Regneo is a research-driven organization. The work here is not an archive of publications — it is an active exploration of what intelligence has to be able to do before it is useful inside a real industrial or enterprise environment.
Techniques change quickly. What a system has to be able to do changes slowly. Structuring research around capability means new methods extend this framework instead of replacing it.
R.01
Perceive
Understanding the world.
Real environments arrive as fragments: sensor streams, scanned documents, logs, images, tables, half-labelled records. Perception research at Regneo is about turning that raw, uneven input into a representation a system can actually work with.
Current threads+
Making sense of messy, partially structured operational data
Grounding models in documents, records and physical signals
Knowing when an input is out of distribution rather than guessing
R.02
Reason
Connecting information and making sense of complexity.
A useful system has to hold context: constraints, dependencies, history, and the rules of the environment it operates in. We study how models can combine retrieved knowledge with explicit structure so that conclusions can be traced rather than merely produced.
Current threads+
Retrieval and knowledge structure over long-lived domain corpora
Constraint-aware decision support instead of free-form generation
Traceability: showing why a system reached an answer
R.03
Learn
Improving through experience, data and feedback.
Industrial and enterprise environments change: processes are revised, regulations are amended, equipment is replaced. We look at how systems absorb that change from real usage and correction rather than being frozen at training time.
Current threads+
Learning from operator correction and human feedback loops
Evaluation that reflects the real task, not a proxy benchmark
Detecting drift between a model and the environment it serves
R.04
Act
Turning intelligence into useful decisions and actions.
Intelligence only matters when it reaches the workflow. This is the research on interfaces, autonomy boundaries and safeguards — deciding what a system should do on its own, what it should propose, and what it must escalate.
Current threads+
Where the boundary between assistance and autonomy should sit
Human-in-the-loop interfaces for high-consequence decisions
Failure behaviour: what a system does when it is unsure
Making sense of messy, partially structured operational dataGrounding models in documents, records and physical signalsKnowing when an input is out of distribution rather than guessingRetrieval and knowledge structure over long-lived domain corporaConstraint-aware decision support instead of free-form generationTraceability: showing why a system reached an answerLearning from operator correction and human feedback loopsEvaluation that reflects the real task, not a proxy benchmarkDetecting drift between a model and the environment it servesWhere the boundary between assistance and autonomy should sitHuman-in-the-loop interfaces for high-consequence decisionsFailure behaviour: what a system does when it is unsureMaking sense of messy, partially structured operational dataGrounding models in documents, records and physical signalsKnowing when an input is out of distribution rather than guessingRetrieval and knowledge structure over long-lived domain corporaConstraint-aware decision support instead of free-form generationTraceability: showing why a system reached an answerLearning from operator correction and human feedback loopsEvaluation that reflects the real task, not a proxy benchmarkDetecting drift between a model and the environment it servesWhere the boundary between assistance and autonomy should sitHuman-in-the-loop interfaces for high-consequence decisionsFailure behaviour: what a system does when it is unsure
QOpen questions
What we are actually trying to answer.
These are stated as questions because that is what they are. Anyone claiming to have solved them for real environments is describing a demo.
Q.01
What does an AI system need to know about an environment before it is useful in it?
Most demos assume clean context. Real deployments start with almost none.
Q.02
How should a system express uncertainty to a person who has to act on it?
A confidence score is rarely the answer an operator actually needs.
Q.03
What is the smallest amount of structure that makes a model reliable in a regulated domain?
Too little and it drifts; too much and it stops generalizing.
Q.04
How do you evaluate a system whose task changes faster than your benchmark?
Evaluation is the hard part of applied AI, not modelling.
LResearch log
Notes, experiments and publications.
This is where in-progress work is published as it becomes shareable.
Status
No notes published yet.
Regneo is early. Rather than fill this page with citations that do not exist, it stays empty until there is genuine work to share — the first entries will be technical notes and experiment write-ups from the four research areas above.
