Tim Bass

Research Statement

My research focuses on AI alignment, cyber situational awareness, and multisensor data fusion. The core thesis is that RLHF-trained LLMs are not a path to AGI; instead, LLMs are most useful as bounded Knowledge Sources within human-directed architectures, while RLWF better describes how biological intelligence actually develops. This is independent, non-affiliated, volunteer research conducted as charitable work.

2026 Publications & Preprints

• Blackboard SA - ACM DTRAP (under review) - Preprint: https://doi.org/10.5281/zenodo.18824512
• DLBP: Deterministic LLM Blackboard Pipeline - IAIT2026 (under review) - Preprint: https://doi.org/10.5281/zenodo.19068475
• MKMU Conceptual Framework - IAIT2026 (under review) - Preprint: https://doi.org/10.5281/zenodo.19089392
• MKMU Reference Implementation & Evaluation - AI & Society, AI in Asia Collection (under review) - Preprint: https://doi.org/10.5281/zenodo.19143912
• Digital Echopraxia - AI & Society (under review) - Preprint: https://doi.org/10.5281/zenodo.19159055

Concept Notes (Zenodo)

• RLWF: A Preliminary Concept - https://doi.org/10.5281/zenodo.19176921
• RLHF-Trained LLMs are Parasitic by Design - https://doi.org/10.5281/zenodo.19182346