arXiv cs.AI· Cheng Lyu, Xueqi Wang·· 12 小时前AI 评分31
从事件到推理:基于显式状态的分层规则组合
From Events to Reasoning: Hierarchical Rule Composition over Explicit States
AI 导读
研究者提出事件推理系统(ERS),将推理状态表示为显式事件图,由语义特征提名候选对象与规则绑定,再由声明的规则和任务条件决定哪些可执行。在结构化事件图上,ERS 可构建最长十二步的依赖链,随着前瞻增至 16,将无关的已执行领域操作从 32 次降至 0,并在 24 个负例中未产生无支撑的目标输出。在十四个联合求解的开发任务上,其记录的动态子目标配置比单层全局控制少用 51.6% 的推理实际耗时。
正文 · 原文
Abstract:What a reasoning system emits is a conclusion; what it owes is a justification. Producing one requires every operation to be admissible on the facts actually available, the dependencies of intermediate results to survive to the end of the path, and the path to be revised when a premise changes. We present the Event Reasoning System (ERS), in which the reasoning state is an explicit event graph. Semantic features nominate candidate objects and rule bindings, and declared rule and task conditions decide which may execute, so candidates that violate the represented period, unit, or evidence constraints cannot enter an accepted reasoning path. Low-level rules provide reusable local transformations, and recursively composable high-level control rules organize subgoals and legal paths under the current goal, information gaps, and budget; a finite library is thereby rebound into paths never stored as complete solutions. We formalize admissible transitions, task-dependent control, compositional expressivity, dependency-preserving revision, and the gap between reachable solutions and those a budgeted controller finds. On structured-event graphs, ERS builds dependency chains of up to twelve steps, cuts irrelevant executed domain operations from 32 to 0 as lookahead grows to 16, and produces no unsupported target output across 24 negative cases; with rules frozen, it passes 12/12 focused checks comprising nine accepted derivations involving unseen input/path combinations and three justified refusals, as well as 6/6 fact-change cases. On fourteen jointly solved development tasks, the recorded dynamic subgoal configuration uses 51.6% less inference wall-clock time than single-level global control under their respective frozen settings.
| Comments: | 19 pages, 6 figures |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.22403 [cs.AI] |
| (or arXiv:2610.22403v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.22403 arXiv-issued DOI via DataCite |
Submission history
From: Cheng Lyu [view email]
[v1]
Wed, 14 Oct 2026 12:47:40 UTC (4,219 KB)
来源:arXiv cs.AI · arxiv.org