User: Proposed a second small LLM Memory Manager that retrieves memory tokens and injects context into a primary LLM.
Assistant: Explored a dual-LLM architecture where LLM1 handles generation and LLM2 handles memory routing and attention-guided context injection.
User: Explored whether all memory tokens should be born equal.
Assistant: Concluded memory tokens can be stored equally but weighted unequally during consideration and recall.
User: Proposed geospatial and chronological metadata, salience, and operator-controlled memory.
Assistant: Discussed Artificial Consideration, Artificial Influence, datastore-backed attention, high-worth token injection, dual parallel attentions, and orthogonal scaling.
User: Proposed a datastore-backed second attention that streams high-value context tokens.
Assistant: Described this as a parallel attention architecture and 'Super-Attention'.
User: Discussed continuous operation, multiple LLM2 instances, self-regeneration, trajectory-aware exits, and high-worth token injection.
Assistant: Agreed the architecture is modular, reversible, orthogonal, and potentially practical using existing hardware and software.