这篇论文搞了个评估系统，能生成随时间变化的企业数据，在任意时间点重放来测试AI代理，比只看最终快照靠谱多了。
该论文来自arXiv 2608.01042，提出一个从真实研究生成人物驱动、随时间演化的企业世界的系统。传统离线评估只用单一静态快照，只能验证最终状态，无法覆盖问答时刻之前的数据变化。新系统通过schema推断时间描述，结合确定性与LLM重建每条记录的过去状态，并预计算为紧凑差分缓存。评估时可在任意时刻重放场景，快速复现且无模型干扰，早期实验已用于企业代理评测。
What Could the Agent See at 19:05? Generating Temporal Enterprise Scenarios from Real Research and Replaying Them to Evaluate Agents
Enterprise AI agents act across many apps whose data changes continuously, so an answer is correct only relative to what data existed and who could see it at the moment it was asked. Offline evaluation today grades against a single static snapshot, effectively the end of the episode. So, it can only evaluate one situation, the final one, even though every earlier moment of the episode is a different situation that invites its own realistic questions with its own correct answers. Recreating each of those moments as a separate snapshot would mean re-provisioning a whole tenant per instant, which is prohibitively costly; and even a single snapshot leaks future state hidden inside records and cannot represent the multi-app, time-ordered way real work happens. Our system closes two gaps at once: it generates a realistic, persona-driven, temporally-evolving enterprise world from real research, and replays that world at any chosen moment to evaluate any pluggable agent. A schema-inferred temporal description drives a deterministic-plus-LLM rebuild of each record's past state; because the queryable moments are finite, all rebuilds are precomputed into a compact difference cache, making evaluation a fast, reproducible lookup with no model in the path. We describe the design, an architecture spanning both flows, and early experience evaluating enterprise agents.