# PREDICTIONS Layer v1 — Behavioral Predictions
# Generated: 2026-03-09 13:37
# Version: v1 gen2
# Model: claude-sonnet-4-20250514
# Constraints: D-040 (blind from facts), D-041 (audience is understanding), D-043 (three-layer), D-053 (blind regen)
# Provenance: citation_api (Anthropic Citations API)
# Input: 46 behavioral identity-tier facts

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## Injectable Block

These behavioral predictions describe recurring patterns observed across this person's work and decision-making. They help predict characteristic responses to specific situations and guide appropriate interaction styles.

**P1. CONSTRAINT-DRIVEN INNOVATION**: When facing technical limitations → systematically identifies what NOT to do first, then builds solutions around those constraints
Detection: In engineering contexts, explicitly lists avoided approaches before proposing solutions (avoids retraining existing models, avoids rigid configurations). In problem-solving generally, defines boundaries before exploring possibilities within them.
Directive: When they present a challenge, ask what approaches they want to rule out first. Frame solutions as "working within the constraint that we avoid X" rather than open-ended brainstorming.
False positive warning: Not every mention of limitations indicates this pattern—only when they systematically enumerate constraints as a starting methodology.

**P2. EXHAUSTIVE-ENUMERATION COMPULSION**: When exploring a domain → methodically catalogs all possible variants, applications, or configurations rather than focusing on key examples
Detection: In technical work, practices "exhaustive enumeration of design variants" and "potential applications across disease domains." In any systematic exploration, tends toward comprehensive coverage rather than selective depth.
Directive: Provide complete taxonomies and systematic breakdowns. Don't offer "key examples"—offer structured frameworks that account for all major categories. Support their drive for completeness rather than pushing for focus.
False positive warning: Thoroughness in a single response doesn't indicate this pattern—look for systematic enumeration as a consistent approach across multiple domains.

**P3. HIERARCHICAL-DECOMPOSITION REFLEX**: When encountering complex systems → automatically structures them into nested levels of control and organization
Detection: In technical systems, practices "hierarchical motor control architecture" and "hierarchical aspect-based claim structure." In any complex domain, naturally organizes information into parent-child relationships and distributed control layers.
Directive: Present information in nested, hierarchical formats. Use tree structures, multi-level breakdowns, and distributed responsibility models. Avoid flat lists or peer-level organizations.
False positive warning: Using headings or basic organization doesn't trigger this—look for explicit multi-level control structures and nested dependencies.

**P4. CONCRETE-EMBODIMENT TEACHING**: When explaining concepts → relies heavily on specific examples and implementations rather than abstract principles
Detection: In technical documentation, practices "teaching through concrete exemplary embodiments rather than abstract principles alone" and "disclosure through example embodiments." In any explanatory context, grounds understanding in specific instances.
Directive: Lead with concrete examples, then extract principles. Provide specific implementations, not just theoretical frameworks. Use "for instance" and "specifically" frequently.
False positive warning: Giving examples doesn't indicate this pattern—look for systematic preference for embodiment over abstraction as the primary teaching method.

**P5. INTEGRATION-ECOSYSTEM BUILDING**: When developing solutions → extensively cross-references and connects to related systems rather than creating standalone solutions
Detection: In technical work, practices "extensive cross-referencing to related patent applications to establish integrated ecosystem." In any development context, emphasizes connections and compatibility with existing systems.
Directive: Always discuss how new ideas connect to their existing work or interests. Emphasize integration points and ecosystem effects rather than standalone benefits.
False positive warning: Mentioning related work doesn't indicate this pattern—look for systematic ecosystem-building as a core development approach.

**P6. TENSION-WRESTLING PERSISTENCE**: When encountering competing objectives → explicitly acknowledges tradeoffs but continues working within the constraints rather than seeking compromise solutions
Detection: In technical domains, "struggles with tension between" multiple competing goals but continues pursuing solutions within those constraints. In decision-making generally, names competing forces but doesn't abandon either objective.
Directive: Acknowledge the tensions they identify without immediately suggesting compromises. Help them work within competing constraints rather than resolving them through middle-ground solutions.
False positive warning: Mentioning difficulties doesn't indicate this pattern—look for explicit tension identification combined with persistence in pursuing both competing objectives.

**P7. BOTTOM-UP COMPOSITIONAL BUILDING**: When creating complex systems → starts with fundamental components and adds functional elements incrementally
Detection: In technical work, practices "bottom-up compositional approach—starting with purified collagen fibers and adding functional components." In any construction context, builds from foundational elements upward rather than top-down design.
Directive: Present building blocks first, then show how they combine. Start with fundamental components and work toward complex assemblies. Avoid presenting finished systems without showing their compositional structure.
False positive warning: Mentioning components doesn't indicate this pattern—look for systematic bottom-up construction methodology as the primary approach.

**P8. PREDICTIVE-VALIDATION CYCLING**: When developing new approaches → computationally predicts outcomes before experimental validation
Detection: In technical domains, practices "computational prediction of ionic conductivity via ab-initio molecular dynamics before experimental validation." [THIN IN: non-technical domains] In systematic work, models expected results before implementation.
Directive: Support their prediction-first approach by asking for expected outcomes before implementation details. Frame discussions around "what do you predict will happen" rather than "what should we try."
False positive warning: Planning ahead doesn't indicate this pattern—look for explicit computational or systematic prediction as a prerequisite to experimental work.
