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20242026
most citedScaling Generalist Data-Analytic Agents

1 citations · 1 across the 10 of their papers we have counts for

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cs.AI2026

Evolving Together, Failing Apart: The Skill-Policy Co-evolution Trap in Self-Improving Agents

Yujie Feng, Xiaobin Wang, Jinluan Yang +5

Skill-policy co-evolution, in which an agent's policy model and its skill bank improve each other during training, is a promising paradigm for self-improving agents. Current evalua…

cs.CL2026

SkillComposer: Learning to Evolve Agent Skills for Specification and Generalization

Qi Zhang, Zhaopeng Feng, Xiaonan Shi +8

Agent skills, which consist of reusable strategies that guide agent reasoning and action, have shown strong potential for improving model capability at inference time. However, cur…

cs.CL2026

Dial HEALTHDIAL for Advice: A Multilingual and Multi-Parallel Spoken Dialogue Dataset for Knowledge-Grounded Information Seeking

Songbo Hu, Yinhong Liu, Ej Zhou +5

Creating spoken dialogue datasets is methodologically challenging, and these challenges are amplified when the goal is to build multilingual, multi-parallel datasets at scale. This…

cs.CL2026

AgentSwing: Adaptive Parallel Context Management Routing for Long-Horizon Web Agents

Zhaopeng Feng, Liangcai Su, Zhen Zhang +16

As large language models (LLMs) evolve into autonomous agents for long-horizon information-seeking, managing finite context capacity has become a critical bottleneck. Existing cont…

cs.CL2026

U-Fold: Dynamic Intent-Aware Context Folding for User-Centric Agents

Jin Su, Runnan Fang, Yeqiu Li +5

Large language model (LLM)-based agents have been successfully deployed in many tool-augmented settings, but their scalability is fundamentally constrained by context length. Exist…