PERA: A Perceive-Reason-Act Interface Bridging Sensing, Cognitive Reasoning, and Trustworthy Agentic Response for 6G

2026-07-18

AI Summary

This work addresses the challenges posed by cross-layer dynamics in 6G networks, where conventional machine learning approaches fall short and large language models (LLMs) lack the ability to perceive heterogeneous wireless multimodal data or provide trustworthy responses. To overcome these limitations, the paper introduces the first-of-its-kind Perceive-Reason-Act (PERA) intelligence paradigm, which integrates Large Wireless AI Models (LWAMs) with LLMs into a unified three-tier architecture. This framework jointly enables multimodal perception, generative cognitive reasoning, and real-time control, thereby breaking down task silos and supporting embodied intelligence on resource-constrained edge devices. Evaluated on link-state classification and beam prediction tasks, the approach achieves high-accuracy, low-energy real-time decision-making while generating human-readable, interpretable diagnostic justifications, significantly enhancing system trustworthiness and operational efficiency.

Abstract

The realization of next-generation (NG) networks hinges on a fundamental departure from preprogrammed protocol engineering towards a paradigm of self-consciously evolving, autonomous and trusted intelligence. While conventional machine learning (ML) has introduced localized automation, it remains inherently bounded by single-task processing pipelines incapable of handling complex cross-layer dynamics. As a partial remedy, large language models (LLMs) excel at generalized cognitive reasoning, but to a degree they remain detached from the rich modalities of wireless telemetry. As a solution, we unveil Generative Network Intelligence conceptualized via the Perceive-Reason-Act (PERA) paradigm. This paradigm treats the wireless channel and the underlying network states as a continuous, multimodal narrative. By synchronizing the perceptual grounding of Large Wireless AI Models (LWAMs) with the cognitive reasoning of LLMs, PERA heralds the era of native NG intelligence. Crucially, this unified intelligence replaces fragmented, task-specific edge models by an efficient multi-task architecture delivering the real-time control needed for supporting dynamic physical applications while reducing both the complexity and energy dissipation. Moreover, we contrast the structural limitations of traditional ML to generative paradigms, conceive agentic reasoning across a NG protocol stack, and detail a practical three-tier design specifically engineered for the resource-constrained wireless edge. This architectural paradigm serves as a foundational framework for realizing fully autonomous, embodied agentic AI in NG networks. To validate this vision, our case study evaluates link-state classification and beam prediction, demonstrating how grounding wireless telemetry within a cognitive engine delivers the transparent, human-readable rationales required for trusted physical-layer diagnostics and beam control.

Problem

• 6G
• autonomous intelligence
• multimodal perception
• cognitive reasoning
• trustworthy agentic response

Innovation

• Perceive-Reason-Act
• Generative Network Intelligence
• Large Wireless AI Models
• Agentic Reasoning
• 6G Networks