activity
20232026
most citedPhi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs

7 citations · 12 across the 21 of their papers we have counts for

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6 papers · 1 filter

cs.AI2026

AesCode: Aesthetic Code Generation with Decoupled Cross-Modal Rewards

Xin Gao, Yifan Yang, Xin Li +13

Generating information-rich visual artifacts such as slides, posters, and dashboards as code is appealing because the outputs are structured, editable, and verifiable. Yet code mod…

cs.AI2026

OpenFC: Learning Verification Policies towards Open-Search Fact Checking

Xinming Wang, Kaixiang Qiu, Yansong Lin +5

Open-search fact checking is not merely retrieval followed by classification, but a sequential decision problem in which every query, source visit, and stopping decision reshapes t…

cs.AI2026

OmniFysics-Nano-V2 Technical Report: Understanding the Physical World Across Modalities

Yizhou Liu, Jinghang Han, Kaixiang Qiu +8

Omni-modal models have expanded multimodal interaction across vision, audio, speech, and language. However, their training is predominantly organized around semantic descriptions a…

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.AI2026★ 1 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-…