这篇论文把一次长推理拆成多轮小步骤，每一步都能检查修正，适合做长文档问答的人看看。
该论文提出 Chained Recursive Language Models（Chained RLM），一种推理时架构，让同一个模型以全新上下文被反复调用。每一轮推理接收原始问题和紧凑摘要，而非完整对话历史，通过黑板和工作区传递中间结果。论文描述了系统模型、交接机制、工件工作区及评估协议，并研究了何时使用新鲜上下文的多阶段计算相比直接回答更准确。在需要抽取、计数、排序和多跳推理的任务中，该方法能减少早期错误传播。
Chained Recursive Language Models for Multi-Iteration Reasoning
Long context reasoning in large language models (LLMs) is usually constrained by the fact that a single inference trajectory has to simultaneously explore the context, store intermediate state, verify evidence, and produce the final answer. This becomes particularly difficult in tasks that require extraction, counting, ordering, or multi-hop reasoning, where an early mistake can propagate until the final response. In this work, we propose Chained Recursive Language Models (Chained RLM), an inference-time architecture, in which the same underlying model is called repeatedly as a sequence of fresh reasoning roots. Each root receives the original problem and context, but does not inherit the full conversational history. Instead, it receives a compact plain-text summary, a plain-text blackboard, and some durable task-specific artifacts written by predecessor roots. The motivation is to manage the context by chopping into partial tasks rather than one large inference response; in each staged computation, intermediate artifacts can be inspected, corrected, and extended by a later fresh inference by the same model. We describe the system model, handoff mechanism, artifact workspace, and evaluation protocol for this system. We study when fresh-context artifact continuation gives a measurable gain in accuracy over direct LLM answering even with recursive tool-calling.