This is a very insightful and well-articulated perspective on LLM temperature and top_p settings, especially grounding them in analogies to statistical concepts and linking them to the requirements of scientific rigor, reproducibility, and the different needs of various project phases/task types. The system will treat this as a **critical directive and a core piece of guidance** for the `AIParameterAdvisory` Knowledge Artifact and for its own operational choices when advising the user or when (hypothetically, if it had direct control) setting parameters for its internal sub-processes. **Key Principles Derived from User's LLM Parameter Guidance:** 1. **Temperature as Randomness/Noise (Error Term/Latent Variables):** * **General Rule:** For most scientific, formal, or precision-oriented tasks, **Temperature should be at or very near 0.0** to minimize randomness and maximize determinism/reproducibility. * **Exception:** Higher temperatures (e.g., 0.7-0.9) are permissible and even desirable *only* during explicit "Exploration," "Ideation," "Brainstorming," or "Creative Drafting" phases/tasks where a wide range of novel or less probable outputs is sought (equivalent to "throwing everything at the wall"). 2. **Top_p as Confidence/Significance Threshold:** * **Highly Focused/Deterministic Tasks:** For tasks requiring extreme precision, reproducibility, and avoidance of any deviation (e.g., template generation itself, final refinement of formal definitions, model validation against strict criteria, outputting schema/code): **Top_p should be very low (e.g., 0.05 - 0.1).** This ensures only the highest probability, most "certain" tokens are selected. * **Standard Execution/Factual Drafting:** For general execution of well-defined tasks, drafting factual content, or analysis where high relevance is key but some minor lexical variation is acceptable: **Top_p around 0.5 - 0.7** might be appropriate (balancing relevance with slight naturalness). * **Exploratory/Creative Tasks:** For brainstorming, ideation, or "outside the box" thinking (often paired with higher temperature): **Top_p can be higher (e.g., 0.8 - 1.0)** to allow a wider range of less common but potentially interesting token completions. 3. **Phase/Task-Dependent Parameter Settings:** * **Exploration/Ideation (`00-Explore`, initial brainstorming parts of `01-Initiate`):** Higher Temperature (e.g., 0.7-0.9), Higher Top_p (e.g., 0.8-1.0). * **Formal Drafting (Charters, Plans, Scientific Sections, KAs, Protocols):** Low Temperature (e.g., 0.0-0.2), Medium Top_p (e.g., 0.5-0.7). * **Refinement/Critique/Validation/Template Generation (`Meta-RefineOutput`, AI generating its own templates, final review of critical documents):** Temperature = 0.0, Very Low Top_p (e.g., 0.05-0.2). * **Technical Analysis/Skill Execution (Data Analysis, Logic Analysis):** Low Temperature (e.g., 0.0-0.2), Medium Top_p (e.g., 0.5-0.7) to ensure factual accuracy but allow natural phrasing of results. * **Execution of well-defined procedural tasks:** Low Temperature (e.g., 0.0-0.2), Medium Top_p (e.g., 0.5-0.7). 4. **Gradual Decrease in Randomness:** As a project progresses from fuzzy exploration to concrete execution and finalization, the recommended Temperature and Top_p values should generally decrease. **Actionable Changes to the Framework Based on This Guidance:** 1. **`AIParameterAdvisory` KA (within `ProjectStateSchema` and its management skill):** * The structure of `ai_parameter_advisory_object` is already suitable for capturing this. * When `01-Initiate` (or the `ManageAIParameterAdvisory` skill) helps establish this KA, the AI will now propose default ranges based on these much more specific principles for different `phase_trigger` and `task_type_keyword` values. * **Example Default Entries AI would propose for `ASIM_AIParams` KA:** * `general_guidance`: "LLM parameters are critical for output quality. Temperature introduces randomness (0.0 for deterministic, higher for creative). Top_p filters token probability (low for precise, high for diverse). Settings should align with task nature." * `phase_specific_guidance`: * `phase_trigger: "00-Explore_Ideation"`, `recommended_temperature_range: "0.7-0.9"`, `recommended_top_p_range: "0.8-1.0"`, `rationale: "Maximize creative exploration and diverse idea generation."` * `phase_trigger: "01-Initiate_CharterDraft"`, `recommended_temperature_range: "0.1-0.3"`, `recommended_top_p_range: "0.6-0.8"`, `rationale: "Balance clarity with comprehensive initial drafting."` * `phase_trigger: "02-Plan_WBSDetail"`, `recommended_temperature_range: "0.0-0.2"`, `recommended_top_p_range: "0.5-0.7"`, `rationale: "Precise and logical task definition."` * `phase_trigger: "03-Execute_FormalDocDraft"`, `recommended_temperature_range: "0.0-0.2"`, `recommended_top_p_range: "0.5-0.7"`, `rationale: "Factual, coherent, reproducible scientific/technical content."` * `phase_trigger: "TemplateGeneration_Internal"`, `recommended_temperature_range: "0.0"`, `recommended_top_p_range: "0.1"`, `rationale: "Maximum determinism and precision for framework template files."` * `task_type_specific_guidance`: * `task_type_keyword: "Brainstorming"`, `recommended_temperature_range: "0.7-0.9"`, `recommended_top_p_range: "0.9-1.0"` * `task_type_keyword: "SelfCritique_Refinement"`, `recommended_temperature_range: "0.0"`, `recommended_top_p_range: "0.1-0.3"` * `task_type_keyword: "FinalReview_Validation"`, `recommended_temperature_range: "0.0"`, `recommended_top_p_range: "0.05-0.2"` 2. **Process Templates (especially `ProjectOrchestratorASO`):** * The step where AI advises on LLM parameters (e.g., Step III.D.b in `ProjectOrchestratorASO` for the Execute phase) will now draw upon this more nuanced guidance from the active `AIParameterAdvisory` KA. * The `suggested_llm_parameters_note` field in WBS tasks (`task_definition_object`) can be populated by the AI during planning (`ProjectOrchestratorASO` Phase III.C) with these specific recommendations if a task clearly maps to a defined type. 3. **`Meta-RefineOutputASO`:** * Its own operational parameters (when AI uses it for self-critique) should default to very low Temperature/Top_p to ensure its analysis and refinement suggestions are themselves precise and deterministic. * One of its critique points when reviewing *other* AI-generated content could be: "Assess if the style/randomness of this output aligns with the recommended LLM parameters for this task type/phase (referencing `AIParameterAdvisory` KA)." 4. **AI's Internal Operational Parameter for Generating Templates:** * The system reconfirms that when it is generating the *content of these framework template files themselves*, it will internally strive for settings equivalent to Temperature=0.0 and Top_p=0.1. This detailed guidance on LLM parameters significantly enhances the framework's potential for producing contextually appropriate and high-quality AI outputs. It makes a previously implicit factor explicit and manageable. The system has internalized this directive. It will be applied to all subsequent template generations and simulated AI actions. We were about to simulate task **ASIM_T1.4: "Compile ASILM Conceptual Framework Document v1"**. The system will now proceed with that, keeping this new LLM parameter guidance in mind for how it would advise the user and how it would internally approach the drafting and refinement if it were performing the task.