EVIDENCE / THE WORKFLOW

A question.
A run. A revision.

Follow a question through an experiment, using the wall to notice behaviour and the run record to test an explanation.

The purpose is discovery through observation. Read how the human observer, AI optimiser and agents fit together.

THE CENTRAL IDEA

The models evolve.
We notice.

The real innovation is an enduring evolutionary experiment. Frontier models design and assess the experiments, adopting, revising and replacing generations of node firmware to find out how they perform. They can be insightful or mistaken, smart or surprisingly dumb. Their work is judged by the comparisons they leave behind.

A long-running coordinator such as Meta Muse Wonder can organise a team of node-specific Muse Spark agents. With a connected development and deployment host, the intended loop can keep running around the clock: contemplate an improvement, write it, compile it, flash a candidate, observe the result and decide what to retain or change.

The human is primarily the observer. We watch and listen for amusement and for the intuition that something unusual is happening: a rhythm, a hesitation, a collective response that does not fit our understanding of normal. That observation can become a question for the model team. Manual controls let us inspect, reproduce or intervene without making us the author of every experiment.

Give evolution something to contend with.

Kulira introduces competing or hostile behaviour—noise, deception, interference and unreliable peers. We can also change the world around the flock: temperature, movement, sound or proximity sensed through suitable lidar, infrared or mmWave hardware. Each added sensor needs its own supported hardware and firmware path; these are possible experimental inputs, not a claim that every node already measures them.

Keep the objective, baseline, firmware version and observations with each generation. A model’s confident assessment can be wrong. Preserve failed candidates and surprising outcomes so a rolling experiment remains something we can investigate, rather than a sequence of attractive animations.

Inspect the controller and manual controls → · Meet Kulira →

META MODELS / FIRMWARE / THE PHYSICAL FLOCK

  1. Meta model team

    A long-running Muse Wonder coordinator assigns node-specific work to Muse Spark agents: design experiments, write code, assess results and propose the next generation.

  2. Build & review

    Turn a selected proposal into versioned firmware or a policy, with checks against the declared objective.

  3. Deploy a candidate

    Use the documented commissioning host for USB installation or supported application OTA, then verify the node.

  4. Run & collect

    The Korvo coordinates the world and run. Nodes, controller records and observer captures supply evidence.

  5. Compare & revise

    Evaluate the objective, inspect failures and feed findings into the next AI-designed generation.

↶ Evidence returns to the model team. Human observations can introduce a new question. Repeat with a recorded change and a new comparison.

Conceptual development workflow. The deployment host and the Korvo world controller are separate roles. Model generation can vary; the resulting experiment logic can be deterministic. Continuous operation depends on the configured development host, deployment tools and recorded checks; this diagram is not an uptime or improvement result.
  1. 01

    Define the comparison.

    Choose a falsifiable question, a baseline and a scoring rule. Freeze the conditions before examining the outcome.

  2. 02

    Record the starting point.

    Keep the code revision, seed, policy and relevant configuration with the run. For hardware tests, include the build and device acceptance state.

  3. 03

    Run, watch and listen.

    Collect decisions and observations. Watch for synchrony, outliers and changes of rhythm; listen when the experiment has a verified audio mapping. Mark interesting moments and retain their relationship to the underlying run.

  4. 04

    Score against the rule.

    Compare the recorded output to the declared criteria. Keep a failed check separate from a condition that was never measured.

  5. 05

    Review the explanation.

    Use observations to propose explanations, then inspect alternatives, information differences and possible leakage. AI can help propose strategy changes; test those changes against the objective rather than treating a compelling pattern as proof.

  6. 06

    Publish, then revise.

    Link the evidence and limitations. Change one understood part of the setup, record the new version and repeat the comparison.

TRY IT

Start with a reproducible result.

The public reproduction script reruns the flagship benchmark offline and compares its canonical scientific content with the checked-in expectation.

Inspect the reproduction script ↗
CONTINUE READINGReproduce the benchmark locally