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With AI, Everyone is a Dev. EveryDev.ai ©
    1. Home
    2. Developers
    3. Tianqi Liu

    Tianqi Liu

    Tianqi Liu is an individual researcher and developer, not an organization. He studies LLM agents and recommender systems, with emphasis on agent post-training, customization, realistic evaluation, and personalized, user-centric self-evolving agents.

    Visit Website

    At a Glance

    1Tool Listed
    4Products
    9Capabilities
    Discussions
    Focus Areas
    AI Coding Assistants
    Code Review
    LLM Evaluations
    Connect
    Latest News
    PACE-Bench project branding and publication links clarified in the repository.Oct 21, 2026
    PACE-Bench code, real-task example, and project page released publicly.Oct 20, 2026
    Markets
    • Academic and industrial AI researchers
    • Coding-agent and software-engineering-tool developers
    • Recommender-system and on-device ML researchers
    • Multimodal GUI-agent and desktop-automation researchers
    • +1 more

    AI Tools by Tianqi Liu

    (1)
    View PACE-Bench
    PACE-Bench tool icon

    PACE-Bench

    Coding Agent Benchmark for PRDs

    LLM EvaluationsAI Coding Asst.Code Review

    Discussions

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    Latest News

    10/21/2026

    PACE-Bench project branding and publication links clarified in the repository.

    github.com
    10/20/2026

    PACE-Bench code, real-task example, and project page released publicly.

    github.com
    10/02/2026

    UILoop v3 posted: What’s Missing in Screen-to-Action? introduces UI-in-the-Loop reasoning and a 26K UI Comprehension-Bench.

    arxiv.org
    06/02/2026

    DeskCraft preprint posted, presenting a 538-task benchmark for professional desktop workflows and human-in-the-loop collaboration.

    arxiv.org

    Products & Services

    4
    PACE-Bench
    2026-10-20

    An open benchmark and evaluation framework for measuring whether coding agents complete evolving product requirements in existing enterprise repositories. It reconstructs PRDs, synthesizes and validates atomic Code Points, localizes code evidence, and attributes unmet requirements to implementation and process symptoms. The project reports 43 enterprise tasks, 750 audited Code Points, 11 model–scaffold settings, and 95.2% expert-consensus agreement.

    CHORD
    2025-10-03 (arXiv); ACM Multimedia 2025 publication

    A device-cloud collaborative framework for personalized mixed-precision quantization in sequential recommendation. It uses frozen weights, channel-wise quantization, user/device-specific sensitivity analysis, and one-forward-pass customization to balance recommendation quality, compression, latency, and heterogeneous device resources.

    UILoop / UI Comprehension-Bench
    2026-04 (arXiv first posting; v3 dated 2026-10-02)

    A multimodal GUI-reasoning paradigm that inserts UI elements between screen and action in a cyclic Screen–UI Elements–Action loop. It introduces UI-element-driven reinforcement fine-tuning and a 26K-sample benchmark covering localization, semantic understanding, and practical usage.

    DeskCraft
    2026-06-02 (arXiv)

    A benchmark for desktop GUI agents on long-horizon professional creative and engineering workflows with human-in-the-loop collaboration. It covers 538 executable tasks, multiple difficulty levels, and mid-turn and post-turn interaction protocols across design, video, audio, and 3D software.

    Market Position

    As an individual researcher, Liu is positioned through research artifacts rather than a commercial company. PACE-Bench differentiates from issue- and patch-centric coding benchmarks by evaluating complete PRD fulfillment in existing enterprise repositories and linking unmet requirements to cited evidence and diagnostic signals. UILoop contrasts with direct Screen-to-Action GUI agents by explicitly modeling Screen–UI Elements–Action reasoning, while DeskCraft targets long-horizon professional workflows and human interaction beyond short GUI tasks. CHORD targets personalized, device-aware sequential recommendation rather than one-size-fits-all cloud inference.

    Target Markets

    Industries & Segments
    • Academic and industrial AI researchers
    • Coding-agent and software-engineering-tool developers
    • Recommender-system and on-device ML researchers
    • Multimodal GUI-agent and desktop-automation researchers
    • Teams evaluating enterprise software-development agents
    Use Cases
    • Evaluating coding agents on feature delivery in mature enterprise repositories
    • Diagnosing incomplete implementation, integration/registration, verification, and contract-alignment failures
    • Personalized sequential recommendation on heterogeneous mobile or edge devices
    • Efficient model compression and device-cloud collaborative inference
    • Interpretable multimodal GUI automation and UI comprehension
    • Benchmarking desktop agents on professional design, video, audio, and 3D workflows

    History & Milestones

    2026.04

    Coauthored paper What’s Missing in Screen-to-Action? Towards a UI-in-the-Loop Paradigm for Multimodal GUI Reasoning, accepted to ACL 2026 according to his homepage.

    2026.06–present

    Internship at Alibaba Taobao & Tmall Group; the PACE-Bench paper identifies the work as conducted during this internship.

    2026.10.20

    Released the PACE-Bench code, public real-task example, documentation, and project page on GitHub.

    2026.10

    Received a National Scholarship, according to his official homepage.

    2025.03–2025.05

    Research internship at Huawei Noah’s Ark Lab.

    Key Capabilities

    9
    PRD reconstruction and requirement-preserving scope definition
    Atomic, weighted Code Point synthesis and contract validation
    Code-evidence localization and constrained semantic judgment
    Symptom–signal attribution for diagnosing coding-agent delivery gaps
    Personalized mixed-precision on-device recommendation with device-cloud collaboration
    Channel-wise and layer-wise quantization strategy generation

    Integrations & Partnerships

    Platform Integrations

    • PACE-Bench is publicly distributed through GitHub with Python source, documentation, tests, prompts, benchmark examples, and a static project website.
    • PACE-Bench is designed to integrate with a team’s own coding-agent runner, repository access, configured model service, and saved criterion judgments.
    • UILoop source code is linked from the paper at github.com/zjukg/UILoop.
    • DeskCraft code, tasks, and data are announced for release at github.com/mrwwk/DeskCraft.

    Key Partnerships

    PACE-Bench was built from Alibaba Taobao & Tmall Group’s coding-agent platform during Liu’s internship; the paper lists Alibaba Taobao & Tmall Group and Zhejiang University affiliations.
    UILoop lists Zhejiang University and the ZJU–Ant Group Joint Lab of Knowledge Graph among the author affiliations.

    Connect

    Website
    tianqiliu1.github.io/
    GitHub
    TianqiLiu1

    AI Topics

    3

    Tianqi Liu focuses on these topics:

    AI Coding Assistants(1)
    Code Review(1)
    LLM Evaluations(1)
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