BAR Conjecture: the Feasibility of Inference Budget-Constrained LLM Services with Authenticity and Reasoning
This addresses a foundational challenge for practitioners in deploying efficient and reliable LLM applications, though it appears incremental as it formalizes known trade-offs.
The paper tackles the problem of designing LLM services that simultaneously optimize for inference-time budget, factual authenticity, and reasoning capacity, proving a trade-off among these properties and proposing the BAR Theorem framework as a principled solution.
When designing LLM services, practitioners care about three key properties: inference-time budget, factual authenticity, and reasoning capacity. However, our analysis shows that no model can simultaneously optimize for all three. We formally prove this trade-off and propose a principled framework named The BAR Theorem for LLM-application design.