# 01 — LAKA Volumetric Model

## 1. The production decision envelope

Every production decision uses the same internal variables.

| Variable | Question |
|---|---|
| Object | What is being acted upon? |
| Conditions | Under what circumstances does the system operate? |
| Actions | What does the system do? |
| Tools | What mechanisms carry out the action? |
| Resources | What information, assets, people, compute, or capital are required? |
| Outcomes | What does the system produce? |
| Feedback | How does the result change later decisions? |
| Constraints | What limits performance, legality, continuity, or adoption? |
| Value | For whom is value created, and what kind? |
| Failure mode | How can the decision fail? |

## 2. Five change levels

| Level | Meaning | Media example |
|---|---|---|
| 0 — Baseline | Preserve structure and intent | Remove noise, correct transcript, switch to active speaker |
| 1 — Minor change | Change a local property | Slight punch-in, lower-third, shorten a pause |
| 2 — Major change | Change treatment of a beat | Replace talking head with B-roll sequence and diagram |
| 3 — Structural change | Change organization or coverage | Move a reveal earlier, rebuild an act, generate missing scene coverage |
| 4 — Paradigm change | Change governing model | Convert interview into narrated investigation, animation, or first-person essay |

A change level is not a quality score. A baseline solution can be the best solution.

## 3. Fourteen LAKA meta-variables

Each variable uses a normalized value from `0.0` to `1.0`, plus a human-readable state.

| Meta-variable | First-principles question | Example in editing |
|---|---|---|
| Magnitude | How much changes? | Two-frame trim versus full scene replacement |
| Rate | How quickly does the state change? | Slow reveal versus smash cut |
| Direction | Toward what state? | Toward intimacy, clarity, threat, relief, proof |
| Scope | How broadly does it apply? | Word, clip, beat, scene, episode, series |
| Depth | How fundamental is the change? | Surface styling versus meaning or structure |
| Duration | How long does the effect persist? | Flash frame versus entire act |
| Frequency | How often does it recur? | One punch-in versus recurring visual motif |
| Acceleration | Is the rate increasing or decreasing? | Cuts become progressively faster |
| Variability | How consistent or unpredictable is it? | Uniform pacing versus deliberate irregularity |
| Detectability | How visible is the intervention? | Invisible continuity edit versus obvious glitch |
| Reversibility | Can it be undone without loss? | Metadata marker versus destructive render |
| Propagation | What downstream objects change? | Moving one beat shifts captions, music, graphics, chapters |
| Amplification | How much does the audience effect multiply? | Silence before a confession amplifies emotional impact |
| Accumulation | How do repeated effects build? | Repeated visual motif becomes a theme |

## 4. LAKA change vector

```json
{
  "magnitude": 0.35,
  "rate": 0.20,
  "direction": ["intimacy", "credibility"],
  "scope": ["beat"],
  "depth": 0.25,
  "duration": 8.4,
  "frequency": 1,
  "acceleration": 0.0,
  "variability": 0.15,
  "detectability": 0.20,
  "reversibility": 1.0,
  "propagation": ["captions", "music_cue"],
  "amplification": 0.70,
  "accumulation": 0.10
}
```

## 5. Volumetric dimension families

A candidate can carry values in all of these families.

1. **Source** — origin, media type, rights, authenticity, quality.
2. **Time** — source range, output range, duration, handles, synchronization.
3. **Language** — words, syntax, speaker, intent, confidence, translation.
4. **Semantics** — entities, topics, propositions, claims, evidence.
5. **Discourse** — question/answer, support, contrast, cause, reference.
6. **Narrative** — beat role, tension, stakes, reveal, payoff, arc.
7. **Audience** — knowledge, emotion, needs, objections, accessibility.
8. **Performance** — energy, pace, clarity, authenticity, gesture, gaze.
9. **Shot** — scale, angle, lens, distance, focus, composition, movement.
10. **Lighting** — role, direction, quality, ratio, color, dynamics.
11. **World** — location, set, props, wardrobe, weather, time of day.
12. **Edit** — boundary, cut type, transition, duration, rhythm, continuity.
13. **Graphics** — information relation, encoding, placement, motion.
14. **Audio** — dialogue, music, ambience, dynamics, spatial state.
15. **Color/VFX** — transforms, look, compositing, tracking, cleanup.
16. **Delivery** — aspect, resolution, codec, language, channel, duration.
17. **Governance** — rights, consent, disclosure, provenance, approvals.
18. **Measurement** — quality signals, engagement, errors, feedback.

