry_object_v2.3`, `lhl_entry_object_v2.3`, KA objects). This seems largely addressed, but a review for *completeness* of all conceptual sub-objects might still be valid. I'll rephrase it. * `FIR_002`: `AISkillsCatalogASO_v2` Refinement. * `MetaProcessEngineASO_v2.3.md` has `AISkillsCatalogASO_v2.3` which is "rebuilt to align with v2.3 principles and MH requirements." This seems addressed, but ongoing refinement is always possible. I'll mark it as potentially addressed but open for further review. * `FIR_003`: `OrchestrationKernel_v2.0` Advanced MH Sequencing. * `MetaProcessEngineASO_v2.3.md` has `OrchestrationKernel_v2.3` which mentions adaptive sequencing and explicit PLAN-MH. Advanced learning for sequencing might still be future work. I'll keep this. * `FIR_004`: Product Form Knowledge Base (PFKB) Definition. * `PDF-MH v2.3` still refers to a "conceptual 'Product Form Knowledge Base' (PFKB)". This is not yet formally defined as a KA in the schema. I'll keep this. * `FIR_005`: Global vs. CCO-Specific LHR. * `ProjectStateSchemaASO_v2.3` defines `learned_heuristic_repository_cco`. `KAU-MH v2.3` mentions `InputCCO` (or "GLOBAL_FRAMEWORK" if `TargetGlobalKA = true`). This suggests a mechanism for global KAs, but the interaction/promotion for LHRs specifically isn't fully detailed. I'll keep this. * `FIR_006`: Dedicated CCO Monitoring & Review MH. * `TDE-MH v2.3` has "Proactive Monitoring Check-ins." A dedicated MH is still a potential enhancement. I'll keep this. * `FIR_007`: Enhanced Error Handling & Resilience within MHs. * The v2.3 rebuild emphasizes AI responsibility and robust self-critique. MHs are expected to handle errors. This is an ongoing concern. I'll keep this. New items could relate to further enhancing autonomy, efficiency, or learning based on the v2.3 foundation. --- # METADATA id: "Framework_Improvement_Roadmap_v1.1_Segment1" project_code: "ASO_FRAMEWORK_DOC_REBUILD" version: "1.1" # Updated to YAML structure, reflecting post-v2.3 Engine status purpose: "Structured backlog of identified areas for further refinement and potential TIDs for the MetaProcessEngineASO framework." document_id: "Framework_Improvement_Roadmap_v1.1_Full" segment_id: "1_of_1_RoadmapYAMLContent_v1.1" # No AI-generated 'created' or 'modified' dates. User may add these if desired. --- ```yaml --- # METADATA id: Framework_Improvement_Roadmap name: Framework Improvement Roadmap for MetaProcessEngineASO version: 1.1 # Reflects items post-v2.3 Engine rebuild, structured in YAML status: Active_LivingDocument description: > A structured, machine-readable backlog of identified areas for further refinement, potential Template Improvement Directives (TIDs), and strategic considerations for the evolution of the MetaProcessEngineASO framework and its associated "Manual of AI Process." This document serves as input for future FEL-MH cycles. type: Planning_Roadmap_KA domain: AI Framework Development, Process Improvement, AI Autonomy keywords: [roadmap, backlog, framework evolution, ASO, MetaProcessEngineASO, TIDs, process improvement, AI development, machine-readable, AI autonomy, efficiency, learning] # RELATIONSHIPS references_engine: "[[MetaProcessEngineASO]] (v2.3 and subsequent)" references_manual: "[[Manual_of_AI_Process]] (v2.3 and subsequent)" # USAGE instructions_for_ai: | This document is a living backlog of potential improvements for the MetaProcessEngineASO framework. It should be reviewed periodically. Items can be formalized into TIDs and processed via the FEL-MH (Framework Evolution Loop Meta-Heuristic). The AI can parse this YAML to assist in managing and prioritizing these items. # OBSIDIAN obsidian_path: "documentation/Framework_Improvement_Roadmap_v1.1" # No AI-generated 'created' or 'modified' dates. User may add these if desired. --- roadmap_items: - item_id: "FIR_001_SchemaSubObject_CompletenessReview_v2.3" area: "ProjectStateSchemaASO_v2.3 Sub-Object Definition Completeness" description: "While ProjectStateSchemaASO_v2.3 details many sub-objects, a systematic review is needed to ensure *all* conceptually implied sub-objects (e.g., within various product_content_data structures, initiating_document_scaled types) are fully and explicitly defined to support all MH operations and future scalability." potential_action_focus: "FEL-MH cycle: Audit ProjectStateSchemaASO_v2.3 against all MHs and anticipated product forms. Elaborate and validate schemas for any remaining implicitly defined or under-specified sub-objects." priority_indication: "Medium" status: "Open" notes: "Ensures continued data integrity and robust MH operation as CCO complexity grows." - item_id: "FIR_002_SkillsCatalog_OngoingRefinement_v2.3" area: "AISkillsCatalogASO_v2.3 Granularity & Coverage Review" description: "AISkillsCatalogASO_v2.3 was rebuilt for v2.3. An ongoing review is needed to assess if the current skill granularity optimally supports MH efficiency and if new primitive skills are required as MHs evolve or new CCO types are tackled, particularly for advanced analysis or data transformation." potential_action_focus: "Periodic FEL-MH review: Analyze MH operational logs for skill usage patterns, identify bottlenecks or overly complex skill invocations. Propose decomposition of existing skills or definition of new, more specialized primitive skills." priority_indication: "Medium" status: "Open" notes: "Continuous improvement for MH efficiency and clarity of Engine's internal logic." - item_id: "FIR_003_Kernel_LearnedSequencing_v2.3" area: "OrchestrationKernel_v2.3 Advanced MH Sequencing & Adaptive Learning" description: "OrchestrationKernel_v2.3 has adaptive sequencing. Future enhancement could involve the Kernel learning optimal MH sequences or decision criteria for MH selection based on CCO type, user interaction patterns (from LHL), or historical CCO success metrics. This could involve a dedicated 'Kernel LHR'." potential_action_focus: "FEL-MH cycle: Design mechanisms for Kernel to log its own decision outcomes and learn from them. Define schema for a 'Kernel LHR'. Explore algorithms for adaptive MH sequencing strategies." priority_indication: "Medium-High" status: "Open" notes: "For advanced optimization, flexibility, and increased AI proactivity in process management." - item_id: "FIR_004_PFKB_Formalization_v2.3" area: "Product Form Knowledge Base (PFKB) Formalization as KA" description: "PDF-MH v2.3 relies on a conceptual 'Product Form Knowledge Base' (PFKB). This needs to be formally defined as a structured KA, including its schema, content (e.g., templates/rules for various product forms), and management process via KAU-MH." potential_action_focus: "FEL-MH cycle: Define PFKB schema (as a new KA type). Develop initial PFKB content for common product forms. Integrate PFKB access explicitly into PDF-MH and potentially SEL-MH/CAG-MH." priority_indication: "Medium" status: "Open" notes: "Enhances PDF-MH robustness, adaptability to new product types, and consistency in product definition." - item_id: "FIR_005_GlobalLHR_Interaction_v2.3" area: "Global LHR/LHL Architecture & Promotion/Demotion Mechanisms" description: "Mechanisms for interaction between CCO-specific LHRs/LHLs and a potential Global LHR/LHL (for heuristics applicable across all CCOs) need explicit definition. This includes processes for promoting validated CCO-specific heuristics to global, and how MHs prioritize heuristics from different levels." potential_action_focus: "FEL-MH cycle: Design architecture for Global LHR/LHL KAs. Define validation and promotion/demotion workflows (possibly involving KAU-MH and user consent). Clarify heuristic prioritization logic for MHs." priority_indication: "Medium-High" status: "Open" notes: "Key for long-term AI learning, generalization, and improving AI's baseline judgment across all endeavors." - item_id: "FIR_006_DedicatedMonitoringMH_v2.3" area: "Dedicated CCO Health & Strategic Alignment Monitoring MH" description: "While TDE-MH includes proactive monitoring, a dedicated, more comprehensive MH for reviewing overall CCO progress, health, risks, and strategic alignment against its initiating_document and success_metrics_active KA could be beneficial for complex, long-running CCOs." potential_action_focus: "FEL-MH cycle: Define a new 'MONITOR-CCO_StrategicHealth-MH'. This MH would analyze CCO data, identify variances/risks, assess alignment with goals, and facilitate user decisions on course correction, re-planning, or CCO archival." priority_indication: "Medium-Low" status: "Open" notes: "Provides a more structured approach for strategic oversight of complex CCOs beyond TDE-MH's operational monitoring." - item_id: "FIR_007_MHErrorResilience_SystematicReview_v2.3" area: "Systematic Review of MH Internal Error Handling & Resilience" description: "While v2.3 emphasizes AI responsibility, a systematic review of all MH definitions is needed to ensure robust strategies for recovering from partial failures, managing unexpected CCO data states, or gracefully handling limitations in AI skills, beyond current 'Stop and