Abstract
The present disclosure relates to the field of Artificial Intelligence (AI), and more specifically to conscious AI systems integrated with Large Language Models (LLMs) for autonomous ideation and decision-making. The present disclosure introduces a consciousness layer to a fine-tuned LLM, enabling the model to continuously generate candidate topics, rank them using a weighted scoring algorithm, and select the highest-priority topic for ideation. The consciousness layer captures and stores intermediate outcomes, embeddings, agent actions, and metadata, which are vectorized and re-injected into the LLM’s context to maintain continuity. This loop operates without external prompting, emulating humans like spontaneous thought and topic switching. Furthermore, the system includes Model Control Protocol (MCP) and Agent-to-Agent (A2A) endpoints to invoke agents for executing actions and retrieving missing information, ensuring that ideation results are actionable. The invention addresses limitations of conventional LLMs by enabling proactive, self-directed analysis across domains such as resource optimization and cybersecurity compliance.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
KC, Shreyas and Gu, Yu, "CONSCIOUS AI THOUGHT ORIGINATION IN LLM", Technical Disclosure Commons, (January 18, 2026)
https://www.tdcommons.org/dpubs_series/9190