arXiv cs.AI· Ping Zhang, Quang-Huy Nguyen, Yingtie Lei, Wei-Lun Chao·· 12 小时前AI 评分18
通过预测恢复本体状态：一种模型相对的状态定义
Ontic state recovery through prediction
AI 导读
研究提出以模型相对方式定义系统状态：状态即系统在指定查询集合上的完整响应剖面，所有查询回答一致的配置视为同一状态。作者通过查询歧义、状态空间几何与预测目标选择三方面考察预测与状态恢复的关系，并在响应规律已知的合成系统与神经记录数据上验证。
正文 · 原文
Abstract:We encounter a system through its responses, yet its state concerns how the system is. Predicting further responses promises knowledge beyond what is immediately observed. What that knowledge tells us about the system's state depends on what we mean by a state in the first place. We give this notion a model-relative meaning: a state is the complete response profile over a specified query universe, and configurations agreeing on every query are regarded as the same state. From this perspective, a learned state is understood through the distinctions between these profiles that it retains. Prediction concerns selected observations; recovery concerns the profile as a whole. We examine their relationship through query ambiguity, state-space geometry, and the choice of prediction targets. Synthetic systems with known response laws and neural recordings provide complementary settings for this investigation. The aim is to clarify how prediction can become a way of knowing a system, and what it would mean for a learned state to preserve the state being studied.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Neurons and Cognition (q-bio.NC) |
| Cite as: | arXiv:2610.27139 [cs.LG] |
| (or arXiv:2610.27139v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.27139 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ping Zhang [view email]
[v1]
Mon, 19 Oct 2026 18:34:43 UTC (771 KB)
来源：arXiv cs.AI · arxiv.org