DeepSeek和GLM-5.2通过将推理痕迹前置，长上下文推理准确率大幅提升，最高达100%。
研究人员提出Trace as State方法，将推理痕迹作为任务状态的文本代理放置在长上下文块之前。在DeepSeek V4 Pro Preview模型上，该方法在GraphWalks Parents任务上将准确率从初始的29.2%提升至81.8%，显著优于Trace Append方法的43.0%。GLM-5.2模型在该任务上实现了100%的准确率。该方法在三种模型和三种长上下文数据集的27种组合中，有26种表现优于对照方法。
Trace as State: Reasoning Traces as Conditional States for Long-Context Transformers
Transformers process information causally, but long-context reasoning may depend on task state discovered only later. We formalize this mismatch through conditional state update tasks. For causal state update processors, providing the condition first can require exponentially less memory in the worst case than providing it last. Motivated by this principle, we introduce Trace as State. We use collected reasoning traces as a textual proxy for task state and place it before the long-context block on a fresh pass, allowing information derived previously to guide rereading. We conduct extensive experiments on Trace as State and Trace Append, a matched control that uses the same task state proxy but put it after the context. Across three models and three long-context datasets, Trace as State outperforms Trace Append in 26 of 27 reported combinations of model, task, and metric. On GraphWalks Parents, exact match lifts DeepSeek V4 Pro Preview from 29.2% on the initial pass and 43.0% with Trace Appendto 81.8% with Trace as State, and from 66.4% and 83.2% to 100.0% for GLM-5.2. These results show that placing traces before the context can improve long-context reasoning while retaining the causal transformer structure.