Interdisciplinary Aspects of Timeline Vector Chronodynamics

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

What's New in Version 3.0:This update introduces critical architectural enhancements to the Annihilated Attention layer, fully aligning the AI implementation with the core tenets of Timeline Vector Chronodynamics.• Causal Masking Integration: Implemented a rigorous causal mask (causal_mask) that prevents tokens from accessing future informational states. This ensures the model operates strictly in a retrospective manner, suppressing forward-looking probabilistic noise.• Temperature Scaling (tau ): Introduced a dynamic scaling factor tau into the context gate equation. This prevents premature informational dissipation, allowing the model to stably anchor its focus on deep historical roots (e.g., 5, 10, or 30 conversational steps back) without sacrificing gradient stability.• Code Monolith Completeness: The PyTorch implementation has been fully refactored, optimized for FP16/BF16 precision safety, and verified for direct deployment into modern Transformer configurations.