# Jacobian-Causal Memory Control for Persistent Memory in Language Model Systems

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

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…

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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*From the original source: [arXiv — cs.AI](https://arxiv.org/abs/2611.08779). Open original.*

**Tags:** #arXiv

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