AGENTIC-SYSTEMSApril 13, 20261 min read10 views

From Business Events to Auditable Decisions: Ontology-Governed Graph Simulation for Enterprise AI

LOM-action introduces event-driven ontology simulation to enterprise AI, enabling traceable and grounded decision-making that outperforms frontier models like DeepSeek-V3 in complex business scenarios.

Computer Science > Artificial Intelligence

Title: From Business Events to Auditable Decisions: Ontology-Governed Graph Simulation for Enterprise AI

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 event-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 $G_{\text{sim}}$; all decisions are derived exclusively from this evolved graph. The core pipeline is event -> simulation -> decision, realized through a dual-mode architecture -- skill mode and reasoning 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 illusive 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.

Source: arXiv cs.AI Recent

More in this category

agentic-systems

MUX: Continuous Reasoning via Multiplexed Tokens

Researchers introduced MUX, a novel latent reasoning method that compresses discrete text-based reasoning steps into continuous multiplexed tokens. By enabling lossless superposition, MUX significantly boosts LLM reasoning efficiency and speed across complex problem-solving tasks.

agentic-systems

ProbSPARQL: Querying Knowledge Graphs with Multi-dimensional, Uncertain Numeric Data

Researchers have introduced ProbSPARQL, an upward-compatible SPARQL extension designed to query multi-dimensional and uncertain numeric measurement data within Knowledge Graphs, providing significant query performance gains for complex industrial applications.

agentic-systems

Position: AI/ML Deepfake Research is Misaligned with AI-Generated Non-Consensual Intimate Imagery (AIG-NCII)

Current AI/ML research on deepfakes focuses primarily on epistemic harms like fake news and scams, leaving a dangerous gap in addressing AI-generated non-consensual intimate imagery (AIG-NCII) and its dignity harms to victims.

NOW LET US Related – Cross-Dialect Generalization Without Retraining: Benchmarks and Evaluation of Schema-Derived Constrained Decoding for MLIR

agentic-systems

Cross-Dialect Generalization Without Retraining: Benchmarks and Evaluation of Schema-Derived Constrained Decoding for MLIR

Researchers introduced schema-derived constrained decoding for MLIR, enabling a 1.7B small language model to match or beat 15B-34B LLMs without retraining, while generating code 8x-25x faster.

agentic-systems

Beyond Accuracy and Cost: Latency-Aware LLM Query Routing for Dynamic Workloads

Researchers introduced a novel latency-aware LLM query router that jointly optimizes generation latency, accuracy, and cost. The framework achieves up to a 40% improvement in accuracy-cost utility while maintaining low latency under dynamic workloads.

agentic-systems

MILP-Evo: Closed-Loop Fully Automatic Design of MILP Solvers

MILP-Evo introduces an LLM-guided closed-loop program evolution framework for automatically designing white-box MILP solver components. By iteratively generating and evaluating executable code directly in SCIP, it discovers inspectable and highly competitive domain-specialized solver logic.