Evans' Law 5.0: Long-Context Degradation in Multimodal Models and the Cross-Modal Degradation Tax 5.0

Evans’ Law v5.0 presents a comprehensive empirical investigation into long-context degradation in large language models (LLMs), introducing two validated scaling laws that quantify coherence collapse in both text-only and multimodal architectures. Through controlled cross-model testing across GPT-5.0/5.1, Claude 4.5, Gemini Pro/Flash, Grok 4.1, DeepSeek v3.2, Qwen, and Mixtral, the study identifies the relationship between model scale and effective coherent context, showing that usable context windows are far shorter than vendor-advertised capacities.

The paper establishes that text-only coherence follows a sublinear law of the form:

L_{\text{text}} \approx 1969.8 \times M^{0.74},

while multimodal coherence follows:

L_{\text{multi}} \approx 582.5 \times M^{0.64}.

The gap between these exponents demonstrates a robust, architecture-independent Cross-Modal Degradation Tax, typically reducing coherent reasoning depth by 60–80% when vision-language fusion is active. The paper also introduces a drift taxonomy (compression drift, expansion drift, logic-deferral drift, and code-layer leakage) that captures vendor-specific signatures preceding full collapse.

A key finding is the architectural regression in GPT-5.0, which collapses at ~35–47K tokens despite a predicted threshold above 110K, and transitions into an “opaque coherence” failure mode where outputs remain fluent but semantically incorrect and difficult to detect. These opaque failures pose significant safety, reliability, and compliance risks for enterprise users, especially when long-context decomposition bypasses single-instruction safety filters.

The work provides actionable implications for engineers, enterprises, and policymakers, arguing for validated coherence-limit reporting (e.g., “1M token window; coherence validated to 75K tokens”) as a necessary transparency norm. Evans’ Law v5.0 advances a reproducible empirical foundation for evaluating long-context reliability and motivates the development of a broader framework for Artificial Conversational Phenomenology.