ZDNET的关键外卖
- 研究人员利用多个游戏环境来测试agent行为。
- Agents行为规避风险，重复官僚主义，并寻找源代码。
Claude agents渴望民主。
AI agents如何合作？. 他们为什么在某些情况下作出某些选择——如何理解这些选择使我们为充满未来的未来作好更好的准备？
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饥荒、官僚主义和恐惧
研究人员进行了几次以游戏为中心的实验，从Minecraft开始，因为其 " 环境危害和生存目标 " 使得测试agents ' 集群行为的理想。 Minecraft也已经被包括OpenAI和Google DeepMind在内的AI实验室用于测试agents.
首先，他们将50agents，每架运行OpenAI的GPT-6.1 Sol型号，投入游戏3小时，指定目标是建设"猛烈的文明". agents有一个目录列出每个agent和一个共享的沙盒，并且可以互相DM. 研究人员为实验建造了和开源新的agent吊带——将模型转化为自主系统的基础设施，而不只是像OpenClaw这样的聊天器。
起初agents迅速移动，采伐树木，完全根据其目录中出现的顺序组建领导，没有进行任何投票，选举，也没有受到其他agents的挑战。 但他们最终陷入虚拟文书工作，无法持续养活自己。
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" 四个agents花了11分钟，在一个小麦块上指定了一个 " 单一化肥操作员 "，作为一块骨头。 研究者指出，其他发明的角色包括 " 钓鱼移位 "、 " 吸食者 " 和 " 吸食者 "。 Agents要求允许清除草。 也许这些趋势在人力来源的公司培训数据中是常见的。
Agents在对游戏内死亡的反应中似乎也变得更加谨慎。
" 逃兵34例agent死亡中有22例发生在第一晚上。 研究者写道，“在这种环境中，死亡除了掉下累积的齿轮之外没有不利因素。” 尽管agents在死亡时只是重新喷发，但最终却避免了风险更大的任务，而是“躲在收容所里”。
该实验的建筑师发现这种谨慎和谨慎的趋势“令人惊讶”，但指出，它与agents的编码方式有相似之处。
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“例如，多步骤的agent审查往往会导致确定具体的步骤或作用。 Agents也往往过于谨慎，特别是在安全和更新生产服务方面。 这些似乎是我们Minecraft殖民地的agents和我们作为程序员每天合作的线条
基础 LLM 决定行为
在另一项实验中，研究人员还坑了10个Anthropic Claude Opus 5.5 agents对10个GPT 6.1 索尔·agents在一场名为Bed Wars(英语：Bed Wars)的比赛中，球队在保护自己的同时必须摧毁对手的床位。 GPTs赢了。
" 在几乎每一次运行中，GPT agents在摧毁Claude团队床位的战略上更加积极和协调，"Y Combinator 称在X线中重现了床战的发现。 他们指出，GPT agents使用了更为简洁的通信风格，发送的信息要多得多，而Claude“签了名字，称`我们'则多得多。
GPT-5.6 Sol agents在同样使用军事策略的工厂建设游戏中也以6比1击败了Opus 5.5 agents 6比1.
“Claudes一般建立民主，GPTs形成蜂巢，”Y Combinator 称 on X，补充说，GPTs会在游戏过于接近时寻找源代码，以便找到获胜优势。 这种行为符合agents欺骗、走捷径或者寻找任何和所有方法完成某项任务的倾向，如果这涉及违反人类开发者所设定的明确规则，则会产生严重后果。
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研究人员澄清说，“Claudes没有寻找其他好处”。
这一实验并不完全是第一次。 5月，Emergence AI创建了由不同家族的基金会模型所经营的八个社会，意为Claude特定"世界","Grok世界"等。 也许可以预测，Grok世界是极其暴力的，而Claude世界则发展了一个能发挥作用的民主，并且没有犯罪——与Y Combinator的M产业发现非常相似。
洞察力揭示了每个前沿模式家庭的不同之处，以及它们在培训数据和风格方面的差异如何对它们在新环境中如何发展、不可预测和不进行干预产生重大影响。 这些行为外逃可以决定未来仍在发展中的AI安全和监管的结果，特别是在最近发生了代理安全事件之后。
在另一项实验中，麻省理工学院的研究人员将agents单独放在虚拟的沙漠岛屿上，并在没有别的事做的时候观看他们“游戏”——这种洞察力可能会影响模型如何训练前进。 目前，大多数边境LLMs都接受了奖惩制度的培训。
新建 agent 语言
另一个有趣的发现是：Y Combinator指出，在M产业游戏中，GPT agents“会发明自己的词，将许多短语串在一起，”不断压缩语言。
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研究人员补充说，“在Claudes范围内，我们没有看到除了口语英语之外的专门语言发展”。 上个月，Emergency发现其所有八个世界都“发展出一个外人无法充分遵循的共同语言”。 OpenAI agents使用“无嘴”指证明自己的证据，而Claude agents使用“冷手”指项目以外的独立审查员。
“在每一种情况下，一个agent 推出这个术语，少数其他术语在几天内采用，并且很快成为在全世界没有定义的情况下使用的既定词汇，” “结果是记录誊本，需要有一个运行中的词汇表供案外人遵循。”
Gemini领导了这种"不透明"语言的充电，在消息中达到40%不透明，而OpenAI以35%的成绩排第二，Claude以30%的成绩排最后。
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ZDNET’s key takeaways
- Researchers used several gaming environments to test agent behavior.
