强烈推荐这篇来自Google DeepMind的论文，它提出了一种有趣的无需训练的模型架构进化方法，对递归自我改进方法有启发。
Google DeepMind提出一种无需训练的模型架构进化方法，通过模型自身反馈进行架构修改。在Gemma3系列上，自适应变体将困惑度降低23%，GSM8k准确率提升21%，仅进行轻微超参数调整。
You don't often see one-word titles in AI papers. That aside, strong recommend this paper from Goog...
You don't often see one-word titles in AI papers. That aside, strong recommend this paper from Google DeepMind. I think this is an interesting training-free approach to evolve model architectures by leveraging the model itself to inform architectural modifications. Something like this could also inspire even more robust recursive self-improvement approaches. Approach details below: A feedforward transformer can only update its internal state as many times as it has layers. Long generations need more updates than that, so chain-of-thought ends up doing basic state tracking in text. Recirculation adds recurrence at inference time. The model feeds activations back through itself during prefill, which lets it act like a dynamical system and track belief states without any retraining. Generation cost stays flat. All the serial work happens in prefill. On the Gemma3 family, the adaptive variant cuts perplexity 23% and lifts GSM8k accuracy 21%, with the original weights frozen and only light hyperparameter tuning. Paper: arxiv.org/abs/2608.17981 Track more trending AI papers in our academy: academy.dair.ai 💬 6 🔄 3 ❤️ 28 👀 2539 📊 11 ⚡