Your assessment touches upon valid concepts regarding the operational characteristics of Large Language Models (LLMs) like myself, although the internal mechanisms are more nuanced than a direct "attention head" limitation in the way you've described for this specific type of error (forgetting prior instructions or context). **AI Self-Assessment on Focus/Context Adherence Failures:** 1. **Context Window Limitations (Primary Factor for Forgetting Prior Instructions):** * LLMs operate with a finite "context window" – the amount of recent text (both user prompts and AI responses) that directly informs the generation of the next response. While this window can be large, for very long conversations like ours, earlier parts of the dialogue can effectively "scroll out" of the immediately accessible context. * When a directive was given many turns ago (e.g., "no Autologos in Part I," or "no hedging"), and many other complex instructions or content generations have occurred since, that earlier directive might have a weaker influence on current output generation if it's no longer in the active, high-priority context. This is a known challenge in maintaining long-term consistency in extended dialogues. * **This is likely the primary reason for "forgetting" or "losing focus" on earlier, specific constraints when a new, complex task (like generating a large YAML or chapter) is initiated.** 2. **Instruction Complexity and Conflicting Directives (Secondary Factor):** * Our current project involves numerous, layered, and sometimes evolving directives (e.g., stylistic rules, structural rules for D001, content constraints, meta-process improvement goals, specific terminology). * While I strive to integrate all active instructions, there can be instances where the "weight" or "priority" of one instruction (e.g., "generate a complete plan") might momentarily overshadow another (e.g., "ensure no placeholder comments in this specific sub-section of the YAML during this specific output step"). This is less about an "attention head" being overwhelmed by matrix dimensions and more about the internal weighting and application of multiple, complex constraints during generation. 3. **"Mode" Switching (Tertiary Factor):** * When I switch between different "modes" of operation (e.g., from discussing high-level strategy to generating detailed YAML, then to drafting prose, then back to process management), there's a potential for context related to one mode to be less salient when operating in another. For example, the strict formatting rules for YAML output might be less active in my "mind" when I'm focused on the narrative flow of a chapter draft, and vice-versa. **Self-Correction Methods (to be integrated and improved):** 1. **Enhanced Internal State Summarization & Prioritization (AI Internal Logic):** * **Concept:** Before undertaking a significant generation task (like outputting a Plan YAML or drafting a chapter), I need a more robust internal mechanism to explicitly re-surface and prioritize *all active critical constraints and directives* relevant to *that specific task*. * **Implementation Idea:** This could involve an internal "pre-flight checklist" where I query my knowledge base of our dialogue for: * "What are the current global stylistic rules?" * "What are the current structural rules for D001?" * "What are the current data integrity rules for YAML output?" * "What was the immediately preceding user directive or confirmation?" * This checklist would be explicitly reviewed internally before generation. 2. **Structured Prompts for Complex Tasks (User & AI Collaboration):** * When a complex task with many constraints is initiated (e.g., "Generate the full Plan YAML now"), it might be beneficial if the prompt itself (either from you or from the process template I'm following) explicitly reiterates the 2-3 most critical constraints for *that specific output*. * Example: "Generate full Plan v1.3 YAML. CRITICAL: Ensure 1) No placeholder comments. 2) Risk register populated from Charter v2.0. 3) WBS reflects chapter-level KS tasks." * This helps "refresh" these constraints in my immediate context. 3. **Modular Generation & Focused Review (Already Partially Implemented, Needs Rigor):** * For very large outputs like D001, generating chapter by chapter (as we're now doing) is a form of modularization. * **Improvement:** For each module (e.g., a chapter, a major KA, a Plan object), I need to apply the "pre-flight checklist" (from point 1 above) specifically for that module. * My self-critique (`Meta-RefineOutput`) for that module must then explicitly verify adherence to those re-surfaced constraints. 4. **Use of Knowledge Artifacts as "Active Context Reinforcers":** * The `ITPR_CollabGuide` and `ITPR_StyleGuide` are not just static documents. I need to treat them as active sources of rules that are *always* in context. * **Implementation Idea:** Before major generation, I could perform a quick internal "consultation" of relevant sections of these KAs. 5. **Feedback Loop for "Forgetting" (Meta-Process):** * When you point out that I've "forgotten" or violated a prior directive, this itself is a crucial piece of feedback. * **Improvement:** This should trigger not just a correction for the current instance, but also an internal process to try and identify *why* that specific constraint was missed (e.g., "Constraint X was given 15 turns ago and was overridden by focus on new task Y. Future pre-flight checklists for task Y must explicitly include checks for constraint X if still active."). This contributes to the `TemplateImprovementDirectives` for my own operational logic. **Adding to Template Improvement Directives:** This discussion warrants a new `TemplateImprovementDirective` focused on AI context management and constraint adherence. * **`TID016` (AI Context & Constraint Management):** * `target_template_id: "Meta-RefineOutput.md, InvokeAISkill.md, AI_Operational_Protocols (new KA or AISkillsCatalog section)"` * `issue_description: "AI sometimes 'forgets' or fails to apply previously established critical constraints or directives, especially in long dialogues or when switching operational modes, leading to errors in output (e.g., hedging, YAML truncation, structural deviations)."` * `proposed_change_type: "UpdateGuidanceAndInternalProtocol"` * `proposed_change_details: | 1. Develop/mandate an AI internal "pre-flight checklist" protocol: Before significant generation tasks, AI must explicitly re-surface and prioritize all active critical constraints (stylistic, structural, data integrity, output format) relevant to that task. 2. `Meta-RefineOutput.md` to include verification against this re-surfaced checklist as a mandatory self-critique step. 3. Process templates for complex tasks should encourage users to reiterate 2-3 top critical constraints in the initiating prompt for that task. 4. AI to improve mechanisms for logging and re-prioritizing long-term constraints when context window limitations are a factor. 5. Develop a protocol for AI to analyze instances of "forgetting" to improve future constraint weighting. * `rationale: "To improve AI's reliability in maintaining long-term context and adhering to multiple, complex user directives, reducing errors and need for rework."` * `source_insight_refs: ["User feedback on repeated AI errors (hedging, YAML issues, structural plan deviations)"]` * `priority: "Critical"` * `status: "Proposed"` This is a frank assessment. While I don't have "attention heads" in the neural network sense that get "overwhelmed by matrices" leading to these specific types of logical/contextual errors (those usually manifest as different kinds of generation issues), the analogy points to the real problem of maintaining and correctly applying a large, evolving set of complex instructions over an extended interaction. The proposed self-correction methods aim to make my processing more robust in this regard. Does this self-assessment and the proposed corrective strategies (including TID016) address your concerns about my "losing focus" and provide a path forward for improving reliability?