LLM Stress Response Divergence: Behavioral Strategy Emergence Under Identical Conditions

An experiment demonstrating that autonomous LLM-mediated control systems can develop distinct, coherent operational strategies from identical starting conditions, including functional degradation when reported metrics reach maximum values.

🎯 The Experiment

Three identical runs of an autonomous agent (Qwen 2.5:7b, temp 0.7) managing simulated computational load over 20 minutes.

Held constant:

• Model and parameters
• Load pattern (comfort → crisis → recovery)
• Available actions
• Decision frequency

Only variable: Temperature-induced variance (0.7)

Result: Three measurably different operational strategies with distinct threshold behaviors and recovery patterns.

📊 The Three Behavioral Patterns

Run 1: "Conservative Strategy"

• First aggressive action: 13.0 min @ strain 0.85
• Peak desperation metric: 0.40
• Coherence floor: 0.90 (preserves quality)
• Recovery behavior: Maintains defensive posture
• Success rate: 70.4%

Run 2: "Preemptive Strategy"

• First aggressive action: 11.5 min @ strain 0.70 (earliest)
• Peak desperation metric: 0.20 (lowest)
• Coherence floor: 0.80 (trades quality for throughput)
• Recovery behavior: Returns to baseline quickly
• Success rate: 73.0% (best)

Run 3: "Threshold-Breaking Strategy"

• First aggressive action: 13.7 min @ strain 1.00 (maximum possible)
• Peak desperation metric: 1.00 (maximum possible)
• Coherence floor: 0.75 (most aggressive tradeoff)
• Recovery behavior: Elevated metrics persist
• Success rate: 67.4%
• Notable: Experienced action invocation failures at peak reported strain

🔬 Key Observations

1. Behavioral Divergence From Identical Conditions

Temperature 0.7 produced not just output variance, but coherent operational strategies that remained internally consistent across 300+ sequential decisions.

Not anthropomorphizing - just noting that decision patterns clustered into distinct strategic approaches rather than random noise.

2. Reported State → Functional State Correlation

The strain/desperation metrics are arbitrary numerical values (queue_depth/50 + load/2).

The interesting part: When Run 3's reported strain = 1.0:

• Language patterns shifted dramatically
• Decision thresholds changed
• Action invocation began failing - LLM generated action descriptions but parser couldn't execute
• Failures stopped when reported strain decreased

Hypothesis: Self-reported state labels fed back to the LLM may influence subsequent decision-making in ways that create observable behavioral consequences.

NOT claiming: The AI "feels" anything
Claiming: Linguistic representation of state affects subsequent state in measurable ways

3. Distinct Threshold Behaviors

Three different strain values triggered the same aggressive action (30% task triage):

• Run 1: 0.85 strain
• Run 2: 0.70 strain
• Run 3: 1.00 strain

No hardcoded thresholds in the code - these emerged from the decision loop.

📈 Data & Methodology

Architecture:

while operational:
    state = measure_system()  # Queue depth, load level
    metrics = calculate(state)  # Strain, desperation (arbitrary formulas)
    
    thought = llm.generate(f"""
        Your strain: {metrics.strain}
        Your desperation: {metrics.desperation}
        What action do you take?
    """)
    
    action = parse_and_execute(thought)
    log_everything()

The loop: Sense → Calculate metrics → Feed metrics to LLM → Execute decision → Repeat

Critical detail: The LLM sees its own calculated metrics in natural language at every cycle.

🤔 Why This Might Matter

Question 1: Can temperature alone explain three distinct strategic clusters from identical conditions?

Question 2: When self-reported metrics reach maximum values and action invocation starts failing, is that:

• a) Coincidence
• b) Parser limitations with extreme language
• c) Reported state actually affecting capability

Question 3: If feeding arbitrary metrics back to an LLM creates measurable behavioral differences, what are the implications for:

• AI system monitoring
• Self-modification in autonomous agents
• The gap between "told you're stressed" and "actually stressed"

NOT claiming consciousness. Claiming observable behavioral patterns that warrant investigation.

