Building self-improving, self-evaluating AI systems that advance STEM research autonomously.
As AI capabilities advance with world models and cutting-edge research, humans are becoming the bottleneck of research progress. ARESA is building the scaffolding for scientifically controlled, empirically proven autonomous research—starting with human-in-the-loop collaboration and evolving toward independent discovery. Every proof, architecture, and method we develop is validated and shared openly with the world.
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.
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.
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.
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.
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.
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.
by Yevheniy Chuba • 2024
A comprehensive reference guiding you through core mathematical domains (calculus, linear algebra, probability, statistics) with applications from classical physics to modern machine learning. Integrates Python programming to visualize concepts and demonstrate how mathematics powers real-world AI systems.
by Yevheniy Chuba • 2024
50 comprehensive projects spanning healthcare, robotics, environmental science, finance, and cutting-edge AI applications. Demonstrates how to apply mathematical foundations to solve complex, real-world problems using state-of-the-art machine learning techniques.