Researching AI systems that do more than generate plausible text — especially semantic trust before execution, falsifiable evidence-seeking behavior, and stateful/governed AI system architecture.
Semantic trust boundaries for AI-generated program behavior.
AiNIR treats model output as a claim, not a fact. It checks bounded workflow semantics before lowering, host handoff, or execution while leaving real execution authority with the host.
Current public line: v1.0 RC candidate public demo.
Testing whether agents know when their observation space is incomplete.
The research asks whether an agent can keep compatible hidden explanations alive, choose a reality-contact observation that distinguishes them, update from evidence, and know when to stop or ask for more.
A public benchmark for execution authority around AI output.
The arena compares what changes when model text acts directly versus when the same output remains a candidate behind state, evidence, and policy gates. It is a benchmark/evidence surface, not a production plant-safety or certification claim.
A recurring theme across the public work is separation of concerns:
proposal != fact
reasoning != authority
evidence != truth
verification != execution
operational commitment != permanent epistemic closure
Additional architecture, learning, memory, perception, control, and product research remains private until an explicit publication or release review determines an appropriate public scope.