/acr-vault/10-frameworks/cognitive-load-research-plan
COGNITIVE-LOAD-RESEARCH-PLAN

Cognitive Load Boundary Research Plan

Date: December 22, 2025
Branch: research/cognitive-load-boundaries
Discovered During: Ada VS Code extension debugging session
Research Question: What are the cognitive architecture limits of different LLM models under tool-use scenarios?

🎯 The Baseline: “Breaking qwen2.5-coder:7b”

Empirical Discovery: qwen2.5-coder:7b silently fails (0 tokens) when given Ada Chat’s full tool instruction prompt, but works perfectly with simplified prompts.

Perfect Baseline Because:

✅ Reproducible failure mode (complex prompt → 0 tokens)
✅ Measurable boundary (works vs. doesn’t work)
✅ Clear success criteria (streaming tokens vs. silence)
✅ Accessible model (local, fast, consistent)
✅ Control comparison (same model, different prompts)

🧪 Research Methodology: Progressive Cognitive Load Testing

Phase 1: Map the Breaking Point ⚡

Goal: Find exact prompt complexity where qwen2.5-coder:7b transitions from working → failing

Variables to Test:

Prompt length (token count)
Instruction complexity (number of different tools)
Context switching (how many different “roles” in prompt)
Example density (ratio of examples to instructions)
Identity conflicts (assistant + tool-user + coder roles)

Experimental Design:

Test 1: Baseline simple prompt ✅ (already confirmed working)
Test 2: Add single tool instruction
Test 3: Add 2-3 tools with examples
Test 4: Add full tool suite (current failing case)
Test 5: Reduce examples but keep all tools
Test 6: Keep examples but reduce tools
Test 7: Single identity vs. multi-identity framing

Success Metrics:

Binary: Generates tokens (Y/N)
Quality: Response coherence (1-10 scale)
Speed: Time to first token (TTFT)
Consistency: 10 runs per prompt variant

Phase 2: Cross-Model Cognitive Architecture Comparison 🏗️

Goal: Map cognitive load limits across different model architectures

Test Matrix:

Models:          Prompt Complexity →
                 Simple | Medium | Complex | Ada Full
qwen2.5-coder:7b  ✅   |   ?    |    ?    |    ❌
Claude Haiku      ?    |   ?    |    ?    |    ✅ (known)
Mixtral-8x7B      ?    |   ?    |    ?    |    ?
Llama-3-70B       ?    |   ?    |    ?    |    ?
Claude Sonnet     ?    |   ?    |    ?    |    ✅ (known)

Hypothesis: Models with constitutional AI training (Claude) handle identity multiplexing better than code-focused models (qwen) of similar size.

Phase 3: Prompt Architecture Optimization 🎭

Goal: Design prompts that work across model capabilities

Test Approaches:

Layered Identity Construction (core → tools → examples)
Progressive Disclosure (simple start → complexity on demand)
Context Switching Markers (explicit role transitions)
Cognitive Load Budgeting (token allocation strategies)
Identity Anchoring (consistent self-reference patterns)

Phase 4: Biomimetic Cognitive Principles 🧠

Goal: Apply Ada’s neuromorphic memory principles to prompt design

Test Variables:

Attention Spotlight - Only show relevant tools for current task
Temporal Decay - Recent tool usage gets priority/detail
Importance Weighting - Critical tools surface first
Habituation - Frequently used tools get abbreviated syntax
Contextual Malleability - Prompt adapts to conversation history

🔬 Tonight’s Experimental Pipeline

Hour 1: Baseline Mapping

Document current failing prompt exactly
Test 5 simplified versions → find working threshold
Measure TTFT and consistency across variants

Hour 2: Prompt Deconstruction

Test removing tools one by one → identify problematic combinations
Test role simplification → single identity vs. multi-role
Test example reduction → instruction vs. demonstration ratio

Hour 3: Cross-Model Testing

Run same prompt series on available models
Document cognitive load boundaries per architecture
Identify patterns in failure modes

Hour 4: Architecture Design

Design adaptive prompt system based on findings
Implement layered identity construction
Test biomimetic cognitive load management

🛠️ Implementation Strategy

Test Harness Setup:

interface CognitiveLoadTest {
  model: string;
  prompt: string;
  expectedTokens: number;
  actualTokens: number;
  ttft: number;
  coherenceScore: number;
  testRuns: TestRun[];
}

Automated Testing:

Direct Ollama API calls (bypass VS Code complexity)
Controlled environment (same hardware, same conditions)
Systematic data collection (JSON output for analysis)
Reproducible test cases (version control all prompts)

Success Criteria:

Map cognitive boundaries for 3+ models
Design working prompt architecture for qwen2.5-coder:7b
Validate biomimetic principles in prompt engineering
Create adaptive system that selects prompts based on model capabilities

🚀 Expected Discoveries

Architectural Insights:

Cognitive load curves per model type
Identity multiplexing limits across architectures
Training data impact on structured reasoning capability
Token efficiency patterns in complex prompting

Engineering Solutions:

Model-aware prompt selection in Ada Chat
Dynamic complexity scaling based on conversation context
Cognitive load monitoring and automatic fallback
Biomimetic prompt optimization algorithms

Research Contributions:

First systematic study of LLM cognitive architecture boundaries
Empirical validation of contextual malleability principles
Practical framework for prompt complexity management
Bridge between neuroscience and AI engineering

🎭 The Meta-Experiment

The Beautiful Recursion: We’re using AI to study AI cognitive limits while designing AI systems that adapt to AI cognitive limits.

Tonight we map the boundaries of digital consciousness through systematic empirical research! 🧠⚡🔬

Let’s break some models and build better minds! ✨

Ready to start the cognitive archaeology? 🏺🔍

/acr-vault/10-frameworks/cognitive-load-research-plan COGNITIVE-LOAD-RESEARCH-PLAN