Newest papers

Environment Duels: A Microcosm of LLM Co-Evolution

Shibo Jin, Zhiqiu Xu, Yiling Zou, Shangyu Gong, Naik Mayur · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.30116 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

从合成培训管道到开放的网络共享数据,边界LLMs通过代码的交互来吸收他们的前辈和竞争对手所创造的内容。 每个模型在这个生态系统中扮演着如何的角色,作为一个创造者和解决者? 我们介绍了从合成培训管道到开放的网络上共享的数据,每个模型都吸收了他们的前辈和竞争对手所创造的内容。 每个模型都如何在这个生态系统中扮演自己的角色,作为一个创造者和解决者? 在九个边界LLM中,解决和作者证明了不同的技能:在交互环境中评估两个能力的最强大的解决者不是最强大的作者,而较小的模型则从其他人身上学习更多,并写出最困难的环境和最有用的线索。 除了一个可用窗口之外,

Machine translation (Meta M2M100); the English original follows.

Abstract

Through a tangle of real-world interactions, from synthetic training pipelines to data shared on the open web, frontier LLMs absorb what their predecessors and competitors create. How well does each model play its role in this ecosystem, as a creator and as a solver? We introduce Environment Duels, a dynamic agentic benchmark that evaluates both capabilities on interactive environments. Each model writes environments, each with a privileged hint, aiming for tasks that capable solvers fail without the hint and pass with it. Across nine frontier LLMs, solving and authoring prove to be distinct skills: the strongest solvers are not the strongest authors, while smaller models learn more from how others played and write the hardest environments and the most useful hints. Beyond the scores, the pairwise interactions form a microcosm of how models affect one another. Head-to-head outcomes are transitive and so yield a stable ranking; independent authors converge on the same designs; and in a reinforcement-learning study with Qwen3.8-27B, the learner's hint lift on an author's environments tracks how much training on those environments improves it on held-out reasoning and agentic benchmarks, whether the environments are its own or another model's. Each of these patterns has a counterpart in how models shape one another outside the duel. By staging these interactions under controlled conditions, Environment Duels offers a window onto how LLMs teach, challenge and learn from one another. Code is available at https://github.com/LLM-Duels/EnvDuels.

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Topics: Microcosm, Computer science, Transitive relation, Frontier, Competitor analysis, Lift (data mining)

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange