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These findings establish articulated rationales as a useful complement to behavioral and content signals, and demonstrate a practical role for MLLMs in scaling sparse human explanations into preference information that improves industrial recommendation.
This work proposes WebSwarm, a progressive recursive delegation framework that jointly constructs task decomposition, recursive expansion, and agent collaboration during inference and consistently outperforms single-agent and multi-agent baselines on deep, wide, and interleaved deep-and-wide tasks.
SWIM (Step-Wise Integrated Measure), a list-level evaluator that models user behaviors as a finite-horizon prefix session-level survival process, and efficiently estimates continuation probabilities and utilities in parallel, satisfying strict industrial latency constraints.
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