Principal Investigator
Independent Researcher · ZenSation Research · Kiel, Germany
Research on cognitive architectures, AI safety in public spaces, and applied AI for knowledge work. Self-funded without external third-party funding. Open access on arXiv and with DOI on Zenodo; code under Apache 2.0. Alongside the research, he partners with organisations on methodologically grounded AI adoption — as a research partner, not a vendor.
The person behind ZenSation
Why a practitioner from foreign-exchange trading now pursues foundational research on machine memory. In his own words.
I started automating foreign-exchange trading in 2009. Anyone who builds trading systems learns an uncomfortable lesson faster than any theory could teach it: the market punishes every assumption you have not made explicit. A system that worked yesterday and loses money today has not changed — it just never understood why it worked. That experience has permanently shaped my relationship with software: I do not trust a system that cannot remember its own decisions.
Out of trading automation grew the real question: how do you build systems that learn from their own history instead of forgetting it? Years of practice inside large corporations — at the seam between grown processes, real data landscapes and what software can actually deliver — sharpened that question. Corporations forget just like trading systems do: in inboxes, in departing heads, in project drives nobody opens any more.
Today's answer to that question is collective intelligence on cognitive architecture — humans and machines together, on a structure that retains knowledge. Not a bigger model, but a better structure around the model: a memory that carries decisions, reasons and context across years. A memory that belongs to someone else is none. It must belong to the one whose experience it holds.
This work is published: ZenBrain (arXiv:2604.23878) describes the architecture in full — nine foundational algorithms and six PMA components. A package of 20 modules is open source. I publish what I build because verifiability is the currency that counts in this discipline.
ZenSation works out of Kiel. That is neither an accident nor a disadvantage: foundational work needs concentration more than proximity to conference stages. Whoever works with me works with the person who built the systems — there is no second row to delegate to.
Identifiers
Research focus
How AI remembers, reasons, and learns over time — seven-layer memory architecture (ZenBrain), sleep consolidation, neuromodulator-driven consolidation, FSRS-style review. We see ZenBrain as an early bridge contribution to dynamic, lifecycle-oriented memory architectures: independent, externally recognised and openly tested.
Predictive early warning in public spaces under a civil-liberties architecture (CrowdGuard) — explicitly without biometric identification, helper protection as a structural element of the objective function.
Research on human–AI collaboration in organisations — meetings, project management, knowledge work, and process mapping, including a methodology for structured AI adoption. At its centre is Eigenraum by ZenSation as a productive research application — a vendor-neutral AI operating system on the open core ZenBrain, where we test whether the memory research holds up in daily use.
Background
Fully automated, algorithmic trading system (MQL4 in MetaTrader 4), live on the personal computers of thousands of customers — with dedicated research and systematic testing. Methodological foundation: statistical modelling, deterministic strategies, real-time processing.
Work on digitalisation projects at scientific institutions: at the IPN – Leibniz Institute for Science and Mathematics Education at Kiel University, and at IQSH (Institute for Quality Development in Schools Schleswig-Holstein), on the digitalisation of learning and educational materials.
Digitalisation and transformation projects at the corporate and stock-corporation level: process atlases, AI strategies, and AI-supported decision systems across industry contexts.
Hands-on operational experience in security — on active duty as an emergency responder and in the commercial security sector as a close-protection officer. Alongside this, work for German public-safety authorities and organisations (BOS) and the Federal Ministry of the Interior (BMI), partly voluntary and partly project-based — in incident-sector command and in committee work. This operational reality shapes the applied orientation of the public-safety track.
Reorientation toward fundamental and applied research. First form of the cognitive architecture with reliable long-term memory; in parallel, the question of fundamental-rights-preserving safety mechanisms for public spaces.
Conception and first implementation. Establishment of the three research tracks (cognitive architectures, safety in public spaces, applied AI for knowledge work). Build-out of the research documentation, pre-registration of central documents via OpenTimestamps.
Production implementation in the internal repository: 7-layer memory architecture, test suite, and the first application (ZenAI), architectural consolidation across multiple phases. Late March: open-source release of the ZenBrain packages on npm under Apache 2.0.
Publication of the ZenBrain paper as an open-access preprint on arXiv (2604.23878) and with a permanent DOI on Zenodo (10.5281/zenodo.19353663) — publicly accessible, with replication material.
Follow-up publications for peer-reviewed venues. Exploratory exchanges with universities and research consortia across the European research area on consortium proposals along BMBF SIFO, Horizon Europe Cluster 3, and national funding lines.
Research collaborations
Research in an independent lab does not occur in a vacuum. Outreach to potential co-authors is consistently kept in conditional terms — names appear on this page only after written consent. This discipline protects potential partners and distinguishes exploration from established collaboration.
Outreach in progress
Outreach to research institutions across the German and European space is ongoing in waves 2026–2027. Cooperation partners are named publicly only after receipt of written consent.
What we say about our systems is published, disclosed, or verifiable live in the playground. What we cannot substantiate, we do not claim.
Good work can be recognised by the fact that it can be continued without its author. Every ZenSation project therefore ends with a handover, not with a dependency: you remain capable of acting, even without us.
Pilot projects and advisory work keep the foundational research independent, not the other way round. Every euro from them flows back into open, verifiable research.
For companies considering a collaboration, the Working-with-us page is the right entry point. For scientific enquiries, reproductions and data access: research@zensation.ai. I answer both personally.
Contact
Inquiries are typically answered within ten working days.
More from this research
Core and application fields, on a shared ethics foundation.
Alexander Bering, as one person. Anyone working with the lab works with the person who built the systems — there is no second layer between research and implementation.
Through a lesson trading systems teach faster than any theory: the market punishes every assumption you did not make explicit. Trading automation from 2009 onward turned into the actual question — how do you build systems that learn from their own history instead of forgetting it. The present answer is collective intelligence on a cognitive architecture.
Because foundational work needs concentration more than proximity to conference stages. The distance from the usual hubs is a working condition, not a drawback.
The paper is arXiv:2604.23878, the concept DOI is 10.5281/zenodo.19353663, and the ORCID is 0009-0001-1793-012X. Google Scholar and Semantic Scholar additionally link the publications.