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Wangze Ni

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Preprint Aug 2026

ActBench: Self-Evolving Benchmark of Behavioral Safety in Cowork Agents

Cowork agents may complete benign tasks while disclosing protected data, manipulating unauthorized state, invocate unauthorized API. We define behavioral safety and introduce ActBench, a self-evolving benchmark that evaluates such behavior risk from execution trajectories rather than final responses. Each case pairs a...

H. Yao, Yimin Liu, Meihui Chen et al. · 0 citations
Preprint Sep 2026

Exploiting Residual Reachability for Cross-Model Migration of Graph-Based Indexes in Approximate Nearest Neighbor Search

Approximate nearest neighbor search (ANNS) underpins large-scale vector retrieval in search, recommendation, and retrieval-augmented generation. Graph-based indexes have demonstrated state-of-the-art search performance for ANNS. They connect each corpus vector to a small set of nearby or navigationally useful vertices...

Bao-Yuan Gu, Xiao-Yao Zhong, Jia-Bao Jin et al. · 0 citations
Jul 2026

DREvo: Distilling Recalibrated Historical Experience for Harness Self-Evolution

A new harness self-evolution method, named DREvo, is proposed, which integrates function-level evidence anchoring, state-dependent evidence recalibration, and role-conditioned search intent distillation to determine which historical evidence remains valid and where the harness should evolve next.

Hanghui Guo, Wei-Jie Shi, Zhangze Chen et al. · 4 citations
#artificial intelligence Review Sep 2026

Beyond Final Decisions: A Process-Centric Benchmark for Transparent AI-Assisted Peer Review

Peer review is central to quality control in science. However, existing evaluations of AI-assisted peer review mainly focus on the overall quality of generated reviews or the accuracy of final decisions. They therefore provide limited evidence about whether model decisions are supported by sufficient and reliable revie...

Siming Yuan, Xue-Yi Zhang, Wang-Ze Ni et al. · 0 citations

Beyond Retrieval: Learning Compact User Representations for Scalable LLM Personalization

TAP-PER (Temporal Attentive Prefix for PERsonalization), a prefix-based framework that encodes user preferences as learnable representations, avoiding the serialization of user histories into prompts and replacing heavy per-user adapters with lightweight user-state prefix embeddings, is proposed.

Heng-Jie Cao, Fan Zhang, Jiangqi Yao et al. · 1 citation
Jul 2026

MERIT: Efficient In-Place Deletion for Dynamic Graph-Based Approximate Nearest Neighbor Indexes

Graph-based indexes have become the dominant approach to approximate nearest neighbor search (ANNS) over high-dimensional data and play a crucial role in real-world applications such as retrieval-augmented generation, recommendation systems, and vector databases. Despite extensive progress in static graph construction...

Ze-Kai Wu, Jiabao Jin, Peng Cheng et al. · 0 citations

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