activity
20202026
most citedHeuristic Domain Adaptation

9 citations · 12 across the 13 of their papers we have counts for

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Showing 2026Show all

7 papers · 1 filter

cs.AI2026

Beyond Endpoint Performance: Process-Level Evaluation of Self-Evolving Agents

Hongqiang Lin, Chao Liu, Xiaofan Bai +4

Self-evolving agents convert interaction feedback into persistent artifacts, such as memories or skills, which in turn guide subsequent decisions. As these artifacts are iterativel…

cs.AI2026

SkillZip Pro: Execution-Aware Dynamic Compression of Progressively Loaded Skills for Self-Evolving Agents

Xiaofan Bai, Chao Liu, Hongqiang Lin +5

Production agent skills are directory bundles, not isolated prompts. The root is loaded at activation; references, schemas, scripts, assets, and nested subskills are loaded only wh…

cs.AI2026

Rethinking Self-Evolution: A Constrained Exploration-Exploitation Process for Mitigating Skill Overfitting

Hongqiang Lin, Chao Liu, Xiaofan Bai +4

Enabling large language model (LLM) agents to accumulate and reuse experience from past interactions remains a central challenge in real-world applications. A promising solution is…

cs.AI2026

SkillZip: Evaluation-Free Skill Compression for Self-Evolving Agents by Discovering Reusable Structure

Xiaofan Bai, Hongqiang Lin, Chao Liu +4

Self-evolving agents accumulate reusable skills by appending successful procedures and failure fixes. Over time, the same requirement is often restated in several branches, example…

cs.CL2026

Yuvion LLM: An Adversarially-Aware Large Language Model for Content And AI Safety

Ting Ma, Xiufeng Huang, Benlei Cui +43

As large language models are increasingly deployed in real-world systems, safety failures can still lead to harmful outputs and dangerous misuse. We argue that the essence of safet…

cs.CV2026

Yuvion VL: A Multimodal Foundation Model for Adversarial Content and AI Safety

Shikai Qiu, Xiaowen Xu, Benlei Cui +55

General-purpose models often struggle to reliably identify and understand real-world multimodal risks, largely due to the inherent multimodal adversarial nature of content and AI s…

cs.CV2026

Rethinking Token Reduction for Large Vision-Language Models

Yi Wang, Haofei Zhang, Qihan Huang +7

Large Vision-Language Models (LVLMs) excel in visual understanding and reasoning, but the excessive visual tokens lead to high inference costs. Although recent token reduction meth…