Our research perspective connects machine perception, explainable AI, human–computer interaction, and epistemic trust. These are evolving questions, not claims of finished studies.
Intelligent perception begins with information from the environment. We explore sensing architectures, spatial detection, and the ways machine interpretation can distinguish relevant entities from surrounding complexity.
Observation is not understanding. Our inquiry considers how models interpret evidence, represent uncertainty, and connect signals to context—without treating an inferred pattern as an established fact.
Autonomous systems connect perception, navigation, and decision logic. We investigate conceptual architectures for operational intelligence, including how changing environments and system constraints might shape machine-assisted action.
Human-machine systems are shaped by attention, information, and judgment. We explore how decision environments could preserve meaningful human context as intelligent systems become more capable.
Fluent interaction is not the same as demonstrated understanding. Our inquiry examines how explanations, persistent memory, contextual inference, naturalistic speech, and visible uncertainty affect how people judge and rely on intelligent systems. We explore ways to make evidence inspectable, correction possible, and human judgment meaningful.
Resident Model Lab is a local research runtime around the existing OLMoE model. Its v0.2 selection v2 always preserves the router's requested experts and uses layer-partitioned caches to manage which expert weights stay GPU-resident. In one small preliminary experiment, generated tokens were identical across 128 tested requests; at equal cache capacity, mean latency fell 6.3%. An earlier expert-substituting selector degraded output and is retained as a historical negative result. Not peer reviewed.
Resident Model Lab / Read v0.2 results →Research begins by making assumptions visible. We consider how information is acquired, how interpretations are formed, and where technical confidence should give way to uncertainty.
Research themes include both conceptual investigations and the implemented Resident Model Lab research software. Its preliminary results are disclosed separately from untested hypotheses; no deployment, validated performance gain, or clinical outcome is implied.