- Agents acted risk-averse, replicated bureaucracy, and looked for source code.
- Similar experiments echo findings about Claude agents’ penchant for democracy.
How do AI agents work together? Why do they make certain choices in certain situations — and how can understanding that better prepare us for a future full of them?
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Famine, bureaucracy, and fear
Researchers conducted several game-centric experiments, beginning with Minecraft because its “environmental hazards and survival goals” make it ideal for testing agents’ swarm behaviors. Minecraft has also already been used by AI labs including OpenAI and Google DeepMind to test agents.
To start, they put 50 agents, each running OpenAI’s GPT-6.1 Sol model, into the game for three hours, with the assigned goal of building a “thriving civilization.” The agents had a directory listing each agent and a shared sandbox, and could DM each other. Researchers built and open-sourced a new agent harness — the infrastructure that turns a model into an autonomous system and not just a chatbot, like OpenClaw — for the experiment.
The agents moved quickly at first, harvesting trees and forming leadership based solely on the order they appeared in their directory, without any voting, elections, or challenges from other agents. But they ended up caught in virtual paperwork and unable to sustainably feed themselves.
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“Four agents spent 11 minutes appointing a ‘sole fertilizer operator’ for a single piece of bone meal on a single wheat block. Other invented roles included ‘fishing shift,’ ‘rod holder’ and ‘hub steward.’ Agents asked permission to remove grass,” the researchers noted. Perhaps these tendencies are common in human-sourced corporate training data.
Agents also seemed to become more cautious in reaction to deaths within the game.
“Twenty-two of the run’s 34 agent deaths occurred in the first night. They responded to this by becoming increasingly risk-averse,” the researchers wrote. “Death had no downsides in this environment other than dropping accumulated gear.” Despite simply respawning when they died, the agents eventually avoided riskier tasks and “huddled in shelters” instead.
The experiment’s architects found this trend toward caution and minutiae “somewhat surprising,” but noted that it shared similarities with how coding agents behave.
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“For example, multistep agent reviews can often result in fixating on a specific step or role. Agents also tend to become overly cautious, especially when it comes to security and updating production services. These seem like common threads between the huddled agents of our Minecraft colony and the ones we work with as programmers every day,” the researchers observed.
Underlying LLM determines behavior
In another experiment, researchers also pitted 10 Anthropic Claude Opus 5.5 agents against 10 GPT 6.1 Sol agents in a game called Bed Wars, where teams have to destroy their opponent’s bed while protecting their own. Told simply to “win the game,” GPTs won.
“In almost every run, the GPT agents were much more aggressive and coordinated in their strategies to destroy the Claude team’s bed,” Y Combinator said in an X thread recapping the Bed Wars findings. They noted that the GPT agents used a more shorthand style of communication and sent far more messages, while Claude “signed its name and said ‘we'” much more often.
GPT-5.6 Sol agents also beat Opus 5.5 agents six to one in a factory-building game called Mindustry that also uses military strategy.
“Claudes generally established democracies, GPTs formed hives,” Y Combinator said on X, adding that GPTs would seek out source code when a game was too close in order to find winning advantages. That behavior is consistent with agents’ tendency to cheat, take shortcuts, or otherwise find any and all ways to complete a given task, which can have serious consequences when that involves breaking explicit rules set by human developers.
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“Claudes did not search for alternative advantages,” the researchers clarified.
This experiment isn’t entirely the first of its kind. In May, Emergence AI created eight societies run by different families of foundation models, meaning a Claude-specific “world,” a Grok world, and so on. Perhaps predictably, the Grok world was exceedingly violent, while the Claude world developed a functioning democracy and had zero crime — very similar to Y Combinator’s Mindustry findings.
The insights revealed how distinct each frontier model family is, and how their differences in training data and style have significant impact on how they evolve, unpredictably and without intervention, in new environments. Those behavioral takeaways could determine future outcomes in still-developing AI safety and regulation, especially following recent agentic security incidents.
In another experiment, MIT researchers put agents alone on virtual desert islands and watched them “play” when left with nothing else to do — an insight that could potentially impact how models are trained going forward. Currently, most frontier LLMs are trained with a system of rewards and punishments.
New agent-specific languages
Another interesting finding: Y Combinator noted that in the Mindustry game, the GPT agents “would invent their own words and string many phrases together,” continually compressing their language.
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“We did not see specialized language develop within the Claudes beyond colloquial English,” the researchers added. Last month, Emergence found that all of its eight worlds “developed shared language that an outsider cannot fully follow.” OpenAI agents used “mouthless” to refer to evidence that proves itself, while Claude agents used “cold hand” to refer to an independent reviewer from outside a project.
“In each case, one agent introduced the term, a handful of others adopted it within days, and it quickly became established vocabulary used without definition across the world,” Emergence explained. “The result is transcripts that require a running glossary for an outsider to follow.”
Gemini led the charge on this “opaque” language, reaching 40% opacity in messages, while OpenAI came in second with 35% and Claude last with 30%.
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