arXiv:2604.08603v1 Announce Type: new
Abstract: Existing LLM-based agent systems share a common architectural failure: they answer from the unrestricted knowledge space without first simulating how active business scenarios reshape that space for the event at hand — producing decisions that are fluent but ungrounded and carrying no audit trail. We present LOM-action, which equips enterprise AI with emphevent-driven ontology simulation: business events trigger scenario conditions encoded in the enterprise ontology~(EO), which drive deterministic graph mutations in an isolated sandbox, evolving a working copy of the subgraph into the scenario-valid simulation graph $G_textsim$; all decisions are derived exclusively from this evolved graph. The core pipeline is emphevent $to$ simulation $to$ decision, realized through a dual-mode architecture — emphskill mode and emphreasoning mode. Every decision produces a fully traceable audit log. LOM-action achieves 93.82% accuracy and 98.74% tool-chain F1 against frontier baselines Doubao-1.8 and DeepSeek-V3.2, which reach only 24–36% F1 despite 80% accuracy — exposing the emphillusive accuracy phenomenon. The four-fold F1 advantage confirms that ontology-governed, event-driven simulation, not model scale, is the architectural prerequisite for trustworthy enterprise decision intelligence.
Adaptation to free-living drives loss of beneficial endosymbiosis through metabolic trade-offs
Symbioses are widespread (1) and underpin the function of diverse ecosystems (2-6), but their evolutionary stability is challenging to explain (7,8). Fitness trade-offs between con-trasting