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20242026
most citedBuilding Self-Evolving Agents via Experience-Driven Lifelong Learning: A Framework and Benchmark

1 citations · 1 across the 16 of their papers we have counts for

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cs.AI2026

Agents-K1: Towards Agent-native Knowledge Orchestration

Zongsheng Cao, Bihao Zhan, Jinxin Shi +25

Current LLM-based research agents have advanced through agent orchestration, yet largely overlook scientific knowledge orchestration. Existing works often reduce papers to abstract…

cs.AI2026

Anything2Skill: Compiling External Knowledge into Reusable Skills for Agents

Qianjun Pan, Yutao Yang, Junsong Li +5

Retrieval-augmented generation (RAG) enables agents to access external knowledge at inference time, but it primarily retrieves fragmented declarative evidence, leaving agents to re…

cs.AI2026

Forget What's Sensitive, Remember What Matters: Token-Level Differential Privacy in Memory Sculpting for Continual Learning

Bihao Zhan, Jie Zhou, Junsong Li +9

Continual Learning (CL) models, while adept at sequential knowledge acquisition, face significant and often overlooked privacy challenges due to accumulating diverse information. T…

cs.AI2026

Can Heterogeneous Language Models Be Fused?

Shilian Chen, Jie Zhou, Qin Chen +4

Model merging aims to integrate multiple expert models into a single model that inherits their complementary strengths without incurring the inference-time cost of ensembling. Rece…

cs.AI2026

Contextual Multi-Objective Optimization: Rethinking Objectives in Frontier AI Systems

Jie Zhou, Qin Chen, Liang He

Frontier AI systems perform best in settings with clear, stable, and verifiable objectives, such as code generation, mathematical reasoning, games, and unit-test-driven tasks. They…

cs.AI2026

Building Self-Evolving Agents via Experience-Driven Lifelong Learning: A Framework and Benchmark

Yuxuan Cai, Yipeng Hao, Jie Zhou +14

As AI advances toward general intelligence, the focus is shifting from systems optimized for static tasks to creating open-ended agents that learn continuously. In this paper, we i…