The Continuous Improvement Engine (CEI) is a research effort on self-improving cognitive systems. It studies how an agent can refine its own behaviour across a recurring cognitive cycle — perception, deliberation, action, and reflection — under pressure that is not merely tolerated but converted into structural improvement (antifragility), with learned rules accumulated, revised, and pruned over time rather than encoded in advance.

This research line originated within applied work in precision poultry genetics (Gallora) and now operates as an autonomous, independent research effort.

Researcher

Portrait of Cleber Barcelos Costa, independent researcher in Betim, Brazil

Cleber Barcelos Costa Independent Researcher, Betim, Brazil ORCID: 0009-0000-5172-9019

Publications

Decentralized Thermal-State Load Routing and an ENAQT-Inspired Circuit Design Principle for Energy-Efficient Manycore Architectures

Barcelos Costa, C.

Zenodo · 2026 · Preprint · DOI: 10.5281/zenodo.19857070

Two contributions to energy-efficient manycore computing: a simulation-validated decentralized thermal-state routing algorithm achieving 92.1% thermal variance reduction under 4× localized overload, and a theoretical ENAQT-inspired CMOS topology proposed to convert Johnson-noise thermal fluctuations into charge-transport assistance.

Topology-Aware Binary SDM for Knowledge Graph Retrieval: A Multi-Architecture Empirical Study with Neural Baseline and Quantum Walk Analysis

Barcelos Costa, C.

Zenodo · 2026 · Preprint · DOI: 10.5281/zenodo.19645323

A hybrid retrieval method combining SimHash content addressing with weighted majority-vote aggregation of 1-hop graph neighbor signatures, achieving MRR 0.914 on a 392-node typed knowledge graph — a 2.13× improvement over a 384-dimension neural baseline using 48× less storage per node, with no GPU, neural training, or embedding API required.

Full publication list on Zenodo: zenodo.org/communities/gallori-ai/records

Contact

research@gallori.ai

For arXiv endorsement requests, please cite ORCID 0009-0000-5172-9019.