What is GESA?

Generative Episodic Simulated Annealing

GESA is the optimization layer of the Cormorant Foraging Framework. It is Layer ∞ — the feedback loop that turns a sequence of decisions into a learning system.

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The One-Line Answer

GESA determines how to act differently over time — generating improved strategies from accumulated episode history using a simulated annealing schedule.

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Where GESA Sits

Layer 0:  Cormorant 3D (Chirp / Perch / Wake)   →  Sense
Layer 1:  DRIFT                                  →  Measure the gap
Layer 2:  Fetch                                  →  Decide to act
Layer ∞:  GESA                                   →  Optimise across episodes

Each layer depends on the one above it. GESA depends on all of them — and feeds back into all of them.

Without GESA: The stack senses, measures, and acts. But each run is independent. No learning accumulates.

With GESA: Every act becomes an episode. Every episode informs the next act. The stack improves over time.

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Three Core Concepts

GESA is built on three concepts that combine into the loop:

1. Episodic Memory

Not semantic memory (general facts) but situated experience with temporal context:

Memory Type	Example	What It Encodes
Semantic	"Burnout is caused by overload"	General knowledge
Episodic	"On March 3rd, reducing context switches for Team A resolved a pain streak within 2 sprints"	Context + action + outcome + time

Every Fetch decision, every DRIFT measurement, every intervention outcome becomes an immutable episode stored with full context.

→ Episodic Memory in depth

2. Simulated Annealing

Derived from metallurgy: heat metal to make it malleable, cool it slowly.

• High temperature (early) → Accept suboptimal moves. Explore widely. Escape local traps.
• Low temperature (late) → Narrow toward proven solutions. Exploit what works.

The critical insight: deliberate acceptance of worse outcomes early prevents getting stuck in local optima.

In GESA, temperature governs exploration vs exploitation across the episode timeline.

→ Simulated Annealing in depth

3. Generative Output

GESA does not retrieve cached answers. It generates new strategies by synthesising:

1. Relevant past episodes (retrieved by similarity)
2. Current context (DRIFT, Fetch, 3D scores)
3. Annealing temperature (how bold vs conservative to be)

The output is always a novel hypothesis — a candidate action that didn't necessarily exist before.

→ Generative Output in depth

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The Name, Unpacked

Word	Meaning in GESA
Generative	Produces novel strategies, not cached lookups
Episodic	Operates on situated, temporal memory — not facts
Simulated	Borrows the annealing metaphor — not real metallurgy
Annealing	The cooling schedule that governs exploration vs exploitation

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What Makes GESA Different

Approach	How It Learns
Reinforcement Learning	Reward signal updates a value function (implicit, neural)
Case-Based Reasoning	Retrieve → Adapt → Reuse (deterministic, no temperature)
GESA	Retrieve → Generate → Anneal → Select (explicit temperature, observable)

GESA's novel contribution: the integration of episodic case-based reasoning with an explicit annealing schedule inside a biomimetic sensing framework — making the exploration/exploitation tradeoff explicit, observable, and tunable rather than implicit in a neural architecture.

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The Completeness it Provides

Question	Framework
What do I perceive?	3D Foundation
Where is the gap?	DRIFT
Should I act now?	Fetch
How should I act differently next time?	GESA

The first three questions make the system capable. The fourth makes it intelligent.

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"Without GESA, the stack is reactive. With GESA, it becomes adaptive." 🦅