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

Japanese

インタラクティブなコーディングパイプラインからオープンウェブで共有されたデータに至るまで、Frontier LLMsは、その先駆者と競争相手が作ったものを吸収します。 どの程度のコードがこのエコシステムで、創造者とソーラーとしての相互作用を果たしていますか? 合成トレーニングパイプラインから、オープンウェブで共有されたデータに至るまで、Frontier LLMsは、それぞれのモデルがこのエコシステムで、創造者とソーラーとしての役割を果たしていますか? 私たちは、環境ドゥエルを導入します:インタラクティブな環境で両方の能力を評価するダイナミックなエージェントベンチャー ベンチャーです。 各モデル

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