Salem Haykal

Salem Haykal

Authored Publications
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    Deep Researcher with Test-time Diffusion
    Guan Sun
    Zoey CuiZhu
    Yuanjun (Sophia) Bi
    Weiming Wen
    Hui Wan
    Chunfeng Wen
    Solène Maître
    George Lee
    Vishy Tirumalashetty
    Emily Xue
    Burak Gokturk
    2025
    Preview abstract Deep research agents, powered by Large Language Models (LLMs), are rapidly advancing; yet, their performance often plateaus when generating complex, long-form research reports using generic test-time scaling algorithms. Drawing inspiration from the iterative nature of human research, which involves cycles of searching, reasoning, and revision, we propose the Test-Time Diffusion Deep Researcher (TTD-DR). This novel framework conceptualizes research report generation as a diffusion process. TTD-DR initiates this process with a preliminary draft, an updatable skeleton that serves as an evolving foundation to guide the research direction. The draft is then iteratively refined through a "denoising" process, which is dynamically informed by a retrieval mechanism that incorporates external information at each step. The core process is further enhanced by a self-evolutionary algorithm applied to each component of the agentic workflow, ensuring the generation of high-quality context for the diffusion process. This draft-centric design guides the report writing process to be more timely and coherent while reducing information loss during the iterative search process. We demonstrate that our TTD-DR achieves state-of-the-art results on a wide array of benchmarks that require intensive search and multi-hop reasoning, significantly outperforming existing deep research agents. View details
    TFX: A TensorFlow-Based Production-Scale Machine Learning Platform
    Akshay Naresh Modi
    Chiu Yuen Koo
    Chuan Yu Foo
    Clemens Mewald
    Denis M. Baylor
    Jarek Wilkiewicz
    Levent Koc
    Lukasz Lew
    Martin A. Zinkevich
    Mustafa Ispir
    Neoklis Polyzotis
    Steven Whang
    Sudip Roy
    Sukriti Ramesh
    Vihan Jain
    Xin Zhang
    Zakaria Haque
    KDD 2017
    Preview abstract Creating and maintaining a platform for reliably producing and deploying machine learning models requires careful orchestration of many components—a learner for generating models based on training data, modules for analyzing and validating both data as well as models, and finally infrastructure for serving models in production. This becomes particularly challenging when data changes over time and fresh models need to be produced continuously. Unfortunately, such orchestration is often done ad hoc using glue code and custom scripts developed by individual teams for specific use cases, leading to duplicated effort and fragile systems with high technical debt. We present TensorFlow Extended (TFX), a TensorFlow-based general-purpose machine learning platform implemented at Google. By integrating the aforementioned components into one platform, we were able to standardize the components, simplify the platform configuration, and reduce the time to production from the order of months to weeks, while providing platform stability that minimizes disruptions. We present the case study of one deployment of TFX in the Google Play app store, where the machine learning models are refreshed continuously as new data arrive. Deploying TFX led to reduced custom code, faster experiment cycles, and a 2% increase in app installs resulting from improved data and model analysis. View details