Research Publications

Yevheniy Chuba, ARESA — YoreAI / University of Pittsburgh

We introduce GeoAI Agentic Flow, a novel architecture that synthesizes coordinate embedding, spatial neural networks, and multi-agent collaboration to achieve state-of-the-art performance in fire hazard risk assessment. Our contributions include the Coordinate Embedding Framework (CEF) with proven bi-Lipschitz properties, a Spatial Neural Network (SNN) with graph-based attention, and a Multi-Agent Collaboration Protocol (MACP) with convergence guarantees. Evaluation on 546,000+ California addresses demonstrates 89.7% accuracy with sub-100ms latency.

Yevheniy Chuba, ARESA — YoreAI / University of Pittsburgh

We present the theoretical foundations of the Coordinate Embedding Framework (CEF), proving that the mapping from geographic coordinates to semantic vectors satisfies key mathematical properties including bi-Lipschitz distance preservation, feature reconstruction bounds, and stage independence. These theoretical results provide rigorous guarantees for geospatial machine learning applications.

Yevheniy Chuba, ARESA — YoreAI / University of Pittsburgh

We formalize the Multi-Agent Collaboration Protocol (MACP) for geospatial risk assessment, proving convergence guarantees, Byzantine fault tolerance bounds, and communication efficiency theorems. A 128-agent system organized into specialized pools achieves weighted consensus with provable optimality and tolerates up to 10 Byzantine failures.

Yevheniy Chuba, ARESA — YoreAI

We present a novel framework for studying emergent collaborative behavior in LLM multi-agent systems through a simulated Mars colony environment. Autonomous agents powered by GPT-4o-mini and Claude Haiku develop complex social dynamics, form relationships, engage in natural dialogue, and coordinate construction activities with minimal hardcoded behavior. Over 600+ simulated days with 100+ API interactions, we observe emergent patterns in conversation topics, relationship formation, and collaborative problem-solving. A hard cost ceiling of $0.50 per session is enforced server-side; observed spend was $0.007 per 100 calls on GPT-4o-mini.

Yevheniy Chuba, ARESA — YoreAI / University of Pittsburgh

AresaDB is a high-performance, multi-model database engine written in Rust that unifies key-value, graph, and relational data paradigms under a single property graph foundation. It achieves sub-millisecond point lookups while supporting complex graph traversals, relational queries, and vector search for RAG applications. Benchmarks demonstrate 22,000+ inserts/second and competitive query latencies against SQLite, DuckDB, and Pandas.

Yevheniy Chuba, ARESA — YoreAI

An interactive dashboard analyzing 550,000+ fire department dispatch records from 2014-2025. Explore temporal patterns, geographic hotspots, false alarm trends, and priority distributions through dynamic visualizations with real-time filtering.