arXiv AI June 18, 2026 By Hatim Chergui, Claudia Carballo Gonz\'alez, Farhad Rezazadeh, Merouane Debbah

Mitigating Anchoring Bias in LLM-Based Agents for Energy-Efficient 6G Autonomous Networks

arXiv:2606. 18272v1 Announce Type: cross Abstract: This paper presents an autonomous agentic resource negotiation framework designed to enable zero-touch network slicing in 6G architectures using Large Language Model (LLM) agents.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Sep 23

Toward an Unbiased Collective Memory for Efficient LLM-Based Agentic 6G Cross-Domain Management

arXiv:2509.26200v2 Announce Type: replace-cross Abstract: Agentic artificial intelligence is a candidate enabler of Level-4 autonomy in sixth-generation (6G) networks, but agents reasoning over a sha...

By Hatim Chergui, Farhad Rezazadeh, Miguel Catalan Cid, Pouria Sayyad Khodashenas, Daniel Camps Mur, Christos Verikoukis

llms agents safety

arXiv AI

Sep 16

End-to-End Latency-Minimizing and Load-Balanced Request Scheduling for Edge LLM Inference in Agentic AI Services

arXiv:2609.17193v1 Announce Type: new Abstract: Large language model (LLM)-powered agentic AI services increasingly demand low-latency inference, motivating the deployment of LLMs across distributed...

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llms agents efficiency

arXiv AI

Aug 11

Dynamic Coalition Formation and Communication Pricing in Skill-Based Agentic AI Systems

arXiv:2608. 07532v1 Announce Type: new Abstract: Modern agentic AI systems combine multiple large language model agents with heterogeneous skills, yet most architectures either fix communication in advance or allow full broadcast.

By Mojtaba Eslami

llms agents efficiency benchmarks

arXiv Machine Learning

Jul 30

Think Short, Defer Smart, Act, and Repeat: Calibrated Reasoning and Uncertainty-Aware Deferral for Edge LLM Agents

arXiv:2607. 26865v1 Announce Type: cross Abstract: LLM agents following the ReAct paradigm are promising enablers of complex multi-step tasks, including multi-hop question answering, code generation, and control of physical AI systems.

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llms agents nlp robotics benchmarks

arXiv AI

Jun 3

Libra: Efficient Resource Management for Agentic RL Post-Training

arXiv:2606. 03077v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become a standard post-training paradigm for large language models (LLMs), extending beyond preference alignment to complex reasoning and multi-turn agentic behaviors.

By Kaiwen Chen, Xin Tan, Jingzong Li, Hong Xu

llms agents reinforcement-learning efficiency safety

arXiv Machine Learning

Jul 31

Real-Time Hard Peak Age-of-Information Safety with No-Regret Learning

arXiv:2607. 27626v1 Announce Type: new Abstract: Safety-critical IoT systems such as industrial closed-loop control, V2X coordination, and remote teleoperation require every sensor's peak Age of Information (peak AoI, also abbreviated PAoI) to stay below a hard per-slot deadline, not merely an average bound.

By Wentao Zhang, Wentao Mo

reinforcement-learning safety

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Toward an Unbiased Collective Memory for Efficient LLM-Based Agentic 6G Cross-Domain Management

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