ScaleToT: Generalizing Structured LLM Reasoning for Billion-Scale Low-Activity User Modeling
arXiv:2606.24605v1 Announce Type: new Abstract: Accurate user modeling often depends on rich interaction histories, which are unavailable for billions of low-activity users. Large Language Models (LLMs) can infer latent user states from static profiles, but this reasoning becomes unreliable when profiles are sparse, and applying an LLM to billions of users is prohibitively expensive. We present ScaleToT, which learns structured reasoning from a small LLM-processed subset and extends it to the broader low-activity user population. To improve reasoning reliability, ScaleToT constructs typed user
The proliferation of LLMs and the economic imperative to extend their utility to a vast, previously untapped user base is driving innovation in scalable, efficient reasoning methods.
This development addresses a critical limitation in applying LLM reasoning to large-scale, sparse data sets, potentially unlocking new markets and improving personalization for billions of users.
The ability to generalize complex LLM reasoning to low-activity users cheaply means that personalized models can now be applied across much larger user populations than previously thought feasible.
- · AI platform providers
- · Consumer tech companies with large user bases
- · Marketing and advertising sectors
- · Data science and ML engineers
- · Companies reliant on expensive, bespoke user modeling
- · Traditional recommendation engine providers
- · Generic mass-market personalization solutions
Companies can now build more sophisticated user models for billions of 'low-activity' users at a significantly reduced cost.
This improved understanding of a wider user base could lead to more effective services, products, and advertising, driving increased engagement and revenue.
The democratization of advanced user modeling might intensify competition in consumer-facing industries, favoring those who can leverage this technology most effectively for personalization.
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Read at arXiv cs.AI