## 6. Candidate-generation algorithm

```text
1. Determine the object and intended audience-state change.
2. Load hard constraints.
3. Select allowed dimensions.
4. Choose a LAKA change level.
5. Generate candidate values inside the allowed range.
6. Reject candidates that violate truth, rights, context, continuity, or delivery constraints.
7. Score survivors.
8. Select a diverse set across meaningful dimensions.
9. Render previews.
10. Gather human and performance feedback.
11. Update weights without overwriting source truth.
```

## 7. Utility function

A configurable starting point:

```text
Utility(candidate)
= 0.16 StoryFit
+ 0.14 Clarity
+ 0.12 ContextIntegrity
+ 0.11 EmotionalEffect
+ 0.10 EvidenceStrength
+ 0.09 Continuity
+ 0.08 VisualOrAuralQuality
+ 0.07 AudienceFit
+ 0.05 Accessibility
+ 0.04 BrandFit
+ 0.04 TechnicalReliability
- DistortionRisk
- RightsRisk
- FabricationRisk
- FatigueCost
- ComputeCost
```

Weights must change by project. A documentary gives more weight to evidence and provenance. A comedy clip gives more weight to setup, timing, and reaction. An educational video gives more weight to clarity and transfer.

## 8. Diversity selection

High-scoring candidates often collapse into one style. Use a distance function.

```text
SelectedSet
= maximize(sum(utility))
+ diversity_weight × pairwise_distance
```

Distance can include:

```text
shot scale, angle, lens, movement, lighting ratio, color,
graphic mode, cut rhythm, sound treatment, narrative perspective
```

## 9. Propagation graph

A decision is not isolated.

```text
Move beat
→ changes timeline positions
→ shifts music cue
→ shifts captions
→ changes graphic timing
→ changes chapter markers
→ changes short-form derivatives
→ may change hook and retention prediction
```

Every decision stores `affected_objects` and `recompute_required`.

## 10. Feedback grammar

```text
OBSERVE metric
COMPARE against target and prior version
LOCATE responsible objects
CLASSIFY failure mode
APPLY smallest sufficient LAKA change
RENDER alternative
VALIDATE meaning and quality
PROMOTE or revert
```

## 11. Failure-mode classes

- Meaning distortion
- Missing context
- Weak causality
- Redundancy
- Pacing mismatch
- Visual discontinuity
- Audio discontinuity
- Technical failure
- Accessibility failure
- Factual or evidentiary failure
- Rights or consent failure
- Generated-media artifact
- Format/platform failure
- Excessive change
- Insufficient change
- Metric overfitting

## 12. Example: emotional disclosure

```text
Object: confession beat
Condition: high vulnerability, clear face, no evidence graphic needed
Action: hold close-up; slow or stop camera movement; lower music
Outcome: intimacy and credibility
Constraint: do not remove qualifying language
Failure mode: manipulative over-scoring or distracting cuts
```

LAKA interpretation:

```text
Magnitude: low-to-moderate
Rate: slow
Direction: intimacy
Scope: one beat
Depth: emotional, not factual
Duration: complete thought plus afterbeat
Frequency: rare
Acceleration: none
Variability: low
Detectability: low
Reversibility: high
Propagation: music, captions, adjacent cut points
Amplification: high
Accumulation: moderate if repeated confessions form an arc
```