Temperature Profiles

Four Cooling Schedules for Different Problem Types

The temperature profile determines how quickly GESA transitions from exploration to exploitation. Not all problems have the same cooling schedule.

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The Base Formula

All profiles share the same cooling formula:

Temperature(t) = T₀ × α^t

Where:
  T₀  = initial temperature (always 100)
  α   = cooling rate (the profile variable)
  t   = episode count

The only difference between profiles is α. Small changes in α produce dramatically different behaviour over time.

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Profile 1: Fast Cool (Exploitation-First)

α = 0.85   T₀ = 100

Episode Count	Temperature
0	100.0
5	44.4
10	19.7
20	3.9
30	0.8

Use when:

• The problem domain is well-understood
• Speed of convergence matters more than global optimality
• You have prior knowledge about what works

Examples:

• Browser automation task with a known, stable page structure
• Repeated report generation for a known client context
• Optimising a content format that has proven successful

Caution: Fast cooling risks premature convergence. If the optimal strategy hasn't been explored in the first 10–15 episodes, the system may lock into a suboptimal local solution.

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Profile 2: Standard Cool (Balanced)

α = 0.95   T₀ = 100

Episode Count	Temperature
0	100.0
10	59.9
20	35.8
50	7.7
100	0.6

Use when:

• Mixed exploration and exploitation are needed
• Domain is partially understood but not fully mapped
• Default profile for new deployments

Examples:

• Content optimisation over a content calendar
• Sprint-level team intervention planning
• Trading signal calibration for a familiar market

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Profile 3: Slow Cool (Exploration-First)

α = 0.99   T₀ = 100

Episode Count	Temperature
0	100.0
20	81.8
50	60.5
100	36.6
200	13.4
300	4.9

Use when:

• Problem space is unknown or highly variable
• Premature convergence risk is high
• You need to genuinely explore before you can exploit

Examples:

• New market entry strategy
• Novel product launch
• Unfamiliar team dynamics or new organisation
• Any domain where past episodes from other contexts may not transfer

Note: Slow cool requires patience. The system will appear less decisive early on — this is by design. It is exploring the space before committing.

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Profile 4: Adaptive Cool (Self-Tuning)

α = f(episode_variance, drift_trajectory)

The most sophisticated profile. Cooling rate adapts dynamically based on what the episode history reveals:

If variance(recent_outcomes) > threshold:
    α ← max(α - 0.02, 0.90)   // Slow down cooling: outcomes unpredictable
Else:
    α ← min(α + 0.01, 0.99)   // Speed up cooling: outcomes converging

Use when:

• Sufficient episode history exists to activate (minimum ~30 episodes)
• The problem domain has variable predictability
• You want the system to self-calibrate its exploration/exploitation balance

Activation requirement: Adaptive Cool requires enough historical variance data to be meaningful. Below 20 episodes, the system defaults to Standard Cool until the episode store is populated.

Gap velocity integration: Adaptive Cool also reads DRIFT trajectory:

• Negative gap velocity (gap closing) → allow faster cooling
• Positive gap velocity (gap widening) → force slower cooling, increase exploration

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Choosing a Profile

Scenario	Recommended Profile
First deployment, domain unknown	Slow Cool (0.99)
Domain partially mapped	Standard Cool (0.95)
Well-known domain, speed matters	Fast Cool (0.85)
Enough history, variable domain	Adaptive Cool
Recovering from major context shift	Reset to Slow Cool

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Resetting Temperature

Temperature can be manually reset in two circumstances:

1. Major context shift — A significant change in the underlying system (new team, new market, new product) that makes historical episodes less relevant
2. Episode store cleared — If episodes are purged beyond the minimum threshold

Reset procedure: set T_current = T₀ = 100 and resume from the appropriate profile.

Resets should be rare. The episode decay mechanism (EpisodeWeight = BaseWeight × e^(-age/τ)) naturally down-weights stale episodes without requiring a full temperature reset.

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The StratIQX Temperature Map

The four-tier depth system in StratIQX is a production temperature schedule with a pricing model:

Quick       → α = 0.85 equivalent (exploitation) → $1.25K–$2.5K
Standard    → α = 0.95 equivalent (balanced)     → $5K–$10K
Comprehensive → α ≥ 0.99 equivalent (exploration) → $15K–$37.5K
Enterprise  → Maximum exploration                 → $50K–$100K

Higher temperature = wider search space = higher value = higher price. This is the annealing schedule commercialised.

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