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.
At a Glance
- 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)PACE-Bench
Coding Agent Benchmark for PRDs
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Latest News
PACE-Bench project branding and publication links clarified in the repository.
PACE-Bench code, real-task example, and project page released publicly.
UILoop v3 posted: What’s Missing in Screen-to-Action? introduces UI-in-the-Loop reasoning and a 26K UI Comprehension-Bench.
DeskCraft preprint posted, presenting a 538-task benchmark for professional desktop workflows and human-in-the-loop collaboration.
Products & Services
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.
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.
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.
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
- 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
- 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