Cross-LLM Consistency in Inference: Evidence from Shared Interactions

Researchers demonstrate that different large language models develop remarkably similar internal inference patterns when processing identical prompts and predicting the same tokens, with this consistency being stronger among advanced models. The findings suggest LLMs may be implicitly converging toward common computational strategies despite differences in architecture and training, though the underlying mechanisms remain unexplained.

This research reveals a fundamental property of modern LLMs: convergence toward shared inference mechanisms. When different models face identical prediction tasks, they exhibit consistent interaction patterns in how they process information internally. This consistency intensifies among state-of-the-art models, suggesting that optimization pressures naturally guide architecturally diverse systems toward similar solutions. The discovery that shared interactions are typically lower-order and exhibit weaker positive-negative cancellation indicates these common patterns are relatively simple and direct compared to model-specific strategies.

The finding builds on years of mechanistic interpretability research attempting to understand LLM decision-making. Previous work established that different models sometimes learn similar features; this study deepens that insight by demonstrating systematic, measurable alignment in actual inference computations. The phenomenon appears universal rather than coincidental, pointing toward either inherent constraints in prediction tasks or powerful convergence properties in deep learning optimization.

For the AI development community, this has profound implications. If advanced models naturally gravitate toward similar inference strategies, this suggests certain computational approaches may be near-optimal for language understanding tasks. This could explain transfer learning success and provide pathways for more efficient model design by learning from convergent patterns. The finding also supports interpretability efforts by suggesting that understanding one model's inference patterns may illuminate others.

Future work should investigate what drives this convergence—whether it stems from task structure, optimization landscapes, or architectural constraints. Identifying these mechanisms could accelerate development of more efficient models and advance our theoretical understanding of deep learning itself.

• →Advanced LLMs develop remarkably similar internal inference patterns despite different architectures and training data
• →Cross-model consistency is more pronounced among state-of-the-art models than weaker baselines
• →Shared interaction patterns tend to be lower-order with reduced positive-negative cancellation effects
• →The finding suggests implicit optimization pressures push diverse LLM designs toward convergent computational strategies
• →Mechanisms underlying this cross-model consistency remain unidentified and warrant further investigation