# 语言模型系统中持久记忆的雅可比-因果记忆控制

> 全文为英语

**Published:** 2026年11月9日
**Author:** Marvin Danig (Achiral Research)
**Source:** arXiv — cs.AI
**Language:** en-US
**HTML:** https://aiqazaqstan.com/zh/articles/jacobian-causal-memory-control-for-persistent-memory-in-language-model-s

arXiv:2611.08779v1 发布类型: 新 摘要: 持久记忆系统不仅选择检索哪些记录，还选择哪些先前信息可以改变当前推理。我们提出雅可比-因果记忆控制 (JCMC)，它表示每个授权的记忆干预…

arXiv:2611.08779v1 Announce Type: new Abstract: Persistent-memory systems choose not only which records are retrieved but which prior information can alter current inference. We propose Jacobian-causal memory control (JCMC), which represents each authorized memory intervention by a predicted influence signature in a shared behavioral measurement space, selects interventions against a target effect and side-effect budget, and compares predicted with observed effects before retaining or rolling back the intervention. Under explicit smoothness and bounded-estimation assumptions, we prove a quadratic local-approximation bound, characterize the target effects reachable by a set of signatures, bound selection regret under signature error, and bound non-additive interactions between simultaneous memories. The paper reports no benchmark measurements; it defines a paired counterfactual estimator and an evaluation protocol comparing JCMC with relevance, graph, and policy-filtered retrieval baselines. The central claim is narrowed or falsified if those simpler baselines match effect targeting, side-effect control, and rollback utility under equal model, memory, policy, and cost budgets.

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*内容来自原始来源： [arXiv — cs.AI](https://arxiv.org/abs/2611.08779). 查看原文.*

**标签：** #arXiv

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