arXiv Machine Learning
Content Exploration Beyond the Feed: Creator Supply and the Shared Corpus
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arXiv Machine Learning
Aug 6
The Price of Isolation: Estimating the Ecosystem Cost of Symmetric Two-Sided A/B Testing
arXiv:2608. 04432v1 Announce Type: cross Abstract: On two-sided content platforms, symmetric two-sided isolation (assigning matched fractions of creators and viewers to isolated treatment and control submarkets) is widely used for creator-side and cold-start experiments because it removes cross-arm marketplace interference.
arXiv Machine Learning
Oct 21
Baseline and Incremental: User, Item, and Request Debiasing in Instagram Feed Ranking
The paper proposes a multi‑signal value model for Instagram Feed ranking that separates baseline predictions from incremental gains, arguing that retaining raw predictions and adding incremental terms improves ranking quality. It describes how three independent debiasing families—viewer habit, item popularity, and request context—are combined into a single parameterized operator, with a design procedure selecting optimal points. Controlled ablation studies show that the two‑term model outperforms single‑term variants in most metrics, and the approach has been adopted across Meta’s products, boosting key engagement metrics.
arXiv AI
Sep 15
ChatGPT Images 2.5 in the Wild: A Launch-Period Dataset and Detector Evaluation
arXiv:2609.15100v1 Announce Type: cross Abstract: An image tool can change its underlying generator while retaining its public name, making version attribution from online posts ambiguous. We study t...
arXiv AI
Sep 25
Delay-of-Gratification as a Multi-Agent Survival Micro-benchmark for Long-Horizon LLMs: Social Exposure, Personas, and Tool Use Budgets
The paper introduces a new multi‑agent micro‑benchmark called Delay‑of‑Gratification, modeled after the Stanford marshmallow experiment, to evaluate large language models (LLMs) in long‑horizon, multi‑turn interactions. In the benchmark, ReAct agents use a per‑step “raise a question” tool under various constraints—social context (broadcast vs. isolated), persona traits (age, hedonic drive), and tool‑use policy (mandatory vs. optional). Across 19,200 trajectories, the study finds that most agents exhibit an early impulse to “eat,” only 75.9% persist to the end, and factors such as isolation and hedonic drive significantly influence survival and questioning behavior, with ablations showing that removing hedonic drive and age can improve completion rates.
arXiv AI
Aug 20
The Lifecycle of LLM-as-a-Judge for Large-Scale Recommendation Explanations
The paper introduces a lifecycle framework for LLM-as-a-Judge systems used to evaluate recommendation explanations at Netflix. It outlines four phases—Birth, Training, Deployment, and Monitoring—detailing how each stage addresses specific technical and operational challenges. The authors report that after five weeks of A/B testing, judge-aligned explanations increased novel content viewing and successful browse-to-play sessions without quality takedowns.
arXiv AI
Sep 1
CineForge: Self-Improving Agents for Long-Horizon Video Generation
arXiv:2608.29621v1 Announce Type: cross Abstract: Long-horizon story-driven video generation requires a production agent to coordinate narrative decomposition, state tracking, shot design, prompt con...