Journal of Pioneering Artificial Intelligence Research

Abstract

This study proposes a preliminary framework for temporal deterministic machine cognition based on parallel eliminative filtering across multiple sensory domains. Rather than treating cognition as unrestricted association generation or isolated classification, the proposed perspective interprets cognition as a stabilization process in which conflicting, incomplete, or ambiguous sensory and conceptual hypotheses are progressively filtered within an acceptable temporal interval. The framework assumes that autonomous systems may receive heterogeneous inputs from multiple sensing modalities, including vision, audio, tactile sensing, thermal sensing, microwave sensing, chemical sensing, and other environmental measurements. These inputs may support, contradict, or partially overlap with one another. Instead of allowing direct action generation from isolated detections, the framework introduces layered filtering, temporal consistency analysis, reliability estimation, contradiction suppression, and operational admissibility evaluation prior to goal emergence. In this formulation, object recognition alone is insufficient for autonomous operation because operational meaning depends on contextual interpretation, mission relevance, temporal stability, and cognitive admissibility. The same detected object may produce different operational meanings under different environmental, biological, or mission conditions. The framework further proposes that goals themselves may require different levels of cognition. Reflexive actions, operational tasks, and strategic missions may therefore belong to distinct cognitive clusters associated with different temporal horizons, ambiguity tolerances, and world-model requirements. Under this interpretation, cognition is not defined as unrestricted intelligence expansion, but as the controlled reduction of instability, contradiction, and unsafe operational hypotheses through parallel eliminative filtering. This perspective may provide a deterministic and operationally stable foundation for future autonomous robotic systems operating under uncertain and multi-sensory environments. The framework remains conceptual and preliminary.