> ## Documentation Index
> Fetch the complete documentation index at: https://avala.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Capability experiments

> The record that links a model failure to a hypothesis, the data collected for it and the measured outcome

A capability experiment is one turn of the [fleet learning loop](/docs/fleet-learning/overview.md), written down. It starts from a failure your model shows on a specific model version. It states a hypothesis about which data will fix it. It ends with a measured outcome and a decision about the next allocation.

Avala opens a capability experiment for each failure condition in a [gap report](/docs/fleet-learning/failure-ingestion.md#the-gap-report). Avala keeps the record current through collection, release and re-evaluation. You receive it with each [capability report](/docs/fleet-learning/evaluation.md#the-capability-report).

## Fields

| Field | What it holds |
| - | - |
| `model_version` | The model version that showed the failure. You supply this identifier. |
| `failure_condition` | The failure and the conditions where it appears, taken from the failure taxonomy. `mistake_type` and `failure_stage` use the same tags as the [outcome labels](/docs/fleet-learning/sequence-metadata.md) of the failing sequences. |
| `hypothesis` | A testable statement: which data should change which metric, and in which direction. |
| `coverage_cells` | The [coverage cells](/docs/fleet-learning/coverage.md#coverage-cells) the experiment targets. |
| `capture_campaign` | The capture campaign that collects the data. |
| `dataset_release` | The versioned dataset release that carries the new sequences. |
| `evaluation_cohort` | The held-out [evaluation scenes](/docs/fleet-learning/evaluation.md#held-out-evaluation-scenes) used to measure the outcome. Their sequences carry `evaluation_membership: "held_out_eval"`. |
| `outcome` | The measured result on the evaluation cohort for each model version, with confidence intervals. |
| `next_allocation` | What the next cycle collects, and why. |

## Example record

```json theme={null}
{
  "experiment_id": "cx_dishwasher_rack_regrasp",
  "status": "validating",
  "model_version": "your-policy@2026-09-20",
  "failure_condition": {
    "mistake_type": "slip_during_place",
    "failure_stage": "place",
    "taxonomy_version": "3",
    "conditions": ["lower_rack_pulled_out", "wet_ceramic", "rack_obstacle_present"]
  },
  "hypothesis": "Adding expert sequences with slip and recovery on lower dishwasher racks raises success on the rack-loading cohort and lowers the intervention rate.",
  "coverage_cells": [
    {
      "environment": "kitchen",
      "mechanism": "dishwasher_pull_rack",
      "material": "ceramic_wet",
      "interaction_primitive": "place",
      "failure_mode": "regrasp_after_slip",
      "embodiment": "human_egocentric"
    }
  ],
  "capture_campaign": { "project_uid": "<project-uid>" },
  "dataset_release": { "dataset": "your-org/kitchen-manipulation", "release": "r4" },
  "evaluation_cohort": { "cohort_id": "eval_kitchen_racks", "version": "2" },
  "outcome": null,
  "next_allocation": null
}
```

`outcome` and `next_allocation` stay empty until Avala re-evaluates the next model version.

## How the record links to existing objects

A capability experiment holds references. Each field points at an object you can already open in Mission Control, the API, the SDKs and the MCP server.

| Experiment field | Existing object | Where to read it |
| - | - | - |
| `failure_condition` | [Fleet recordings](/docs/visualization/fleet/fleet-dashboard.md) and [fleet events](/docs/visualization/fleet/events-and-markers.md) that show the failure, and the outcome labels of the failing sequences | `client.fleet.recordings.get(...)`, `client.fleet.events.list(recording_id=...)`, `client.sequence_outcomes.list(owner, slug, outcome=...)`, MCP `fleet_get_recording`, `fleet_list_events`, `list_sequence_outcomes` |
| `capture_campaign` | A [project](/docs/getting-started/concepts.md#projects) that runs the capture campaign | `client.projects.get(uid)`, MCP `list_capture_campaigns`, `get_campaign_acceptance_summary` |
| `coverage_cells` | Acceptance coverage on the campaign project | MCP `get_campaign_acceptance_coverage` |
| `dataset_release` | A [dataset](/docs/getting-started/concepts.md#datasets) and its [slices](/docs/api-reference/slices.md) | `client.datasets.get_by_slug(owner, slug)`, `client.slices.get(owner, slug)`, MCP `get_dataset`, `get_slice` |
| `evaluation_cohort` | A slice of held-out evaluation scenes, kept out of every training release | `client.slices.get(owner, slug)`, `client.sequence_outcomes.list(owner, slug, evaluation_membership="held_out_eval")`, MCP `get_slice`, `list_sequence_outcomes` |

The failure-and-recovery lane and each evaluation cohort are delivered as slices. You can list their items with `client.slices.list_items(owner, slug)`.

## Lifecycle

| Status | Meaning |
| - | - |
| `proposed` | Avala drafted the hypothesis and the target coverage cells from the gap report. |
| `approved` | You approved the brief. The capture campaign is open. |
| `collecting` | Sequences are being captured, reviewed and accepted. |
| `released` | The dataset release is published. You are training on it. |
| `validating` | Avala is scoring the new model version on the evaluation cohort. |
| `closed` | The outcome is recorded and the next allocation is set. |

A closed experiment stays in the record. The next experiment on the same failure condition links back to it, so you can follow a capability across model versions.