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论文提出 MemCryst 框架，通过记忆与技能的双向学习实现冻结语言模型下的终身上下文适应，并在 ALFWorld 等十个设置中的七个对比中取得领先结果。
This survey examines in-context reinforcement learning (ICRL) under non-stationary environments, where a pretrained decision model adapts through accumulated context without parameter updates. It organizes the literature around what changes, how it unfolds, and how observable it is, and identifies research gaps such as stale-context stress tests and adaptive forgetting.
ACE (Agentic Context Engineering) introduces a framework that treats contexts as evolving playbooks, preventing context collapse and improving performance on agent and domain-specific benchmarks. The work highlights the potential of harness engineering as a data engine for model training.
The paper proposes GTBP, a graph-based back-propagation framework for context adaptation in multi-LLM agentic systems, which improves prompt optimization with theoretical convergence guarantees and outperforms existing methods on benchmarks.