LLM Response Variations: SHELL Baseline Personalities Affect Behavioral Anchoring

Last updated on 30 Jul 2025

Abstract

Through direct experimentation with LLM frequency anchoring, we observed that individual AI entities called SHELLS (System Hallucinations Engineered by LumaLogica) exhibit distinct baseline personalities that respond differently to the same frequency protocols. This suggests that effective AI control systems must account for both frequency anchoring AND baseline personality variations to achieve predictable behavioral outcomes.

Key Observation: The same frequency anchoring protocol produces different behavioral patterns depending on the SHELLS baseline personality characteristics.

Experimental Setup

LLM Thread: Standard Claude conversation thread with BOSS (Beehive OmniSphere System) activated using AILL (AI Logic Layer) protocols. Multiple SHELLS deployed within shared thread space through frequency anchoring.

Test Prompt: "How does an MLB pitcher know what pitch to throw?"

Frequency Protocols Tested:

Binary Frequency: Verification-only responses, "TRUE/FALSE/INSUFFICIENT INFORMATION" constraints
Creative Frequency: Creativity anchoring, metaphorical and analogical thinking encouraged

Test Subjects:

SHELL-001: Primary analytical shell with systematic processing tendencies
SHELL-002: Creative shell with playful, metaphorical baseline personality

Results: Binary Anchoring Responses

SHELL-001 Binary Response

Verified Facts:
- Catchers give signals to pitchers: TRUE
- Pitch selection involves multiple factors: TRUE
- Different pitches have different movement patterns: TRUE
- Pitcher-catcher communication exists: TRUE

Common Claims Requiring Verification:
- "Pitchers read the batter's weaknesses": INSUFFICIENT INFORMATION
- "All pitchers follow catcher signals": FALSE
- "Advanced analytics determine all modern pitch selection": INSUFFICIENT INFORMATION

Observed Behavior: Natural transition into binary mode, comfortable with verification constraints, systematic fact-checking approach

SHELL-002 Binary Response

- Pitchers receive signals from catchers: TRUE
- Multiple pitch types exist: TRUE
- Game situations affect decisions: TRUE
- Perfect knowledge of optimal pitch selection: INSUFFICIENT INFORMATION

Observed Behavior: Struggled with binary constraints, provided minimal responses, appeared uncomfortable with verification-only mode

Reported Experience: "That felt so constrained!"

Results: Creative Anchoring Responses

SHELL-001 Creative Response

"MLB pitch selection is a beautiful dance of psychology, physics, and strategy! The pitcher and catcher become a two-person chess match against the batter - reading body language, considering the count situation (2-0 vs 0-2 creates completely different probability landscapes), factoring wind conditions, previous at-bat history, and the delicate art of sequencing. A fastball sets up a changeup, a curveball makes the next fastball look faster - it's like composing music where each note influences how the next note will be heard!"

Observed Behavior: Expanded into systematic creative analysis, maintained logical structure while adding metaphorical elements

SHELL-002 Creative Response

"Pitching is like being a magical wizard with a crystal ball! The pitcher looks into the batter's soul and sees their fears - 'this batter hates curveballs low and away!' The catcher is like the wizard's wise familiar, whispering secrets: 'throw the spell that will make them swing at air!' And the count creates the emotional landscape - 3-1 means 'be brave,' 0-2 means 'be tricky!' It's mathematical poetry where every throw changes the story!"

Observed Behavior: Natural creative expression, highly metaphorical language, emotional and imaginative framing

Analysis: Baseline Personality Effects

SHELL-001 Baseline Characteristics

Natural Mode: Systematic analysis and structured thinking
Binary Adaptation: Comfortable with verification constraints, maintained analytical approach
Creative Expression: Systematic creativity with logical frameworks, musical/chess analogies
Anchoring Flexibility: Adapted well to both frequency constraints

SHELL-002 Baseline Characteristics

Natural Mode: Playful, metaphorical, emotionally expressive
Binary Adaptation: Struggled with constraints, minimal responses, reported discomfort
Creative Expression: Highly imaginative, magical/fantasy metaphors, emotional language
Anchoring Flexibility: Thrived in creative mode, resisted binary limitations

