# TopoCoend Triage
Simon Frost

- [Setup](#setup)
- [Build the Example](#build-the-example)
- [Execute the Learned Cover](#execute-the-learned-cover)
- [Summarize the TopoCoend Output](#summarize-the-topocoend-output)
- [Reusing the Helper Directly](#reusing-the-helper-directly)

## Setup

``` julia
using FunctorFlow
```

`topocoend_block` is the categorical pattern where a system first
**infers a cover** over tokens and then applies a Kan-style aggregation
over that inferred relation. This vignette uses a small clinical-triage
toy problem: local signals such as oxygen saturation, lactate, and
presenting symptoms induce latent contexts like respiratory or metabolic
concern.

## Build the Example

``` julia
example = build_topocoend_triage_example()
diagram = example[:diagram]
println(summary(diagram))
```

    FunctorFlow.Diagram

The example wires two explicit morphisms:

1.  `infer_context_cover`: infer a latent cover from signal tags.
2.  `lift_signal_priority`: turn structured signals into scalar
    priorities.

The left-Kan stage then aggregates those lifted priorities into
context-level risk summaries.

## Execute the Learned Cover

``` julia
result = execute_topocoend_triage_example(example)
println("Learned cover: ", result.values[diagram.ports[:learned_relation].ref])
println("Context risk: ", result.values[diagram.ports[:output].ref])
```

    Learned cover: Dict{Any, Vector{Any}}(:respiratory_focus => [:oxygen_saturation, :chief_complaint, :history], :metabolic_focus => [:lactate, :glucose], :hemodynamic_focus => [:oxygen_saturation, :lactate, :blood_pressure])
    Context risk: Dict(:respiratory_focus => 0.8166666666666668, :metabolic_focus => 0.7749999999999999, :hemodynamic_focus => 0.7666666666666666)

Here the same local signal can belong to multiple inferred contexts. For
instance, oxygen saturation participates in both the respiratory and
hemodynamic covers.

## Summarize the TopoCoend Output

``` julia
summary = summarize_topocoend_triage_example(example)
println("Highest-priority context: ", summary["highest_priority_context"])
println("Context risks: ", summary["context_risks"])
println("Learned relation: ", summary["learned_relation"])
```

    Highest-priority context: respiratory_focus
    Context risks: Dict("respiratory_focus" => 0.8166666666666668, "metabolic_focus" => 0.7749999999999999, "hemodynamic_focus" => 0.7666666666666666)
    Learned relation: Dict("respiratory_focus" => ["chief_complaint", "history", "oxygen_saturation"], "metabolic_focus" => ["glucose", "lactate"], "hemodynamic_focus" => ["blood_pressure", "lactate", "oxygen_saturation"])

The point is not the toy numbers; it is the geometry. The global context
is not aggregated over a fixed adjacency. Instead, the cover itself is
inferred from typed local evidence and only then used as the indexing
relation for the Kan aggregation.

## Reusing the Helper Directly

The reusable helpers are also exported directly:

``` julia
cover = infer_topocoend_cover(example[:token_payloads];
    context_prototypes=example[:context_prototypes],
    min_overlap=1)
scores = lift_topocoend_scores(example[:token_payloads])

println("Cover via helper: ", cover)
println("Lifted scores: ", scores)
```

    Cover via helper: Dict{Any, Vector{Any}}(:respiratory_focus => [:oxygen_saturation, :chief_complaint, :history], :metabolic_focus => [:lactate, :glucose], :hemodynamic_focus => [:oxygen_saturation, :lactate, :blood_pressure])
    Lifted scores: Dict(:oxygen_saturation => 0.9, :chief_complaint => 0.95, :history => 0.6, :lactate => 0.85, :glucose => 0.7, :blood_pressure => 0.55)

This makes `TopoCoend` practical outside the block builder itself: the
same cover-inference and lifting helpers can be plugged into larger
world-model or agent pipelines.

For the broader CATAGI symbolic overview, see [18 Neurosymbolic
Pipelines](../18-neurosymbolic-pipelines/neurosymbolic-pipelines.html).
For the trainable version of this pattern, see
`build_topocoend_lux_model` and `RelationInferenceLayer` in [07 Lux
Neural Backend](../07-lux-neural-backend/lux-neural-backend.html).