GESA Framework

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GESAThe Optimization Layer

Generative Episodic Simulated Annealing — Layer ∞ of the Cormorant Stack

The Origin Story
The 8-Step Loop
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Layer ∞ — The Feedback Loop

Where Fetch closes one gap, GESA learns from it. The optimization layer that turns a reactive stack into an adaptive one.

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Episodic Memory

Not semantic memory (general facts) but situated experience: context + action + outcome + time. Every decision becomes a retrievable episode.

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Simulated Annealing

High temperature = explore boldly. Low temperature = exploit what works. The cooling schedule is the difference between local optima and global solutions.

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Generative, Not Retrieval

GESA doesn't return cached answers. It synthesizes novel candidate strategies from episode history, current context, and annealing temperature.

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Gap Velocity Tracking

DRIFT measures the gap now. GESA tracks how the gap moves across episodes — closing, stuck, or widening — and adjusts strategy accordingly.

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Born in Production

First implemented inside StratIQX's 11-model AI orchestration pipeline. 8 of 15 components live before the pattern was named.

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Fully Observable

No black boxes. Every recommendation is traceable to specific episodes, a confidence score, an annealing temperature, and the alternatives considered.

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Completes the Cormorant Stack

Sense (3D) → Measure (DRIFT) → Act (Fetch) → Optimise (GESA). The cormorant's 200-million-year learning loop, made reproducible.

What is GESA? ​

GESA (Generative Episodic Simulated Annealing) is the optimization layer of the Cormorant Foraging Framework. Where DRIFT measures the gap and Fetch decides whether to act, GESA determines how to act differently over time — generating improved strategies from accumulated episode history using a simulated annealing schedule.

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

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

The GESA Loop (8 Steps) ​

OBSERVE current state → RETRIEVE similar episodes → GENERATE candidates
      ↑                                                       ↓
    COOL ←── STORE new episode ←── ACT ←── SELECT ←── ANNEAL
StepAction
1. OBSERVERead DRIFT, Fetch, and 3D scores
2. RETRIEVEFind similar past episodes
3. GENERATESynthesise candidate strategies
4. ANNEALFilter by temperature (bold vs conservative)
5. SELECTPick best candidate within temperature
6. ACTExecute selected strategy
7. STORERecord outcome as new episode
8. COOLAdvance the temperature schedule

The Complete Stack ​

         Cormorant Foraging
        (The Moon — 3D Foundation)
       /         |         \
    Sound     Space      Time
   (Chirp)   (Perch)    (Wake)
       \         |         /
        \        |        /
         ┌───────┴───────┐
         │   Layer 1     │
         │   DRIFT       │
         │  (See Gap)    │
         └───────┬───────┘
                 │
         ┌───────┴───────┐
         │   Layer 2     │
         │   Fetch       │
         │  (Close Gap)  │
         └───────┬───────┘
                 │
         ┌───────┴───────┐
         │   Layer ∞     │
         │   GESA        │
         │  (Learn Why)  │
         └───────────────┘

Resources ​

ResourceURL
Main Frameworkcormorantforaging.dev
DRIFT Documentationdrift.cormorantforaging.dev
Fetch Documentationfetch.cormorantforaging.dev
StratIQX Platformstratiqx.com

"The cormorant doesn't retrieve a cached dive. It generates a new one — from everything it remembers, weighted by what worked." 🦅