LLM-Based Agentic Negotiation for 6G: Addressing Uncertainty Neglect and Tail-Event Risk

Researchers propose a risk-aware framework for LLM-based agents in 6G networks that addresses uncertainty neglect bias by using Digital Twins and Conditional Value-at-Risk (CVaR) to evaluate tail-event risks instead of relying on simple averages. The framework eliminates SLA violations and reduces extreme latencies by up to 51.7% while maintaining sub-1.5-second inference times on consumer GPU hardware.

This research tackles a fundamental vulnerability in autonomous LLM-based systems: their tendency to optimize for average outcomes while ignoring catastrophic tail risks. In high-stakes applications like telecommunications infrastructure, this bias creates dangerous blind spots where worst-case scenarios—precisely where reliability matters most—remain unaccounted for. The paper's contribution lies in demonstrating that agents can be systematically debiased through formal mathematical frameworks borrowed from quantitative finance.

The problem emerges as 6G networks increasingly rely on autonomous negotiation agents for resource allocation. Traditional LLM-powered systems lack native mechanisms to reason about extreme events or express confidence in their predictions, making them fundamentally unsuited for critical infrastructure. This research shows these limitations aren't merely theoretical: in their baseline comparison, the mean-based approach violated strict latency requirements 11 times across 200 trials, an unacceptable failure rate for telecom services.

The solution—combining Digital Twins for probabilistic forecasting with CVaR for tail-risk evaluation—creates a mathematically grounded approach to decision-making under uncertainty. By requiring agents to explicitly quantify epistemic uncertainty and propagate it through their reasoning, the framework transforms vague confidence into precise risk calculations. The results demonstrate complete SLA compliance while reducing 99.999th-percentile latencies by half, though at the cost of energy efficiency gains.

The practical feasibility claims matter significantly: achieving this robustness without specialized hardware or prohibitive latency overhead opens deployment pathways for real-world networks. This represents a notable step toward trustworthy autonomous systems in critical infrastructure, though broader adoption hinges on whether the energy-reliability tradeoff remains acceptable at scale.

• →LLM agents exhibit systematic uncertainty neglect bias, optimizing for averages while ignoring tail risks in high-stakes decisions.
• →Digital Twins combined with Conditional Value-at-Risk analysis enable agents to reason explicitly about worst-case scenarios rather than relying on simple statistical means.
• →The proposed framework achieved 100% SLA compliance and 51.7% latency reduction for 99.999th-percentile events in 6G network slicing negotiations.
• →Sub-1.5-second inference times on standard GPU hardware demonstrate practical feasibility for non-real-time network infrastructure use cases.
• →Risk-aware agentic systems require explicit epistemic uncertainty quantification to prevent decision-making on unreliable predictions.