arXiv:2607.01792v1 Announce Type: new Abstract: While decoder-only LLMs excel at a vast array of natural language tasks, it suffers from an asymmetric information flow induced by causal attention: later tokens are richer in contextual grounding than earlier ones. A simple and effective remedy is prompt repetition -- just appending a second copy of prompt before generation can redistribute grounding across positions and improve reasoning performance. However, full repetition of the original prompt doubles the KV cache footprint and quadruples attention cost during prefill, making it impractical
The rapid development and deployment of LLMs are pushing researchers to find more efficient methods to improve their core capabilities without significant computational overhead.
This research addresses a fundamental limitation in decoder-only LLMs related to information flow and computational cost, which impacts the practical scalability and performance of leading AI models.
A potential method for significantly improving LLM reasoning performance while mitigating the prohibitive computational costs associated with existing prompt repetition techniques is introduced.
- · AI model developers
- · Cloud computing providers
- · Companies relying on advanced LLMs
- · Inefficient LLM architectures
- · Energy-intensive data centers
More sophisticated and cost-effective LLMs become available for a wider range of applications.
The improved efficiency could accelerate the development of more complex AI agents and autonomous systems.
Reduced compute costs might lower barriers to entry for AI development, fostering broader innovation and competition.
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Read at arXiv cs.CL