摘要：长对话记忆基准日益测试召回能力和由提示触发的知识更新，近期研究也在考察不断演变的用户信念与记忆状态。TWIST 是一套拟议中的基准套件，旨在衡量一种互补但尚未被量化的属性：干预质量——即已部署的记忆系统通过其自身的摄入、检索和审核接口运行时，能否在信念变化节点做出正确行为。四条赛道分别覆盖：未提示的张力检测、依据记录审核即将输出的草稿、在保留替代历史的同时根据当前信念作答，以及对敏感记忆检索进行治理。该套件扩展了 LoCoMo 的语料库和测试框架，并为每项“检测/拦截”指标配套设置了相应的“不要过度检测”对照项：表面形式匹配的困难负例为错误干预设定代价，因此任何赛道都无法通过标记所有内容来获得优势。基准本身首先经过验证：采用独立、对黄金标签盲测的双重标注与裁决，并进行评审器诱饵校准和可分离性审计。在经人工验证的 Track B v1.0 标注集上（161 个项目，裁决后 kappa = 0.85），没有任何已测试配置能同时实现高矛盾召回率、高困难负例特异性和高归因能力：平面 RAG 基线可检测到 0.76-0.97 的真实矛盾，但会错误标记 16-43% 表面形式匹配的安全草稿，具体取决于后端；而一套已部署、以连贯性为导向的系统几乎从不过度标记（特异性为 0.98-1.00），却只能捕获 42% 的真实矛盾——这是任何仅看召回率的分数都无法揭示的权衡。由 13 种配置构成的基线阶梯定位了问题成因：每个黄金标准矛盾都能仅凭其证据被检测到（召回率 1.000）；在获得完整对话记录时，经校准的模型几乎能解决该赛道——这与存在显著检索覆盖缺口的判断一致；而仅草稿的性能下限则揭示了因模型而异的文体先验。除召回分数外，系统的 TWIST 档案还可衡量其记忆系统是否知道何时应干预，以及何时不应干预。

Abstract:Long-conversation memory benchmarks increasingly test recall and prompted knowledge updates, and recent work studies evolving user beliefs and memory state. TWIST is a proposed benchmark suite for a complementary, unmeasured property: intervention quality -- whether a deployed memory system, exercised through its own ingest/recall/vet surface, acts correctly at belief change points. Four tracks cover unprompted tension detection, vetting outgoing drafts against the record, answering with current beliefs while preserving supersession history, and governing sensitive recall. The suite extends LoCoMo's corpora and harness, pairing every detect/block metric with a matched do-not-over-detect control: surface-matched hard negatives price false intervention, so no track can be gamed by flagging everything. The benchmark itself is validated first: independent, gold-blind double annotation with adjudication, judge decoy calibration, and a separability audit. On the human-validated Track B v1.0 key (161 items, post-adjudication kappa = 0.85), no tested configuration simultaneously achieves high contradiction recall, high hard-negative specificity, and high attribution: flat-RAG baselines detect 0.76-0.97 of true contradictions but falsely flag 16-43% of surface-matched safe drafts depending on backend, while a deployed coherence-oriented system almost never over-flags (0.98-1.00 specificity) yet catches 42% of true contradictions -- a trade-off no recall-only score can see. A 13-configuration baseline ladder localizes causes: every gold contradiction is detectable from its evidence alone (recall 1.000), calibrated models nearly solve the track given the full transcript -- consistent with substantial retrieval-coverage gaps -- and draft-only floors reveal model-dependent style priors. A system's TWIST profile, beside its recall score, measures whether memory knows when to intervene and when not to.