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A deep architecture that is able to jointly optimize cost functions and route-ranking model towards any route preference is proposed, and a novel loss function is proposed that optimizes a single-objective variable, with other variables strictly under constraints.
IntHQ's innovative architecture not only mitigates common pitfalls in multi-task learning but also delivers a measurable 1.60% lift in user engagement for travel recommendations at scale.
Forget maps: LLMs can learn end-to-end transit route planning directly from data, even grounding GPS coordinates without explicit mapping.
LLMs can handle basic route planning, but fall apart when user preferences enter the mix, as shown by a new benchmark based on real-world queries.
