LLM-OSDA couples bid-dependent optimal stopping with envelope pricing to auction a single native ad insertion in multi-turn LLM conversations; the ideal mechanism is DSIC in expectation, and a learned version gains 11% net revenue over fixed-timing baselines in simulation.
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LLM-OSDA: An Optimal-Stopping Dynamic Auction for Native Advertising in Multi-Turn LLM Conversations
LLM-OSDA couples bid-dependent optimal stopping with envelope pricing to auction a single native ad insertion in multi-turn LLM conversations; the ideal mechanism is DSIC in expectation, and a learned version gains 11% net revenue over fixed-timing baselines in simulation.