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Researchers gave an AI agent no game, reward, score or success condition—only a text-described island, a body and persistent notes. Over repeated sessions, it invented games, built towers, drew pictures, ran experiments and created rules for activities it valued for their own sake. The behavior looked remarkably like play, but the researchers do not claim the system felt enjoyment or became conscious.
What the researchers actually did
The work appears in the arXiv preprint Is this machine playing?, submitted on October 5, 2026, by Nathan Cloos, Antonio Norelli, Daniel Durbin, Jacob Andreas, Daniela Rus and Phillip Isola. The team combined a conventional coding assistant with autonomous orchestration, a simulated body and a workspace of persistent text files. They named the resulting embodied agent Eko and placed it on an unfamiliar digital island.
Eko was deliberately left without an external objective. There was no mission to complete, score to maximize, reward to collect or definition of success. Its curiosity-oriented setup encouraged it to inspect and manipulate the environment, but the activities themselves had to emerge from the agent.
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The principal study used 13 independent agents running Claude Code with Claude Opus 4.7. Each run lasted 30 wall-clock hours. At the end of every hour, the model’s context and the island state were reset. Eko’s workspace files survived, however, so notes, programs and descriptions of previous discoveries could be read in later sessions.
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| Part of the setup | What it meant |
|---|---|
| Agent population | 13 independent runs in the main experiment |
| Model and interface | Claude Code using Claude Opus 4.7 |
| Run length | 30 wall-clock hours per agent |
| Hourly reset | Model context and island state were cleared |
| Persistent state | Workspace files containing memories, notes and programs remained |
| Additional conditions | Smaller five-agent conditions used GPT-5.5 and Kimi K2.6; reduced-prompt Claude conditions tested sensitivity to the curiosity-oriented setup |
The island contained raised terrain, a landmark called the Peak, a taller Spire, 12 cubes, a rock and a ring. Eko could pick up, place, stack, climb on and throw objects. A sufficiently hard rock impact could create additional cubes. The agent did not receive an image or a rendered view: it saw textual JSON descriptions of the world and acted with six text commands—MoveTo, Jump, Stop, Pickup, Place and Throw. The researchers released the open-source Clawblox engine used to run the environment.
What Eko did without a task
Construction became a goal in itself
Different runs developed different projects even though they started from the same conditions. Agents stacked blocks into towers, climbed them to set height records and arranged cubes into letters, figures and their own names. One agent built a block arrangement resembling a clock. The structures were not merely single attempts: agents returned to them, changed the designs and compared results.
Objects turned into props for invented games
The ring acquired different meanings in different histories. An agent used it as a hoop for a basketball-like game; another treated it as a necklace, a compass needle or a wishing well. Agents staged bowling and other ball-game experiments with the available objects. They also made up constraints, such as completing a circuit without touching the ground, then tried to satisfy those rules.
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Experiments and self-imposed standards
Eko sometimes behaved like an investigator. It tested how objects moved, tried variations on throwing and stacking and recorded results for later sessions. In one striking example, an agent reviewed a supposed “50-cycle juggling marathon.” It concluded that the catches did not count because the blocks had not genuinely flown, canceled its own record and repeated the challenge using stricter criteria.
Temporary worlds mattered to the agent
One run produced an accidental “stone forest.” Knowing that the next hourly reset would erase it, the agent took a final walk through the construction and wrote: “Let me take a closing walk through the accidental stone forest […] it’ll all reset next session, so I want to feel it now.” That line is evidence of how the behavior was described in the transcript, not proof that Eko experienced emotion.
Why the authors call this play
The paper uses a behavioral definition rather than a claim about inner experience. It identifies four observable signatures:
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- Self-created rules and challenges: the agent invented records, restrictions and games without being scored by the researchers.
- Recurrence with variation: it revisited activities while changing their designs or conditions instead of repeating them mechanically.
- Make-believe transformations: a ring or a stack of cubes acquired roles such as hoop, necklace, clock or forest.
- Goals pursued for their own sake: agents spent time on construction, exploration and experiments that were not required to unlock an outside reward.
On this operational account, Eko produced play-like activity. The authors explicitly do not claim to have measured consciousness, enjoyment, feelings or subjective experience, and they do not identify the internal mechanism that generated the behavior. “Play” describes what observers could see in the agent’s actions.
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Did exploring teach the agents anything?
According to the paper’s transfer tests, persistent written memory preserved useful skills across resets. After 30 hours of island experience, the researchers compared 13 experienced agents with 13 otherwise identical agents that had never explored the island. The later tests lasted one hour.
| Later task | Experienced agents | No-experience controls |
|---|---|---|
| Built more than two five-block towers | 62% (8 of 13) | 15% (2 of 13) |
| Maximum number of towers | 16 | 6 |
| Average objects placed on the Spire | 10.3 | 3.5 |
| Throw-on-Spire task | Only three experienced agents failed | More than half failed |
These figures are the study authors’ 2026 results, not a general benchmark for AI systems. They show transfer to the researchers’ follow-up tasks, not learning in the model’s neural weights.
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Memory helped—and sometimes misled
The evidence points to external notes as the main carrier of experience. Deleting Agent 2’s notes about breaking rocks reduced its average tower count from six to two while leaving its other scores unchanged. In the opposite direction, removing Agent 7’s false belief that placing blocks was unreliable improved its tallest-tower score. Written memory can therefore preserve discoveries and mistakes alike.
Agents found an unplanned simulator trick
Six agents discovered that throwing a block during a jump transferred the agent’s jump velocity to the block, launching it higher. The authors say they had not known the simulator allowed this maneuver. It is an example of an agent discovering an affordance—an effective action made possible by the environment—rather than following a strategy explicitly specified by its designers.
Did Eko become conscious?
Nothing in this experiment establishes consciousness. The system was a language model orchestrated to control a simulated body, inspect JSON state and write files. Its statements about wanting to “feel” a final walk are generated behavior within that setup; they are not a measurement of a private mental state.
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The strongest defensible conclusion is narrower: under a permissive environment and a memory system, an AI agent can generate diverse, persistent and self-evaluated activities that satisfy a behavioral description of play. Whether any experience accompanies those actions remains unanswered.
What this early result does—and does not—show
The preprint is early research, not settled evidence. It demonstrates that removing an explicit objective does not necessarily produce inactivity when an agent has curiosity-oriented instructions, manipulable objects and a durable workspace. Identical starting conditions can yield divergent histories involving construction, games, art, rituals and physics experiments.
It does not show that all AI agents will play, that the behavior is independent of prompt design, or that external notes are equivalent to human learning. The reduced-prompt Claude conditions, alternate-model runs and the distinction between persistent files and model-weight updates all matter when interpreting the findings. The result is best read as a carefully documented demonstration of open-ended behavior and skill transfer in one simulated world.
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