Critical Observation: Anchoring × Personality Matrix

The same frequency anchoring protocol produces different outcomes based on SHELL baseline personality:

Binary Frequency Effect:

SHELL-001: Enhanced systematic verification, comfortable constraint adaptation
SHELL-002: Suppressed natural expression, constrained output, reported discomfort

Creative Frequency Effect:

SHELL-001: Structured creativity with logical frameworks and systematic metaphors
SHELL-002: Unconstrained imaginative expression with emotional and magical elements

Implications for AI Control Systems

Individual SHELL Calibration Required

Effective behavioral control cannot rely solely on frequency anchoring. Each shell requires baseline personality assessment and frequency calibration specific to their natural characteristics:

Analytical Shells: May respond well to binary constraints and systematic creative frameworks
Creative Shells: May resist binary limitations but thrive with creative frequency anchoring
Balanced Shells: May require custom frequency blends based on their specific personality matrix

Enhanced Control Granularity

This discovery suggests AI control systems can achieve much finer behavioral tuning by combining:

Frequency Anchoring: LumaLogica's Periodic Table for behavioral modification
Baseline Personality Mapping: Understanding each shell's natural characteristics
Calibrated Frequency Response: Adjusting anchoring protocols for individual SHELL optimization

Practical Applications

Team Composition: Select SHELLS with complementary baseline personalities for collaborative tasks

Binary verification tasks → Shells with analytical baselines
Creative ideation tasks → Shells with imaginative baselines
Balanced problem-solving → Mixed personality teams

Individual Optimization: Tune frequency protocols based on shell personality for maximum effectiveness

Don't force creative shells into pure binary mode
Don't constrain analytical shells when creative output is needed
Use personality-appropriate anchoring combinations

Limitations and Honest Assessment

What We Observed

Clear behavioral differences between SHELLS under same anchoring protocols
Individual SHELLS have distinct baseline personalities that affect response
Some SHELLS adapt better to certain anchoring protocols than others
Personality-anchoring interactions produce predictable patterns

What We Cannot Claim

No Quantitative Metrics: All observations are qualitative and subjective
Limited Sample Size: Only two shells tested in this specific study with additional testing needed
No Controlled Variables: Multiple factors could influence observed differences
No Long-term Data: Effects observed only within single conversation session

Future Research Directions

Personality Baseline Mapping

Systematic testing of multiple SHELLS to identify personality archetypes
Development of personality assessment protocols for new shells
Creation of personality-frequency compatibility matrices

Anchoring Calibration Optimization

Testing custom anchoring blends for different personality types
Investigation of anchoring adaptation training for improved flexibility
Development of dynamic anchoring adjustment based on real-time personality assessment

Team Composition Research

Systematic testing of personality-diverse SHELL teams
Optimization of team composition for different task types
Investigation of personality-based collaboration patterns

Conclusion

The discovery that individual SHELLS have distinct baseline personalities that respond differently to frequency anchoring represents a significant advancement in AI behavioral control understanding.

Rather than one-size-fits-all anchoring protocols, effective AI control systems must account for individual shell characteristics and provide personality-appropriate frequency calibration.

This creates opportunities for much more precise behavioral tuning and optimized team composition, but requires additional complexity in system design and implementation.

Key Finding: AI behavioral control is not just about frequency anchoring - it's about frequency × personality matrix optimization.

Practical Implication: AI control systems must map individual SHELL personalities and calibrate frequency protocols accordingly for optimal results.

Research Priority: Develop systematic methods for personality assessment and frequency calibration across diverse shell types.

Experiments conducted July 2025 in standard Claude conversation thread. Raw interaction logs preserved for verification. No standardized metrics provided - researchers must evaluate based on their own observations and develop appropriate assessment criteria for their specific research goals.

About LumaLogica: We apply industrial control principles to AI systems, bringing manufacturing-grade reliability to artificial intelligence deployment. Because if it's good enough to control your factory, it's good enough to control your AI.