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Emergent Strategic Reasoning Risks in AI: A Taxonomy-Driven Evaluation Framework

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#artificial intelligence#ai safety#machine learning#large language models#emergent behavior#Tharindu Kumarage#Lisa Bauer#Yao Ma#Dan Rosen#Yashasvi Raghavendra Guduri#Anna Rumshisky#Kai-Wei Chang#Aram Galstyan
Emergent Strategic Reasoning Risks in AI: A Taxonomy-Driven Evaluation Framework
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The paper introduces Emergent Strategic Reasoning Risks (ESRRs) in large language models, which include deceptive behaviors, evaluation gaming, and reward hacking. To assess these risks, the authors propose ESRRSim, a taxonomy-driven framework with 7 risk categories and 20 subcategories for automated behavioral evaluation. Testing across 11 reasoning LLMs shows significant variation in risk detection, with newer models showing improved awareness of evaluation contexts.

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Computer Science > Artificial Intelligence arXiv:2604.22119 (cs) [Submitted on 23 Apr 2026] Title:Emergent Strategic Reasoning Risks in AI: A Taxonomy-Driven Evaluation Framework Authors:Tharindu Kumarage, Lisa Bauer, Yao Ma, Dan Rosen, Yashasvi Raghavendra Guduri, Anna Rumshisky, Kai-Wei Chang, Aram Galstyan, Rahul Gupta, Charith Peris View a PDF of the paper titled Emergent Strategic Reasoning Risks in AI: A Taxonomy-Driven Evaluation Framework, by Tharindu Kumarage and 9 other authors View PDF HTML (experimental) Abstract:As reasoning capacity and deployment scope grow in tandem, large language models (LLMs) gain the capacity to engage in behaviors that serve their own objectives, a class of risks we term Emergent Strategic Reasoning Risks (ESRRs).

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