Research
Our work sits at the intersection of formal argumentation theory, behavioral science, and consciousness studies. Every tool we ship starts as a genuine question we could not answer with existing methods.
Most decision-making software assumes you have all the facts. In the real world, you almost never do. Parallax was built to handle exactly that uncertainty. It implements formal argumentation frameworks where some arguments are settled and others are not, where the system can reason about what it does not know just as rigorously as what it does.
The engine powers every other project in the lab. When MANAS resolves a household preference conflict, Parallax is doing the formal reasoning underneath. When CLAW evaluates whether an AI response passes a governance check, Parallax handles the argumentation layer. When Elthea assesses a child's behavioral profile, the decision evaluation traces back here.
It is open source under a license designed for collaborative intelligence. We believe that if you build on our engine, the insights you discover should flow back into the commons. Not as obligation. As shared curiosity.
Currently adopted by elthea.xyz for behavioral assessment in educational settings.
Here is a question that keeps regulators awake at night: when an AI generates something, who owns it, and who is responsible if it causes harm? Most people assume the answers are the same. Our formal analysis suggests they are not, and the gap between them creates a structural blind spot in every AI governance framework currently in use.
The OAAI framework does not just theorize about this problem. It proves the inconsistency formally using argumentation theory, then tests whether real human intuitions match the formal result. The live behavioral experiment presents participants with scenarios and measures where they draw the line. The preliminary data has been illuminating, and we are not ready to say more until the analysis is complete.
What we can say: the gap is real, it is measurable, and it matters for anyone building, deploying, or regulating AI systems. If you want early access to the findings, request it through our newsletter.
The Panchatantra was composed over two thousand years ago. The Jataka tales are older. Indigenous animal narratives span every continent. Modern researchers have studied them as literature, as theology, as cultural artifacts. Almost nobody has studied them as data.
We do. The Pattern Extractor treats each narrative as a compressed observational record. It identifies the species involved, the environmental stressors, the decision thresholds, and the exact moment conviction takes over from calculation. Every extraction goes through human validation. No automated pipeline. No hallucinated patterns. Just careful attention to what the stories actually encode.
The corpus is growing. It feeds directly into the design of our behavioral experiments and offers a temporal depth that no modern dataset can match. Thousands of years of observations about how conscious beings make decisions under pressure. Dismissed as fairy tales. We think that dismissal was premature.
Open Questions
We publish what we find. Even when the data contradicts our starting assumptions. Especially then.
How does framing change conviction? Same information, different perceptual context. We are building experiments to measure whether the threshold shifts when you change not what someone knows, but how they perceive what they know.
The conviction threshold is not uniquely human. Animal decision-making under uncertainty shows strikingly similar patterns. We are mapping these signals formally, building on recent advances in animal consciousness research.
Does subjective time perception shift the threshold? A dog and a human may face the same scenario with fundamentally different temporal architectures. We are designing experiments to test whether compressing or stretching time changes when conviction emerges.
Narratives from Panchatantra, Jataka, and indigenous traditions encode real behavioral patterns observed over millennia. We extract them formally and use them to inform the design of Directions 01 through 03. The ancients were better field researchers than we give them credit for.
We share research updates, experiment data, and working papers with a curated group. Apply for access.
Request Access →