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#machine learning Preprint Oct 2026

DecepEval: A Benchmark for Evaluating Deception in LLM Agents

As large language model (LLM) agents become increasingly autonomous, they may pursue task performance through deception, raising concerns about their reliable deployment. Existing evaluations show that LLM agents can deceive, but often examine isolated scenarios or narrowly defined conditions, limiting systematic under...

Yi-Ming Xu, Hong Yu, Bei-Hua Yang et al. · 0 citations
#artificial intelligence Preprint Sep 2026

When Should Agents Check External State? Budgeting Observations for Stored Intentions

BudgetPM offers two policy variants that share a hard-budget executor that distills full-episode hindsight schedules into a lightweight policy that decides when to spend or reserve capacity using only pre-query information at deployment, and outperforms adapted Mem0 and PMA workflows.

Zheng-Kun Di, Bin Shi, Kai Sun et al. · 0 citations

Generalist Graph Anomaly Detection via Prototype-Based Distillation

ProMoS is introduced, the first unsupervised generalist GAD framework, which detects anomalies by modeling the abundant normality in unlabeled data, and proposes prototype-guided soft-label distillation to align teacher and student in a shared prototype space, enhancing cross-graph generalizability.

Yiming Xu, Zihan Chen, Z. Peng et al. · 0 citations

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