Category: FAANG

Large-scale commercial search systems optimize for relevance to drive successful sessions that help users find what they are looking for. To maximize relevance, we leverage two complementary objectives: behavioral relevance (results users tend to click or download) and textual relevance (a result’s semantic fit to the query). A persistent challenge is the scarcity of expert-provided …

Read more “Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments”

With the dawn of the gen AI era, businesses are facing unprecedented opportunities for transformative products, demanding a strategic shift in their technology infrastructure. A few years ago, PayPal, a digital-native company serving hundreds of millions of customers, faced a significant challenge. After 25 years of success in expanding services and capabilities, we’d created complexity …

Read more “PayPal’s historically large data migration is the foundation for its gen AI innovation”

Prior studies investigating the internal workings of LLMs have uncovered sparse subnetworks, often referred to as circuits, that are responsible for performing specific tasks. Additionally, it has been shown that model performance improvement through fine-tuning often results from the strengthening of existing circuits in the model. Taken together, these findings suggest the possibility of intervening …

Read more “Constructive Circuit Amplification: Improving Math Reasoning in LLMs via Targeted Sub-Network Updates”

Large Language Models (LLMs) can be adapted to extend their text capabilities to speech inputs. However, these speech-adapted LLMs consistently underperform their text-based counterparts—and even cascaded pipelines—on language understanding tasks. We term this shortfall the text-speech understanding gap: the performance drop observed when a speech-adapted LLM processes spoken inputs relative to when the original text-based …

Read more “Closing the Gap Between Text and Speech Understanding in LLMs”

Reasoning and planning are the bedrock of intelligent AI systems, enabling them to plan, interact, adapt, and ultimately, operate independently. At Apple, understanding and advancing reasoning capablilities in AI systems has long been an area of active research, and has resulted in numerous publications that both explore new techniques to advance the frontier of reasoning, …

Read more “Apple Workshop on Reasoning and Planning 2025”

Avneesh Saluja, Santiago Castro, Bowei Yan, Ashish Rastogi Introduction Netflix’s core mission is to connect millions of members around the world with stories they’ll love. This requires not just an incredible catalog, but also a deep, machine-level understanding of every piece of content in that catalog, from the biggest blockbusters to the most niche documentaries. As …

Read more “MediaFM: The Multimodal AI Foundation for Media Understanding at Netflix”