most citedSkillOpt: Executive Strategy for Self-Evolving Agent Skills

1 citations · 1 across the 11 of their papers we have counts for

collaborators

11 papers

cs.AI2026

ReDeck: Step-Level Render-Grounded Refinement for Document-to-Slide Generation

Muzhao Tian, Zezi Zeng, Yifan Yang +14

Document-to-slide generation is challenging because slides are dense editable artifacts that require both faithful content selection and precise spatial layout. Recent slide agents…

cs.CL2026

DocAtlas: Long-Document Understanding as Mutable-State Interaction

Hongchen Wei, Yuanzhe Wang, Bei Liu +8

Long-document understanding requires models to find and combine evidence across many pages, layouts, tables, figures, and charts. Existing retrieval-augmented systems usually selec…

cs.CL2026

XL-DocBench: Benchmarking Evidence-Grounded Extra-Long Document Understanding

Hongchen Wei, Yuanzhe Wang, Bei Liu +9

Real-world document tasks often ask professionals to answer questions from annual reports, regulations, clinical guidelines, and technical manuals that span hundreds or thousands o…

cs.SE2026

RESOURCE2SKILL: Distilling Executable Agent Skills from Human-Created Multimodal Resources

Yijia Fan, Zonglin Di, Zimo Wen +8

Skills are a useful abstraction for software agents, turning human and agent experience into reusable procedural knowledge. Yet existing skill libraries are mostly hand-written, te…

cs.AI2026

Token Predictors Are Not Planners: Building Physically Grounded Causal Reasoners

Zheng Lu, Mingqi Gao, Qinlei Xie +8

Current benchmarks for embodied vision-language planning often favor linguistic next-token prediction over physically grounded next-state reasoning. This rewards models that mimic…

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…