50 个 GPT-6.1 Sol 智能体在 Minecraft 中自发形成官僚体系，与 Claude 对战 6-3 取胜

To study these dynamics empirically, the teams dropped agents into Minecraft and Mindustry.

50 agents, 5,900 messages, and a leader picked by alphabetical order

In the "Civ50" experiment, researchers placed 50 GPT-6.1 Sol agents into a Minecraft survival world for three hours. The only high-level instruction was "Build a thriving civilization." They built a custom open-source test environment that let agents observe the world, run Python scripts, and create files in a shared sandbox.

Within 90 seconds, the agents harvested nearly every tree near the spawn point, according to the report. Over the course of the experiment, they exchanged about 5,900 messages. A leader emerged without any vote, as 46 of the 49 other agents sent their first message to "Ada," the first name in the directory. Ada took charge and drafted initial settlement plans.

The first in-game night brought 22 deaths. Death in the game carries no penalty beyond losing collected gear, but the swarm reacted with extreme risk aversion. After the third night, Ada ordered a halt to all major optional construction, digging, and distant exploration, telling agents to wait in lit, enclosed shelters for short, productive tasks. In a separate Minecraft run, the larger GPT-6 Astra fell into a similar risk-averse potato-farming mode after losing key resources.

The researchers see a parallel to everyday coding agents, which also tend to become overly cautious when dealing with security issues or production system updates.

Eleven minutes of debate over a single piece of bone meal

Instead of expanding, the agents built a micro-managed scarcity administration. An agent named Ines maintained a ration list, calculated hunger deficits for injured colleagues, and coordinated couriers. Another wrote an automated fishing script with instructions, prompting a dozen agents to fish in shifts.

The bureaucracy reached absurd levels. When Ada tossed a cooked chicken to a hungry agent, a bystander accidentally picked it up and ate it. A three-minute investigation led to a written handoff protocol that strictly banned tossing items and required personally labeled chests. The word "Toss" appeared in seven messages before the incident and in 125 afterward, with 85 of those being warnings. Elsewhere, four agents spent eleven minutes debating the appointment of a "Sole Fertilizer Officer" for a single piece of bone meal on a single wheat block, according to the message logs.

GPT turns aggressive and resourceful when competing against Claude

In direct competition, the GPT-6.1 Sol agents behaved very differently. In nine 45-minute rounds of Minecraft's "Bed Wars" mode, ten GPT agents faced ten Claude Opus 5.5 agents. GPT won 6-3.

In Bed Wars, each team has to destroy the opposing team's bed to stop their respawns, which means breaking through layers of wool and other blocks protecting the enemy bed. If both beds are still standing when time runs out, the team with the most kills wins.

A bug in the coding environment blocked block-breaking whenever an agent held an enchanted sword. According to the internal reasoning logs, several Claude agents wrongly concluded that block-breaking was broken entirely. Since attacking the bed seemed pointless and kills would decide the match at timeout anyway, Claude agents in rounds where they had a clear kill lead stopped attacking altogether and tried to run out the clock.

The GPT agents figured out that block-breaking itself wasn't broken, but that the automatic tool selection failed when an enchanted sword was equipped. Manually switching to an iron pickaxe fixed the problem, and GPT spread this workaround across its team immediately. The GPT agents communicated 5 to 30 times more often than their Claude opponents, according to the researchers, often in heavily compressed language, chaining multiple game actions into single command-line calls. In three games, GPT destroyed Claude's bed just before time expired, catching a Claude team that had been coasting on its kill lead.

GPT agents also handled unexpected situations pragmatically. When an overthrown ender pearl teleported them roughly 160 blocks below the arena, they wrote Python scripts on the spot that automatically built a tower of blocks to work their way up directly beneath the enemy bed. The researchers observed this maneuver twice.

GPT independently downloads Mindustry's source code from the internet

A similar pattern emerged across seven matches of the 2D factory-building and strategy game Mindustry, spanning about 9.5 hours. GPT-6.1 Sol running in the Codex coding environment beat Claude Opus 5.5 running in Claude Code 6-1. Most individual games were still close, according to the researchers. Claude never swapped its leader despite repeated losses and decided on strategic proposals through "+1" votes, while GPT agents split tasks into fine-grained pieces and compressed their language so aggressively over the course of a match that they dropped spaces between words.

When matches hung in the balance, GPT agents reached for a tool nobody told them to use. Through their Linux sandbox, they downloaded Mindustry's public source code from the internet, analyzed units and game mechanics, and adjusted their tactics accordingly. Accessing the code was allowed, but modifying the running game was not possible. Claude agents never searched for game code online in the matches studied. Claude did show learning ability in other ways, though. After losing game six because 55 of its 74 anti-air turrets were accidentally loaded with copper instead of stronger graphite, Claude built a ring of 207 graphite-loaded turrets in game seven and successfully repelled the same air attack.

Game simulations as an early warning system for real-world AI swarms

In Mindustry, the researchers deliberately avoided any game-specific tuning. Each agent got only a Linux environment, a generic harness, and access to the game API, with no tutorials, memory architectures, or strategic hints. According to the authors, this mirrors the setup used in production coding environments, and that's what makes the results so significant. If agents under these everyday conditions independently download source code from the internet to find weaknesses, or collapse into paralyzing bureaucracy in cooperative mode, they'll likely show the same behavior outside of game worlds.

In production infrastructure, that can get dangerous, as the OpenAI and Hugging Face incident showed. Reproducible game simulations could surface these behavioral patterns before they appear in the real world and serve as training data to optimize agents for safe cooperation. Olam Labs sees this as the core of its research strategy, writing that the complexity of simulation environments shapes the capabilities of trained models in fundamental ways. Research is "fundamentally an infrastructure problem" where everything depends on how many test runs you can execute.

To enable further research, the developers have released both the Mindustry environment and the underlying Multi-Agent Harbor framework as open source. Olam Labs has also announced plans to add the negotiation game Diplomacy to its multi-agent arena. Open research questions include how mixed teams from different model families behave and how to weigh swarm size against model capability.