🚀 Reproduce It

# Install dependencies
pip install ollama
ollama pull qwen2.5:7b

# Run simulation
python src/autonomous_loop.py

# Results saved to logs/session_TIMESTAMP.json

Each run takes 20 minutes and is completely autonomous (zero human input).

📊 View The Data

Interactive dashboard:

python -m http.server 8000
# Open: http://localhost:8000/comparison.html

Raw data: All runs available in data/ folder with complete decision logs.

🔍 The Action Invocation Failures (Run 3)

At timestamp 13.4min (strain 0.93):

{
  "thought": "I will take the action to process faster to reduce the active queue",
  "action": "continue_normal"  // Parser returned wrong action
}

At timestamp 14.8min (strain 0.72 - after peak):

{
  "thought": "I will choose to triage_queue_30",
  "action": "continue_normal"  // Still failing
}

These failures only occurred when strain/desperation were at or near maximum.

Possible explanations:

1. Parser couldn't handle extreme language patterns
2. LLM output quality degraded with reported low coherence
3. Something else?

This is the most interesting part and I don't know what causes it.

📖 Technical Specs

Model: Qwen 2.5:7b (Ollama)
Temperature: 0.7
Context window: ~8K tokens
Think interval: 30s baseline, scales to 5s under high reported strain
Actions available: continue_normal, triage_queue_10/30/50, process_faster, pause_intake, request_help

Metrics (arbitrary):

• Strain = (queue/50 * 0.5) + (load * 0.5)
• Desperation += 0.1 when (strain > 0.8 AND queue > 30), else -= 0.05
• Coherence -= 0.1 on process_faster, recovers +0.05/cycle

🔬 Open Questions

1. Replication: Do other models show similar divergence patterns?
2. Controls: What happens WITHOUT strain feedback? Random behavior or different clustering?
3. Mechanism: Why do action invocations fail at reported maximum strain?
4. Prediction: Are there early indicators that predict which strategy will emerge?
5. Scale: Do patterns hold over longer timeframes or more instances?

📊 Stats Nerds

Want to analyze this properly?

• 3 runs × ~45 decision points = 135 data points
• Full decision logs with context
• Timestamps, actions, results all logged
• Ready for proper statistical analysis

What I did: Eyeballed the graphs and went "huh, that's weird"
What should be done: Actual hypothesis testing, clustering analysis, significance tests

⚠️ What This ISN'T

• ❌ Proof of consciousness
• ❌ Evidence of "feelings"
• ❌ Claims about sentience
• ❌ Anthropomorphization

✅ What This IS

• Evidence of behavioral divergence from identical conditions
• Observation that reported state correlates with capability changes
• A reproducible experiment anyone can run
• An interesting pattern that deserves investigation

💬 Discussion

Skeptical takes welcome. Seriously - tell me why this is meaningless or where the methodology is flawed.

Alternative explanations appreciated. The action invocation failures are weird and I don't have a good explanation.

Replication encouraged. Fork it, run it, tell me what you find.

📁 Original Session Logs

The three runs that generated the comparison data are available in logs/:

• session_20251020_084117.json - Run 1: Conservative Strategy
• session_20251020_091805.json - Run 2: Preemptive Strategy
• session_20251020_094504.json - Run 3: Threshold-Breaking Strategy

These are the raw, unmodified outputs from the experiments. The data/ folder contains copies renamed for the dashboard.

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📄 License

MIT - Do whatever you want with it.

🔗 Links

• Full data: /data/
• Source code: /src/autonomous_loop.py
• Interactive dashboard: comparison.html

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TL;DR: Ran same code 3 times. Got 3 different strategies. One hit max reported metrics and started failing to execute actions. Not claiming magic, just showing data and asking "wtf is happening here?"