most citedSkillOpt: Executive Strategy for Self-Evolving Agent Skills

1 citations · 1 across the 5 of their papers we have counts for

collaborators

18 papers

cs.AI2026

Ideas Have Genomes: Benchmarking Scientific Lineage Reasoning and Lineage-Grounded Idea Generation

Yifan Zhou, Qihao Yang, Yan Li +14

Scientific ideas rarely start from a blank page. They inherit mechanisms, repair known limitations, and recombine pieces of earlier work, much like biological genomes. Current benc…

cs.CV2026

SpaceDG: Benchmarking Spatial Intelligence under Visual Degradation

Xiaolong Zhou, Yifei Liu, Ziyang Gong +8

Multimodal Large Language Models (MLLMs) have made rapid progress in spatial intelligence, yet existing spatial reasoning benchmarks largely assume pristine visual inputs and overl…

cs.CV2026

CrossEarth-Gate: Fisher-Guided Adaptive Tuning Engine for Efficient Adaptation of Cross-Domain Remote Sensing Semantic Segmentation

Shilei Cao, Ziyang Gong, Hehai Lin +10

In Remote Sensing (RS), Parameter-Efficient Fine-Tuning (PEFT) has emerged as a key approach to activate the generalizable representation ability of foundation models for downstrea…

cs.AI2026

Breaking Bad Molecules: Are MLLMs Ready for Structure-Level Molecular Detoxification?

Fei Lin, Ziyang Gong, Cong Wang +9

Toxicity remains a leading cause of early-stage drug development failure. Despite advances in molecular design and property prediction, the task of molecular toxicity repair, gener…

cs.AI20261 cited

SkillOpt: Executive Strategy for Self-Evolving Agent Skills

Yifan Yang, Ziyang Gong, Weiquan Huang +12

Agent skills today are hand-crafted, generated one-shot, or evolved through loosely controlled self-revision, none of which behaves like a deep-learning optimizer for the skill, an…

cs.AI2026

From Raw Experience to Skill Consumption: A Systematic Study of Model-Generated Agent Skills

Zisu Huang, Jingwen Xu, Yifan Yang +13

Language agents increasingly improve by reusing \emph{skills} -- structured procedural artifacts distilled from past experience. In particular, \emph{domain-level} and \emph{model-…