arXiv:2602.16837v2 Announce Type: replace Abstract: Transformer models systematically favor certain token positions, yet the architectural origins of this position bias remain poorly understood. This bias is closely connected to the Lost-in-the-Middle phenomenon, where models underutilize information placed in the middle of the context. We show that Lost-in-the-Middle-type behavior can arise from the architecture of causal Transformers itself. To do so, we develop a structural theory of position bias based on residual-aware cumulative attention rollout. At finite depth, causal masking and resi
This research provides a deeper architectural understanding of a known limitation ('Lost-in-the-Middle') in Transformer models, crucial as these models become more central to AI applications.
Understanding and addressing fundamental biases in Transformer architecture is critical for improving model reliability, efficiency, and performance across all AI applications, especially those requiring long context windows.
This structural theory allows for the development of more robust Transformer architectures and training methodologies that mitigate position bias and the 'Lost-in-the-Middle' phenomenon.
- · AI researchers
- · Transformer developers
- · Companies building advanced AI applications
- · Developers of long-context AI models
- · Legacy Transformer architectures
- · Applications highly sensitive to context window bias
Improved performance and reliability of large language models and other Transformer-based AI.
Reduced computational costs and smaller model sizes for equivalent or better performance in tasks requiring long context understanding.
Acceleration of breakthroughs in agentic AI and complex reasoning over extensive data, fostering new applications previously limited by context handling.
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Read at arXiv cs.LG