[Submitted on 14 Oct 2026]
Title:From Events to Reasoning: Hierarchical Rule Composition over Explicit States
View a PDF of the paper titled From Events to Reasoning: Hierarchical Rule Composition over Explicit States, by Cheng Lyu and 1 other authors
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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)
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