activity
20242026
most citedDarwinX: Evolving Agent Harnesses Through Natural Selection

2 citations · 3 across the 10 of their papers we have counts for

collaborators

12 papers

cs.CL2026

CoTrace: Data Recipes for Training Terminal Agents with Harness-Model Co-Evolution

Jixuan Chen, Jiaxin Zhang, Qinyuan Ye +11

Terminal-agent capability depends jointly on model weights and the runtime harness that formats prompts, binds tools, and handles error recovery. Existing harness-model co-evolutio…

cs.CL2026

Opera: A Verbal Critic Framework for Long-horizon Coding Agents

Kai Mei, Zhiyuan Hu, Yutong Dai +7

Long-horizon coding agents need timely corrections, yet feedback can be ineffective or even harmful when it misjudges ongoing work or fails to address the underlying problem. Exist…

cs.NE2026★ 2 cited

DarwinX: Evolving Agent Harnesses Through Natural Selection

Yifan Zhang, Yutong Dai, Juntao Tan +9

An LLM agent's capability depends not only on model weights but on its harness: prompts, tools, skills, and control flow. Self-improvement loops already edit harnesses, yet single-…

cs.SE2026

StateAct: Program State, before Pixels, for Long-Horizon Computer-Use Agents

Yan Yang, Xiangru Jian, Ziyang Luo +7

Computer-use agents are usually improved by strengthening perception: better models for reading a screenshot and choosing where to click. Yet a screenshot is only a lossy rendering…

cs.AI2026

OpenSkill: Open-World Self-Evolution for LLM Agents

Zhiling Yan, Dingjie Song, Hanrong Zhang +8

Self-evolving agents requires adaptation after deployment, but existing approaches assume a usable learning loop, such as curated skills, successful trajectories, or verifier signa…

cs.AI2026

How Far Are Vision-Language Models from Constructing the Real World? A Benchmark for Physical Generative Reasoning

Luyu Yang, Yutong Dai, An Yan +3

The physical world is not merely visual; it is governed by rigorous structural and procedural constraints. Yet, the evaluation of vision-language models (VLMs) remains heavily skew…

cs.CV2025

WALT: Web Agents that Learn Tools

Viraj Prabhu, Yutong Dai, Matthew Fernandez +8

Web agents promise to automate complex browser tasks, but current methods remain brittle -- relying on step-by-step UI interactions and heavy LLM reasoning that break under dynamic…

cs.CL2025

GUI-KV: Efficient GUI Agents via KV Cache with Spatio-Temporal Awareness

Kung-Hsiang Huang, Haoyi Qiu, Yutong Dai +2

Graphical user interface (GUI) agents built on vision-language models have emerged as a promising approach to automate human-computer workflows. However, they also face the ineffic…

cs.AI2025

SCUBA: Salesforce Computer Use Benchmark

Yutong Dai, Krithika Ramakrishnan, Jing Gu +8

We introduce SCUBA, a benchmark designed to evaluate computer-use agents on customer relationship management (CRM) workflows within the Salesforce platform. SCUBA contains 300 task…

cs.CL2025

CoAct-1: Computer-using Multi-Agent System with Coding Actions

Linxin Song, Yutong Dai, Viraj Prabhu +9

Autonomous agents that operate computers via Graphical User Interfaces (GUIs) often struggle with efficiency and reliability on complex, long-horizon tasks. While augmenting these…

cs.AI2025

GTA1: GUI Test-time Scaling Agent

Yan Yang, Dongxu Li, Yutong Dai +12

Graphical user interface (GUI) agents autonomously complete tasks across platforms (\eg, Linux) by sequentially decomposing user instructions into action proposals that iteratively…

cs.CV2024★ 1 cited

xGen-MM (BLIP-3): A Family of Open Large Multimodal Models

Le Xue, Manli Shu, Anas Awadalla +30

This paper introduces BLIP-3, an open framework for developing Large Multimodal Models (LMMs). The framework comprises meticulously curated datasets, a training recipe, model archi…