Task-Blind No MORE: Multi-Task Information Flow in Unified Ranking Backbones

• URL: http://arxiv.org/abs/2609.07273v1
• Date: Mon, 07 Sep 2026 09:36:57 GMT
• Title: Task-Blind No MORE: Multi-Task Information Flow in Unified Ranking Backbones
• Abstract summary: MORE (Multi-task cO-evolving Ranking modEl) embeds multi-task information flow inside the backbone.<n>It enables task-specific signals to co-evolve with sequence and feature representations at every layer rather than in a post-hoc fusion.<n> MORE consistently outperforms baselines across all tasks under comparable parameter and FLOPs budgets, and scales well with model size.
• Score: 15.647515309133025
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Industrial ranking models for recommendation have scaled feature interaction and sequence modeling separately; recent architectures such as HyFormer and MixFormer unify both in a stackable backbone. Real-world recommender systems, however, nearly always require multi-task learning, yet existing unified architectures confine multi-task modeling to shallow post-backbone towers, leaving the backbone without task-aware information flow. We propose MORE (Multi-task cO-evolving Ranking modEl), which embeds multi-task information flow inside the backbone, enabling task-specific signals to co-evolve with sequence and feature representations at every layer rather than in a post-hoc fusion. It introduces Anchor Tokens that persist across backbone layers: Shared Anchors encode cross-task commonalities, while Private Anchors capture task-specific priors. In each block, Anchor Tokens (1) read task-conditioned signals from behavior sequences, (2) mix with non-sequential features under a task-boundary mask, and (3) refine per-task representations through independent branches; as blocks stack, each task obtains a differentiated representation refined through all backbone layers. Experiments on large-scale industrial datasets show that MORE consistently outperforms baselines across all tasks under comparable parameter and FLOPs budgets, and scales well with model size. Online A/B tests on Momo, a leading Chinese social discovery platform with tens of millions of monthly active users, yield 3% improvement in usage duration, 3.6% in interaction rate, and 2% in deep-chat rate. MORE is deployed in production with request-level shared computation reducing scoring latency by about 30%.

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