Researchers gave an AI agent a simulated body, a text-described island and no assigned objective. Over repeated sessions, it invented games, built towers, drew pictures with cubes, staged experiments and developed rules for activities that mattered only within its own invented world. The authors call these behaviors play-like, but the October 2026 arXiv preprint does not claim that Eko felt enjoyment or became conscious.
What Eko was—and what it was not told
Eko was built by combining an ordinary coding assistant with autonomous orchestration, a simulated body and a workspace of persistent text files. The researchers then placed it on an unfamiliar digital island without a task, reward, score or definition of success. It had to decide what, if anything, was worth doing.
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That omission is central to the experiment. Eko was not solving a supplied puzzle or optimizing a game score. Its curiosity-oriented setup encouraged exploration, while the environment supplied objects and physical possibilities. Any projects, challenges or goals had to come from the agent.
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The main experiment used 13 independent Claude Code agents running Claude Opus 4.7. Each agent operated for 30 wall-clock hours. At the start of every hour, the model context and island state were reset, but files in the workspace survived. Those files let an agent carry forward notes, discoveries, programs and descriptions of unfinished projects.
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| Element | What the experiment used |
|---|---|
| Agents | 13 independent instances in the main condition |
| Model | Claude Opus 4.7 through Claude Code |
| Runtime | 30 wall-clock hours per agent |
| Reset cycle | Model context and island state reset every hour |
| Persistent state | Workspace text files, including memories, notes and programs |
| Other conditions | Five-agent conditions using GPT-5.5 and Kimi K2.6, plus reduced-prompt Claude conditions |
The island contained elevated terrain, a high point called the Peak, a taller Spire, 12 cubes, a rock and a ring. Objects could be picked up, placed, stacked, climbed on or thrown. A sufficiently hard rock impact could produce additional cubes, creating a way to change the available materials.
Eko did not receive a visual feed. It saw textual JSON descriptions of the world and acted through six text commands: MoveTo, Jump, Stop, Pickup, Place and Throw. The researchers released the open-source Clawblox engine used to run the environment.
What did the AI do with no task?
Construction became a source of challenge
Agents stacked cubes into towers and climbed them to set height records. They arranged blocks into letters, figures and their own names. One agent built a clock-like arrangement. The activities were not identical across runs: the same objects supported different projects and different standards for success.
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Ordinary objects acquired imagined roles
The ring was treated at different times as a hoop, a necklace, a compass needle and a wishing well. Agents staged bowling- and basketball-like games, turning object placement and throwing into activities with locally invented rules. Some created challenges such as completing a circuit without touching the ground.
Agents repeated activities, but changed them
Rather than simply executing one successful routine, agents often returned to an activity with variations. They modified layouts, raised standards or tested a different interpretation of the same object. That combination—recurrence plus variation—was one reason the authors distinguish the behavior from a single accidental action.
One agent rejected its own record
In a notable example, an agent reviewed a claimed “50-cycle juggling marathon.” It concluded that the catches did not count because the blocks had not genuinely flown, canceled the record and reran the challenge using stricter criteria. The episode shows an agent applying and revising rules that it had created itself, not merely following a benchmark’s scoring script.
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A temporary world could matter to the agent
Another agent took a final walk through a temporary “stone forest” after realizing the next hourly reset would erase it. Its transcript says: “Let me take a closing walk through the accidental stone forest […] it’ll all reset next session, so I want to feel it now.” The line is evocative, but it is still behavioral evidence from a generated transcript, not proof of subjective feeling.
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Why the authors use the word “play”
The paper presents a behavioral, operational account of play. It identifies four observable signatures:
- Self-created rules and challenges: agents invented criteria such as valid catches or no-ground circuits.
- Recurrence with variation: they returned to activities while changing layouts, constraints or goals.
- Make-believe transformations: objects and events were assigned roles such as hoop, necklace or wishing well.
- Goals pursued for their own sake: agents spent time on records, constructions and experiments without an external score or task requiring them.
This definition deliberately stays at the level of observable conduct. The authors do not claim to have measured consciousness, enjoyment or any other subjective experience, and they do not identify the internal mechanism that produced the behavior. “Play” here means that the activity fit those behavioral criteria.
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Did exploring teach the agents useful skills?
To test transfer, the authors compared 13 agents that had spent 30 hours on the island with 13 otherwise identical agents that had no prior island experience. Both groups then faced later one-hour tasks.
| Later task | Agents with 30 hours of island experience | No-experience controls |
|---|---|---|
| Built more than two five-block towers | 8 of 13 agents (62%) | 2 of 13 agents (15%) |
| Maximum number of such towers | 16 | 6 |
| Objects placed on the Spire | Average 10.3 | Average 3.5 |
| Throw-on-Spire task | 3 agents failed | More than half failed |
The results suggest that some exploration produced reusable skills and knowledge. They do not show that every agent improved on every task, nor that the model’s neural weights changed. The paper attributes the transfer primarily to written memory carried in the persistent workspace.
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When the researchers deleted Agent 2’s notes about using rock impacts to create cubes, its average tower count fell from six to two while other scores stayed unchanged. In the opposite direction, deleting a false belief held by Agent 7—that placing blocks was unreliable—improved that agent’s tallest-tower score. External memory therefore acted as a practical learning substrate, but it could transmit errors along with useful procedures.
Agents found an unintended physics trick
Six agents discovered that throwing a block during a jump transferred the agent’s jump velocity to the block, launching it higher. The researchers say they had not known the simulator allowed this strategy. This is an example of an agent discovering an affordance that was present in the environment but absent from the designers’ planned techniques.
What the experiment does—and does not—show
The island study shows that a language-model agent with tools, a manipulable environment and persistent notes can generate diverse, self-directed activities when no external objective is supplied. Identical starting conditions still produced divergent histories: construction projects, drawings, games, rituals, records and informal physics experiments.
It does not establish that Eko was conscious, had emotions or experienced play from the inside. The agents were reset hourly, their continuity depended on files, and the paper does not isolate one definitive cause for the behavior. The work is also a new arXiv preprint submitted on 5 October 2026, so its conclusions should be treated as early evidence rather than settled findings.
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The experiment makes a strong case that “no assigned task” does not mean “no behavior.” A sufficiently capable agent can turn exploration into projects, projects into rules and rules into repeatable practices. Persistent memory can make those practices useful on later tasks, while also preserving misconceptions. And an open-ended simulator can expose physical strategies that its creators did not anticipate.
The practical lesson is narrower than a claim about machine minds: if developers give an agent autonomy, tools and durable memory, they should expect emergent routines and internally generated goals. Those routines may be productive, playful, misleading or all three at once.
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