AgenticSTS: 一种用于长周期LLM智能体的有界记忆测试平台
AgenticSTS: A Bounded-Memory Testbed for Long-Horizon LLM Agents
AI总结：
提出一种有界记忆契约，通过类型化检索组装用户消息，避免原始跨决策转录，在《杀戮尖塔2》中实现并验证，揭示了战略技能层对胜率的提升效果。
摘要：
长周期LLM智能体的记忆是一种契约，规定了每个未来决策允许看到的内容。最简单的契约将过去的观察、工具调用和反思附加到每个提示中，这使得先前的上下文易于访问，但也使其变成一种混杂的混合物，其中任何单一记忆成分的效果都难以隔离。我们引入并实现了一种替代的有界契约：每个决策都由通过类型化检索组装的新用户消息做出，不附加原始跨决策转录。因此，提示在任何长度的运行中保持有界，并且任何单层都可以被隔离消融。我们在《杀戮尖塔2》中实例化该契约，这是一款封闭规则的随机卡牌构建游戏，其运行需要数百个战术和战略决策。一个关于同一游戏的前沿LLM的公开在线基准报告，在五个配置中最低难度下零胜率，而开发者报告的人类在同一难度下的胜率为16%；该任务困难但尚未饱和。在我们的框架内，固定A0消融显示，当触发战略技能时观察到最大差异：无存储基线赢得3/10局，添加技能层后赢得6/10局。在此样本量下，比较是方向性的而非统计上决定性的（Fisher精确p≈0.37）；跨骨干探测和公共累积上下文基线作为操作比较而非对契约变量本身的控制测试报告。我们发布一个可复现的测试平台：298条带有条件标签的完成轨迹、冻结的记忆/技能快照、提示记录和分析脚本——一个智能体设计以及一种经过验证的可重复方法论，用于研究显式记忆层如何塑造长周期LLM智能体的决策。
英文摘要：
Memory for a long-horizon LLM agent is a contract about what each future decision is allowed to see. The simplest contract appends past observations, tool calls, and reflections to every prompt, which makes prior context easy to access but also turns it into a jumbled mixture in which the effect of any single memory component is hard to isolate. We introduce and instrument an alternative bounded contract: every decision is made from a fresh user message assembled by typed retrieval, with no raw cross-decision transcript appended. The prompt thus stays bounded across runs of any length, and any single layer can be ablated in isolation. We instantiate the contract in Slay the Spire 2, a closed-rule stochastic deck-building game whose runs require hundreds of tactical and strategic decisions. A public online benchmark of frontier LLMs on the same game reports zero wins at the lowest difficulty across five configurations, and the developer-reported human win rate at the same difficulty is 16%; the task is hard but not saturated. Within our harness, a fixed-A0 ablation shows the largest observed difference when triggered strategic skills are enabled: the no-store baseline wins 3/10 games and adding the skill layer 6/10. At this sample size the comparison is directional rather than statistically decisive (Fisher exact p\approx0.37); a cross-backbone probe and public accumulating-context baselines are reported as operational comparisons rather than controlled tests of the contract variable itself. We release a reproducible testbed: 298 completed trajectories with condition tags, frozen memory/skill snapshots, prompt records, and analysis scripts -- an agent design and a validated, reusable methodology for studying how explicit memory layers shape long-horizon LLM-agent decisions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
