Aashu Singh
Senior Staff Software Engineer, Meta
San Francisco, California, United States
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Aashu Singh is a Senior Staff Software Engineer at Meta, where he leads cutting-edge initiatives in Multimodal Large Language Models for content understanding across Facebook and Instagram recommendation systems. With over 9 years at Meta and significant expertise in machine learning, Aashu specializes in developing AI solutions that bridge the gap between content comprehension and personalized recommendations.
In his current role within the Meta AI, Aashu is pioneering approaches that leverage multimodal LLMs to enhance relevance throughout Meta's recommendation stack. His work transforms how machine learning systems interpret and process content across different modalities to deliver more intuitive and personalized user experiences.
Aashu has co-authored several influential publications in the field of multimodal AI, including "Transfer between Modalities with MetaQueries" (2025) and "CompCap: Improving Multimodal Large Language Models with Composite Captions" (2024).
Previously, Aashu made substantial contributions to Meta's advertising ecosystem, developing models for Ads Retrieval, Ranking and Dynamic Ads - a hyper personalized ads for users.
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From Correlation to Cognition: The LLM Revolution in Recommendation Systems
This talk explores how the integration of Large Language Model reasoning capabilities is fundamentally transforming industry-scale recommendation systems, shifting from traditional pattern-matching approaches to cognitive frameworks that deliver unprecedented personalization and transparency.
Recent research demonstrates that LLM reasoning, particularly through Chain-of-Thought (CoT) prompting, significantly enhances recommendation quality by addressing the inherent subjectivity and personalization challenges of recommendation tasks. Rather than simply matching users to items based on statistical correlations, LLMs now employ sophisticated reasoning processes to assess user preferences and generate appropriately ranked recommendations. They also provide transparent explanations that elucidate their suggestions in natural language, dramatically improving system clarity.
The presentation will delve into recent industry implementations and research developments from early 2025 and look at how they have leveraged LLM-powered reasoning to enhance recommendation quality through techniques such as query understanding, metadata enrichment, and innovative evaluation frameworks. We'll examine how multi-agent LLM orchestration enables collaborative reasoning processes where models share insights to generate more accurate conclusions, and explore key paradigms emerging in current research.
The talk will conclude with insights on how LLM reasoning is opening new frontiers for personalization without requiring curated gold references or human raters, pointing toward a future where recommendation systems don't just predict what users want but understand why they want it—ultimately creating more meaningful and effective user experiences.
Beyond Precision: Building Trust and Safety in AI-Powered Content Recommendation
The future of AI-powered content recommendation demands more than just technical precision—it requires building systems that users can trust. I will share frameworks and methodologies for evaluating recommendation quality beyond traditional metrics, incorporating dimensions of transparency, accountability, and user agency. Through case studies from the industry, attendees will gain insights into effectively navigating trade-offs between innovation and responsible deployment, establishing appropriate human oversight mechanisms, and designing recommendation systems that not only understand content but respect the complex human values and preferences they serve. This session offers practical guidance for organizations seeking to harness the power of multimodal LLMs while maintaining robust ethical standards in their recommendation practices.
Aashu Singh
Senior Staff Software Engineer, Meta
San Francisco, California, United States
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