arXiv cs.AI· Tianchi Chen, Shihua Yu·· 3 天前AI 评分20
Active Neural Matter：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）提出一种有限窗口响应框架，用于判断状态需保留哪些历史交互信息才能解释未来响应。前向分析预测状态与输出对扰动的响应，逆向分析检验候选状态信息是否充分，其分布分离度给出最坏情况误差下界。延迟共识仿真验证了响应幅度与相位的解析预测，AI 实验则识别出复现响应分布所需保留的交互历史与证据顺序差异。
正文 · 原文
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