Chenxin Li

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Chenxin Li | 李宸鑫

Hi! I'm Chenxin "Jason" Li, a final-year Ph.D. candidate at The Chinese University of Hong Kong (CUHK).

I work on scaling the agentic stack of LLM/VLM for digital automation:

(i) CLI agents that operate creative software (HuggingFace Spaces, PhotoShop, Blender, etc.) and automate model training pipelines (IR3D-Bench, JarvisArt, JarvisIR, Doubao RSI Flywheel); (ii) Visual coding agents that generate charts, web pages, and structured visualizations — code-to-chart/web (Seed 1.8); (iii) GUI agents for screen-level software automation (UI-TARS-2). Incidentally, these directions parallel Anthropic's bet on Claude Code/Cowork, Claude Design, and Claude Computer Use.

Throughout my fulfilling years in Master and Ph.D., I have gained extensive industry exposure through internships at ByteDance Seed, Tencent AI, Ant Ling, Giga AI, AMD, Hedra AI, JoinQuant, etc. Spanning Agent, World Model, and Quant, this trajectory has trained me to stay open to change and to spot emerging opportunities with a cross-disciplinary perspective. I also visited UT Austin and UMD for research.

I anticipate graduating in the summer of 2026 and am interested in industrial positions (Profile). Please feel free to reach out via email (chenxinli@link.cuhk.edu.hk) or WeChat (jasonchenxinli).

LinkedIn | Scholar | GitHub | X

Selected Work

* Equal contribution, † Project Leader, ‡ Corresponding author

Seed-1.8: Towards Generalized Real-World Agency

ByteDance Seed Team

[Project] [Model Card]

Contributed to agent post-training through visual coding, IPython-based tool use.

UI-TARS-2: Advancing GUI Agent with Multi-Turn Reinforcement Learning

ByteDance Seed Team

[Project] [Report]

Contributed to GUI grounding flywheel and agent post-training.

Ling: Open-sourced LLM with MoE Architecture by InclusionAI

Ant Group InclusionAI Team

[Project]

Contributed to long-context post-training and LLM-as-Judge verifiers.

IR3D-Bench: Evaluating Vision-Language Model Scene Understanding as Agentic Inverse Rendering

Parker Liu*, Chenxin Li*, Zhengxin Li, Yipeng Wu, Wuyang Li, Zhiqin Yang, Zhenyuan Zhang, Yunlong Lin, Sirui Han, Brandon Y. Feng

NeurIPS 2025

[Project] [Paper] [Code]

An agentic inverse-rendering framework that closes the loop from visual understanding to structured code generation, Blender execution, and environment feedback.

SAM-Agent: Empowering Interactive Image Segmentation with Multi-turn Agentic Reinforcement Learning

Shengyuan Liu*, Chenxin Li*, Liuxin Bao, Qi Yang, Wanting Geng, Boyun Zheng, Wenting Chen, Houwen Peng, Yixuan Yuan

Preprint

[Paper] [Code]

An interactive segmentation agent system that learns multi-turn correction actions with process rewards for iterative image refinement.

U-KAN: U-KAN Makes Strong Backbone for Image Segmentation and Generation

Chenxin Li*, Xinyu Liu*, Wuyang Li*, Cheng Wang*, Hengyu Liu, Yixuan Yuan

AAAI 2025

[Project] [Paper] [Code] Top 1 most influential papers in AAAI 2025

Integrating Linear Attention mechanism like KAN into vision backbone

JarvisIR: Elevating Autonomous Driving Perception with Intelligent Image Restoration

Yunlong Lin*, Zixu Lin*, Haoyu Chen*, Chenxin Li*, Sixiang Chen, Kairun Wen, Yeying Jin, Wenbo Li, Xinghao Ding‡

CVPR 2025

[Project] [Paper] [Code]

A restoration agent system that schedules structured recovery steps across expert modules and optimizes execution outcomes.

JarvisArt: Liberating Human Artistic Creativity via an Intelligent Photo Retouching Agent

Yunlong Lin*, Zixu Lin*, Kunjie Lin*, Chenxin Li*, Haoyu Chen, Zhongdao Wang, Xinghao Ding†, Wenbo Li, Shuicheng Yan†

NeurIPS 2025

[Project] [Paper] [Code]

An artistic editing agent system that plans multi-step retouching commands and coordinates expert models for execution-quality image refinement.

Visual Large Language Model Fine-Tuning via Simple Parameter-Efficient Modification

Mengjiao Li, Zhiyuan Ji, Chenxin Li†, Lianliang Nie, Zhiyang Li, Masashi Sugiyama

EMNLP 2024

[Project] [Paper] [Code]

Simple yet efficient parameter-efficient fine-tuning for VLM alignment.

Selected Experience

LLM Agent

ByteDance Seed: Agent for CLI, visual coding and GUI (Seed-1.6 / Seed-1.8 / UI-TARS-2)
Tencent AI Lab: Agent execution loop, code generation and environment-grounded RL (IR3D-Bench)
Ant Ling: Agent memory, context compression and output verification (Ling-Pilot)
AMD: Visual token compression for efficient VLMs

World Model

Giga AI: World-model agent for 3D environments and tool-augmented training (Giga Brain-0)
Hedra AI: Omnimodal attention architecture for commercial digital-human generation (Hedra Character-3)

Quant, Real-world Utility

JoinQuant: RL post-training of LLMs for quant alpha-factor mining; agent evaluation on financial data

ScholaGO (Co-founder): LLM-backend Education Startup

Co-founded ScholaGO Education Technology Company Limited (学旅通教育科技有限公司) to build LLM-powered education products that turn static content into immersive, interactive, multimodal learning experiences. Grateful to receiving funding from HKSTP, HK Tech 300, and Alibaba Cloud.

Professional Activities

Workshop Organizer: AIM-FM: Advancements In Foundation Models Towards Intelligent Agents (NeurIPS 2024)
Talks: "UKAN" at VALSE Summit (Jun 2025) and DAMTP, University of Cambridge (Jul 2024)
Conference Reviewer: ICLR, NeurIPS, ICML, CVPR, ICCV, ECCV, EMNLP, AAAI, ACM MM, MICCAI, BIBM
Journal Reviewer: Nature Machine Intelligence, PAMI, TIP, DMLR, PR, TNNLS

Beyond Work

Reading: I dedicate substantial time to reading, especially history, philosophy, and sociology, which shapes my perspective on what AGI should be from first principles.

Investment: Investment is real-world RL: returns provide fast feedback to iteratively improve individual decision policy. Recently, I am fascinated by the idea that how to (i) build benchmarks for LLMs that quantify real-world investment utility (in the similar spirit of GPT-5.2's gdpeval benchmark), and (ii) extending quantitative financial metrics to more general event and trend forecasting.