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Home/Authors/Qifan Wang

Qifan Wang

3 indexed papers

Recent (6 mo)
3
With code
0
Influential cites
0
Benchmarked
0

Publications per year

3
26

Top categories

AI×2Crypto×1ML×1

Frequent co-authors

Yangbo Wei1×
Zhen Huang1×
Shaoqiang Lu1×
Junhong Qian1×
Chen Wu1×
Lei He1×

Research Timeline

2026
TrEEStealer: Stealing Decision Trees via Enclave Side Channels

The paper introduces TrEEStealer, a novel side-channel attack that efficiently steals Decision Trees (DTs) protected within Trusted Execution Environments (TEEs), demonstrating that TEEs fail to provide adequate protection against control-flow leakage.

SkillSmith: Co-Evolving Skills and Tools for Self-Improving Agent Systems

SkillSmith is a synergy-aware framework that jointly co-evolves skills and tools, significantly improving self-improving agent systems by modeling skill-tool interactions and diagnosing failures.

DAG-MoE: From Simple Mixture to Structural Aggregation in Mixture-of-Experts

The paper proposes DAG-MoE, a novel sparse Mixture-of-Experts framework that replaces standard weighted-sum aggregation with structural aggregation to enhance model performance and enable multi-step reasoning.

Highlighted terms show continued research focus across papers

Papers

cs.AIRecentMay 31, 2026

SkillSmith: Co-Evolving Skills and Tools for Self-Improving Agent Systems

Yangbo Wei, Zhen Huang, Shaoqiang Lu, Junhong Qian +3 more

SkillSmith is a synergy-aware framework that jointly co-evolves skills and tools, significantly improving self-improving agent systems by modeling skill-tool interactions and diagnosing failures.

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cs.AIRecentMay 31, 2026

DAG-MoE: From Simple Mixture to Structural Aggregation in Mixture-of-Experts

Jiarui Feng, Hanqing Zeng, Karish Grover, Ruizhong Qiu +10 more

The paper proposes DAG-MoE, a novel sparse Mixture-of-Experts framework that replaces standard weighted-sum aggregation with structural aggregation to enhance model performance and enable multi-step r…

View →
cs.CRcs.LGRecentApr 20, 2026

TrEEStealer: Stealing Decision Trees via Enclave Side Channels

Jonas Sander, Anja Rabich, Nick Mahling, Felix Maurer +4 more

The paper introduces TrEEStealer, a novel side-channel attack that efficiently steals Decision Trees (DTs) protected within Trusted Execution Environments (TEEs), demonstrating that TEEs fail to provi…

View →