---
title: "math-qworld-learned — Mova code model"
description: "Mova code model of math/src/qworld.rs: components with their labels, obernants and the model text."
url: "https://groupants.elamur.ai/free/en/mova/math/src/qworld.rs.groupant/learned.groupant/"
---
Elamur: [Mova](https://md.mova.elamur.ai/free/en/) · [Dictionary](https://md.dictionary.elamur.ai/free/en/) · [Research](https://md.research.elamur.ai/free/en/) · **Groupants**

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# math-qworld-learned

The groupant beside math/src/qworld.rs in the Mova repository: what the code computes, what it never does, how to go back from its outputs, its invariants, cost and checks.

## Facts

- **kind:** groupant_fragment
- **status:** partial source bound model
- **source:** [`math/src/qworld.rs`](https://github.com/ElamurAI/mova-free/blob/main/math/src/qworld.rs)
- **source SHA-256:** `6225ef705c842a95…`
- **covers:** script_labels visits numeric script occurrences and binds each to the first unused equal exact nonconstant quantity. WorldModel predicts argmax over ['=','+','-','r']; learned cells join the highest-scoring prior pair only if score>0. (bytes 7370–14655)
- **part of:** [math-qworld](https://md.groupants.elamur.ai/free/en/mova/math/src/qworld.rs.groupant/)
- **repository folder:** [`math/src/qworld.rs.groupant/learned.groupant`](https://github.com/ElamurAI/mova-free/tree/main/math/src/qworld.rs.groupant/learned.groupant)
- **repository commit:** `48f2e6f74146`

> **Implementation material.** A Mova code model describes this code at one source revision: partial models, finite checks and their stated scope. It is not a reviewed general exposition, and its labels are shown as written, never raised.

## Components

| component | label | where |
|---|---|---|
| **approximant** | partial | [approximant](#approximant) |
| **ast** | partial | `../syntax.json` |
| **descriptant** | present | [descriptant](#descriptant) |
| **invariant** | partial | [invariant](#invariant) |
| **tests** | executed bounded checks | [tests](#tests) |

## Obernants

The typed relations around the forward model: the behaviour complement (what the code never does), the reverse reconstruction (every input compatible with an output) and specialised views.

| id | type | label | where |
|---|---|---|---|
| `behavior_complement` | behavior_complement | partial source derived model | [behavior_complement](#behavior-complement) |
| `reverse_reconstruction` | reverse_reconstruction | partial source derived model | [reverse_reconstruction](#reverse-reconstruction) |
| `zero_weight_boundary` | zero_weight_boundary | partial source derived model | [zero_weight_boundary](#zero-weight-boundary) |
| `greedy_cluster_history` | greedy_cluster_history | partial source derived model | [greedy_cluster_history](#greedy-cluster-history) |

## Model text


### descriptant

#### math-qworld-learned

script_labels visits numeric script occurrences and binds each to the first unused equal exact nonconstant quantity. WorldModel predicts argmax over ['=','+','-','r']; learned cells join the highest-scoring prior pair only if score>0.

Epoch 2026-10-31: replaced duplicate catalog sentences with concrete exclusions, compatible-antecedent examples and two distinct scoped behavioral views. Source and prior execution evidence are unchanged; this revision ran no tests.

### approximant

#### Scoped forward relation

script_labels visits numeric script occurrences and binds each to the first unused equal exact nonconstant quantity. WorldModel predicts argmax over ['=','+','-','r']; learned cells join the highest-scoring prior pair only if score>0.

This source-derived relation is partial; no executable percentage surrogate or replacement is supplied.

### invariant

#### Scoped domain and invariants

Labels must align with qs; floating weights/scores must permit partial_cmp. Training uses10 passes and does not guarantee separation of conflicting labels.

##### Cost

Training scans item quantities and all i<j pairs for10 epochs; learned write compares each item with all earlier ones. Feature string/hash and exact cell work add to O(N²) pair enumeration.

### models

### behavior_complement

No source quantity occurrence can be labelled twice by script_labels. A pair with score0 cannot join a prior cell. At all-zero operation weights, a non-rate prediction is excluded because max_by keeps the last tied r.

### reverse_reconstruction

Two script lines assigning the same number to different slots can match duplicate-valued quantities in order; swapping otherwise equivalent lines changes occurrence labels. Many scripts differing in ignored lines produce the same labels. Distinct feature-weight maps can share all selected op/pair scores.

### zero_weight_boundary

Default WorldModel chooses r for every operation; write_learned then emits commands but leaves state empty. Pair score0 creates a new cluster instead of joining.

### greedy_cluster_history

For item i, choose the last maximal positive prior-pair score and reuse that prior item's assigned slot; cluster names come from the first item that created a slot, not the newest item's description.

### tests

#### Scoped evidence

Greedy script duplicate binding and default learned rate/no-cell dispatch; no training.

Named actual source fixtures: qworld::wave28::{exact_world_overwrites_and_labels_duplicates}.

Parent tests.md records commands and paired profiles. No additional coverage inferred from parent fixtures outside this byte range; source-scoped numeric/parse properties remain partial.