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20242026
most citedxLAM: A Family of Large Action Models to Empower AI Agent Systems

5 citations · 11 across the 15 of their papers we have counts for

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cs.CL2026

CLIFT: Conformal Self-Verification for Web Agent Training and Test-Time Scaling

Yifan Zhang, Yutong Dai, Viraj Prabhu +3

Open-source web agents are now strong enough to execute realistic browser tasks, but training them with reinforcement learning still depends on weak supervision: binary task succes…

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.AI2026

Co-Evolving Harnesses and Models: On-Policy Correction Helps Weaker Models Catch Up Where Imitation Fails

Zhou Yu, Bin Bi, Shiva Kumar Pentyala +8

Agent harnesses (the system prompt, tool set, execution hooks, and context-management scaffolding around a model) are a critical determinant of agentic task success. Automated harn…

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.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…