LLM Advertisement Based on Neuron Auctions

As Large Language Models (LLMs) transition into conversational agents, genera- tive advertising emerges as a crucial monetization strategy. However, embedding advertisements within unstructured LLM outputs introduces a critical trilemma: balancing advertiser payoffs, platform revenue, and user experience. Existing methods, such as prompt injection or rigid position slots, disrupt semantic coher- ence and lack a parametric framework for independent control, rendering rigorous mechanism design intractable. To bridge this gap, we introduce Neuron Auctions, a novel paradigm that shifts the auction object from the surface text space to the LLM’s internal representations. Leveraging mechanistic interpretability, we iden- tify brand-specific feed-forward network (FFN) neuron sets with low overlap in the evaluated settings. We use neuron counts as fine-grained intervention budgets that serve as auctionable commodities. Building on this computational carrier, we design a menu-based auction mechanism and establish an approximate strategy- proofness guarantee under bounded cross-advertiser interference. The mechanism is further optimized for platform revenue while explicitly accounting for user utility, thereby discouraging overly aggressive interventions. Direct empirical comparisons against simple prompt-level and fixed-position ad insertion baselines show more responsive and consistent bid-dependent control over predicted click intent, while menu-training experiments demonstrate a controllable trade-off among platform revenue, advertiser utility, and user utility.