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Agents2026-06-25 · source-backed

Qwen-AgentWorld topped Hugging Face Daily Papers with "language world models."

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The most-upvoted paper for June 24 (394 upvotes) proposes agents that simulate and reason about an environment in text before acting, rather than learning purely from trial-and-error rollouts (HF Daily Papers). It's the planning-agent direction: predict consequences internally instead of discovering them by failing. A complement to today's reactive tool-calling loops, and worth tracking if your agents waste steps thrashing through actions they could have ruled out by thinking first.


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2026-06-25-qwen-agentworld-topped-hugging-face-daily-papers-with-language-world-models
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source-backed, canonical briefing excerpt