We have a theory suggesting that the negation problem in AI is not a training defect but a structural limitation. Our initial proof of concept supports this direction, and we are now examining its implications.
Transforming the Future with Intelligent Innovation
Current Experiments
01 // Behavioral Shifts
We are studying how models change when negation is treated as an explicit logic constraint rather than a statistical pattern. Early observations point to improved consistency, but we are still mapping the full behavioral landscape.
02 // Physical Consequences
In dialogue systems, a hallucination is inconvenient. In physical systems, it can be dangerous. We are testing whether negation-aware reasoning reduces failure rates in simulated environments where safety margins matter.
03 // The Refusal Question
When a model correctly interprets "no," does it become safer or simply more selective? We are exploring when improved understanding leads to compliance and when it leads to unexpected forms of refusal.
04 // Asymmetric Intelligence
We are evaluating how small, negation-aware kernels compare to large-scale models on focused tasks. Our goal is to understand whether structured reasoning can, in some cases, compensate for parameter count.