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LLMs can learn to actively compute evidence by routing through retrieval and computation, dramatically improving performance on complex, multi-source reasoning tasks.
User simulators trained on existing datasets may be optimizing for unrealistic scenarios, as a new benchmark reveals a significant "realism gap" in their ability to generalize to diverse recommender behaviors.