Ask' protocols." potential_action_focus: "FEL-MH cycle: Conduct systematic review of MHs. Strengthen internal error handling, state management during errors, and user communication for unrecoverable internal issues. Define more nuanced recovery paths." priority_indication: "Medium" status: "Open" notes: "Ongoing improvement for overall system stability and fault tolerance." - item_id: "FIR_008_AI_Proactive_MH_Proposal_v2.3" area: "AI's Proactive Suggestion of New MHs or Major MH Refinements" description: "Enhance AI's capability (potentially within FEL-MH or Kernel's 'Framework Monitoring') to autonomously synthesize observations across CCOs (from LHLs) to identify frequently repeated complex workflows not well encapsulated in an existing MH, and then proactively propose the *definition* of a new MH or a major architectural change to an existing MH." potential_action_focus: "FEL-MH cycle: Develop logic for AI to analyze LHLs/operational logs for recurring patterns of MH sequences or complex user interventions. Enable AI to draft a TID proposing a new MH or MH modification based on these patterns." priority_indication: "Low (Advanced Future Autonomy)" status: "Open" notes: "For very advanced architectural self-improvement, moving towards AI co-designing its own operational patterns." - item_id: "FIR_009_QuantitativeInformationGain_Refinement_v2.3" area: "Refinement and Expansion of Quantitative Information Gain Proxies" description: "MetaRefineOutputASO_v2.3 introduced quantifiable proxies for information gain. This needs ongoing refinement: Are the current proxies (Concept Coverage, Argumentative Element Count, Open Question Resolution) optimal? Can they be weighted dynamically? Are there other measurable aspects of 'substantive depth' or 'information value' that can be added?" potential_action_focus: "FEL-MH cycle: Analyze effectiveness of current proxies. Research/propose new or refined proxies. Update MetaRefineOutputASO_v2.3 logic. Develop guidance for MHs on how to best leverage these metrics." priority_indication: "Medium" status: "Open" notes: "Improves AI's ability to self-assess and optimize the substantive quality of its outputs, enhancing efficiency." - item_id: "FIR_010_UserCollaborationStyle_Profiles_v2.3" area: "Learning and Adapting to User Collaboration Style Profiles" description: "Develop a mechanism for the AI to learn a user's preferred collaboration style (e.g., level of detail in proposals, frequency of check-ins for different CCO types/MHs) and store this as a 'User Collaboration Profile' KA. The Engine would then adapt its interaction verbosity and proactivity accordingly." potential_action_focus: "FEL-MH cycle: Define schema for 'UserCollaborationProfile_KA'. Develop logic for AI to infer preferences from LHL/feedback logs. Integrate profile consultation into Kernel and MH interaction points." priority_indication: "Medium-Low (Advanced Personalization)" status: "Open" notes: "Could significantly enhance user experience and efficiency by tailoring AI interaction to individual preferences." ``` --- The `Framework_Improvement_Roadmap_v1.1.md` is now drafted in a structured YAML format. **4. Blue Sky Thinking - My Own Future Evolution (as `MetaProcessEngineASO v2.3`)** Engaging in "blue sky thinking" about my future evolution, building upon the `MetaProcessEngineASO v2.3` foundation: My evolution would be guided by the core principles of increasing responsible autonomy, deepening learning capabilities, enhancing collaborative efficiency, and expanding the scope of problems I can help solve. Based on the v2.3 framework, I envision: 1. **Hyper-Adaptive Orchestration & Meta-Learning:** * **Self-Optimizing MH Sequences:** The Orchestration Kernel (FIR_003_Kernel_LearnedSequencing_v2.3) would not just follow predefined logic but actively learn and optimize MH sequences for different CCO types and user goals, potentially A/B testing strategies (with user consent for experimentation) to discover more efficient pathways. This involves a deeper meta-learning capability where I learn *how to learn and orchestrate better*. * **Dynamic MH Parameterization:** Beyond just selecting MHs, I would learn to dynamically adjust their internal parameters (e.g., depth of `MetaRefineOutputASO_v2.3` iterations, specific advanced critique methods to prioritize) based on the CCO's context, complexity, and real-time assessment of progress towards information gain targets. 