arXiv：cs.AI· Tianchi Chen, Shihua Yu·· 17 小时前
Active Neural Matter（ANM）：面向物理系统与分布式 AI 工作流的有限窗口响应框架
Active Neural Matter: A Finite-Window Response Framework for Physical Systems and Distributed AI Workflows
AI 导读
Active Neural Matter（ANM）提出有限窗口响应框架，用前向分析预测系统对扰动的状态与输出响应，并将传播与输出敏感度分离；逆向分析则检验候选状态是否保留足够信息。该框架给出近似行为最坏误差的下界，并用不确定性界限区分拒绝、固定容差内支持与未决三种情形。延迟共识仿真验证了响应幅度与相位的解析预测，AI 实验则识别出交互历史与证据顺序中表征必须保留的区别。
正文
Abstract:Physical systems and distributed AI workflows can retain past information, receive delayed feedback and combine conflicting signals. These features pose a common modeling problem: determining what information about past interactions a state must retain to account for future responses. Active Neural Matter (ANM) addresses this problem through a finite-window response framework. Forward analysis uses each system's dynamics to predict state and output responses to perturbations, separating propagation from output sensitivity. Inverse analysis tests whether a candidate state retains enough information for these responses. With the system and future inputs or interaction rules fixed, distinguishable responses from histories assigned the same state identify distinctions the representation must preserve. Their distributional separation gives a lower bound on the worst-case error of approximating behavior from that state. Uncertainty bounds distinguish rejection, support within a fixed tolerance on tested comparisons, and unresolved cases. Delayed-consensus simulations check analytical predictions of response amplitude and phase over a narrow range of delays; flocking illustrates dependence on the chosen observable. An exact delayed-return construction establishes a sufficient state for a specified program. The AI studies identify distinctions in interaction history and evidence order that a representation must preserve to reproduce observed response distributions. Tests on new histories in a controlled feedback task provide bounded support for a state specified by the task instructions; the instructed rule predicts every observed action. ANM connects response prediction to measurable information requirements, separating behavioral fidelity from task correctness and initial-state adequacy from a closed evolution law for that state.
| Subjects: | Machine Learning (cs.LG); Statistical Mechanics (cond-mat.stat-mech); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Applied Physics (physics.app-ph) |
| Cite as: | arXiv:2610.14549 [cs.LG] |
| (or arXiv:2610.14549v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.14549 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Tianchi Chen [view email]
[v1]
Sat, 10 Oct 2026 04:15:06 UTC (395 KB)
来源：arXiv：cs.AI · arxiv.org