Similar papers
Open access
Sep 2026
EBRB–LLM Hybrid Architectures for Trustworthy Agentic AI: Vision, Opportunities, and Challenges
This position paper argues that EBRB could serve as a core safety and reasoning governor within LLM-based agentic pipelines, yielding hybrid systems to better align agentic AI systems with emerging regulatory and ethical requirements for Trustworthy AI (TAI) in high-risk settings.
Jun Liu, Chris D. Nugent, De-Hu Yu et al.
· International Journal of Com... · 0 citations
Review
Open access
Aug 2026
From Language Models to Agentic AI: A Survey of Autonomous, Action-Enabled, and Collaborative LLM Agents
A unified, taxonomy-driven, and deployment-oriented survey of agentic AI systems, synthesizing recent advances through a modular reference architecture and a four-dimensional taxonomy that characterizes agents along the axes of autonomy, tool use, collaboration, and safety–governance is presented.
Sparsh Bajoria, Shreyanshu Ranjan, Adhitya M et al.
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Preprint
Aug 2026
From Atomic to Agentic: Towards Interpretable Evaluation of LLMs'Agentic Mathematical Capabilities
Experiments reveal that models with similar end-to-end accuracy can exhibit markedly different agentic capability profiles, demonstrating that process-level evaluation is crucial for interpreting the true potential of LLMs and guiding the development of next-generation mathematical agents.
Jiayi Kuang, Ying-Hui Li, Yun-Ze Song et al.
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Towards Hierarchical Cyber Defense with Large Language Models: From Planning to Execution
An autonomous cyber defender trained with reinforcement learning (RL) is typically tied to the network on which it was trained, limiting its ability to generalize as network scale changes. Hierarchical RL reduces decision complexity by separating strategic targeting from tactical execution, but it does not eliminate th...
Harshith Doppalapudi, Nathaniel D. Bastian, Ankit Shah
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Conference
Aug 2026
Agentic AI Architectures for Autonomous Decision-Making in Dynamic Environments
The increasing complexity of real-world systems has revealed the shortcomings of traditional artificial intelligence models that usually base on a set of rules or offline training. These strategies fail to work well in flexible settings where the conditions keep on changing and decisions are made on the fly. This artic...
Sanjay Tiwari, Isha Sharma, Ankita et al.
· 2026 6th International Confe... · 0 citations
DeReAct: Decomposed Reasoning and Acting for Reliable AI Agents
ReAct-based agents typically rely on a single LLM policy to propose actions, interact with the environment, and decide when a task is complete. This coupling makes action authorization and completion control difficult to enforce independently, allowing errors to propagate and unsupported completion claims to terminate...
Ajay Vohra, Tao Chen, Neeti Narayan et al.
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