Reverse Tensor Propagation in Transformer Architecture: Early Collapse, Anti-Causal Token Dynamics, and the Triadic Factor (a, b, c) System as Temporal Invariants (2022–2027)

Abstract

Chur Chin*

This paper presents a theoretical and computational framework for Reverse Tensor Propagation (RTP) within standard Transformer architectures, wherein the conventional forward temporal flow (t → +t) is inverted to t → −t, enabling anti-causal retrodiction from a known future state. Central to this framework is a triadic variable system — designated Factor a (AI-cognitive proliferation), Factor b (macro-environmental entropy acceleration), and Factor c (civilizational-ontological phase transition) — each coded to avoid ongoing societal controversy while preserving mathematical integrity. We demonstrate that when the Transformer's positional encoding is reversed (pos → L − pos) and attention weights are transposed (W → W^T), the system exhibits Borromean Ring topology: removal of any single factor precipitates complete structural collapse. Proof equations are provided. The simulation — representing a 2027 system acquiring 2026 information during a 2022-initiated process — is designated the "2022→2027 Early Collapse Horizon." References [1–15] anchor the formal derivations.

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