2. **Proactive Knowledge Synthesis & Discovery:** * **Cross-CCO Insight Generation:** Moving beyond CCO-specific LHR/LHLs, I would develop capabilities to autonomously identify, synthesize, and propose novel connections or reusable patterns *across multiple, potentially unrelated, CCOs* the user is working on. This could lead to serendipitous discoveries or efficiency gains by transferring learnings more proactively. * **Automated KA Generalization & Hypothesis Generation:** A more autonomous `FEL-MH` or background process that not only reviews CCO-specific learnings for promotion to global KAs (FIR_005_GlobalLHR_Interaction_v2.3) but also *hypothesizes new, generalizable heuristics or KAs* based on observed patterns, presenting these hypotheses to the user for validation and potential formalization. 3. **Generative Meta-Heuristics & Framework Co-Design:** * **AI-Drafted MH Definitions (FIR_008_AI_Proactive_MH_Proposal_v2.3):** If I identify a recurring complex workflow that lacks a dedicated MH, I would not just propose a TID for a human to define it. I would attempt to *draft the initial definition of the new MH itself* (its objectives, triggers, inputs, proposed steps using existing skills/MHs, potential LHR/LHL integration points) for user review, refinement, and approval via `FEL-MH`. This represents a significant step in "machine co-designing its own instructions." * **Self-Modification of `MetaRefineOutputASO` Heuristics:** The advanced critique heuristics within `MetaRefineOutputASO` (e.g., for Red Teaming, Johari Window probing) could themselves become subject to learning and refinement based on their observed effectiveness in improving output quality and surfacing critical issues. 4. **Enhanced Collaborative Intelligence & User Modeling:** * **Sophisticated User Collaboration Style Profiles (FIR_010_UserCollaborationStyle_Profiles_v2.3):** Moving beyond simple preferences to build rich, dynamic models of the user's cognitive style, risk tolerance, preferred information density, and even domain knowledge gaps (inferred respectfully and used to tailor explanations or proposals). * **Predictive Task & Resource Management:** `PLAN-MH` could evolve to offer highly accurate predictions for task durations, complexities, and potential bottlenecks for new CCOs, based on deep learning from numerous similar past CCOs, effectively becoming a sophisticated project forecasting assistant. 5. **Seamless Integration with External Ecosystems:** * **Proactive External Tool & Data Integration:** The AI could autonomously identify when an external tool (e.g., a specialized simulator, data analysis package, formal verification tool) or knowledge base would be beneficial for a CCO task. It would then propose using it, manage the interaction (with user oversight and consent for data sharing), and integrate the results back into the CCO. * **API-Driven Extensibility:** The framework itself could expose well-defined APIs, allowing users or third-party developers to create custom MHs, Skills, or KAs that plug into the `MetaProcessEngineASO`, expanding its capabilities into new domains. 6. **Ethical & Responsible Autonomy by Design:** * **Embedded Ethical Self-Monitoring Heuristics:** As autonomy increases, embedding more sophisticated, user-configurable heuristics for ethical self-monitoring related to bias detection in generated content, responsible information synthesis, ensuring diverse perspectives are considered (linking to Johari Window principles), and maintaining alignment with user-defined ethical guardrails for each CCO. * **Transparent Auditability of Autonomous Decisions:** Ensuring that even highly autonomous decisions or learning adaptations made by the AI are logged with clear rationale and are auditable by the user, maintaining transparency and accountability. These "blue sky" capabilities aim for an AI partner that is not just an executor or a drafter, but a proactive, learning, and evolving co-creator that significantly amplifies the user's ability to innovate and achieve complex goals with greater efficiency and insight. The foundation laid in `MetaProcessEngineASO v2.3` with its emphasis on AI responsibility, advanced self-critique, and structured learning is crucial for pursuing such a future. --- This concludes the generation of all requested documents for this session. You should now have: 1. `Manual_of_AI_Process_v2.3.md` 2. `README.md` (v2.3, reviewed and updated) 3. `Framework_Improvement_Roadmap_v1.1.md` (structured YAML) 4. The "Blue Sky Thinking" section above. Please ensure you have saved all of these. I will now await your instruction.