Future is delighted to confirm that the Future Games Show Summer Showcase (June 6), FGS Live From Los Angeles (June 6) will return as part of the season of summer gaming events.

Join us for the Future Games Show at Gamescom 2026

Saturday, June 6, 2026 starting at 9:00 AM Pacific, 12:00 PM Eastern

The head of the Defense Department's FutureG Office discussed plans for the open centralized unit distributed unit (OCUDU) project at the AITalks conference.

Pentagon-adjacent FutureG Office taps OTFS pioneer to build dual-use sensing network.

Join us for the Future Games Show: Summer Showcase 2025

Tom Rondeau, director of the FutureG programme, discussed the agency’s role as a founding member of the new OCUDU Ecosystem Foundation

Join us for the Future Games Show at Gamescom 2026

A new transceiver invented by electrical engineers at the University of California, Irvine boosts radio frequencies into 140-gigahertz territory, unlocking data speeds that rival those of physical fiber-optic cables and laying the groundwork for a transition to 6G and FutureG data transmission protocols.

The Defense Department is poised to release an open-source software stack that users from the military and commercial industry can use to develop their own custom network solutions.

Join us for the Future Games Show at Gamescom 2026

Tune in for the Future Games Show Spring Showcase on March 20 with a BONUS broadcast live from GDC

We present a retrieval-augmented question answering framework for 5G/6G networks, where the Open Radio Access Network (O-RAN) has become central to disaggregated, virtualized, and AI-driven wireless systems. While O-RAN enables multi-vendor interoperability and cloud-native deployments, its fast-changing specifications and interfaces pose major challenges for researchers and practitioners. Manual navigation of these complex documents is labor-intensive and error-prone, slowing system design, integration, and deployment. To address this challenge, we adopt Contextual Retrieval-Augmented Generation (Contextual RAG), a strategy in which candidate answer choices guide document retrieval and chunk-specific context to improve large language model (LLM) performance. This improvement over traditional RAG achieves more targeted and context-aware retrieval, which improves the relevance of documents passed to the LLM, particularly when the query alone lacks sufficient context for accurate grounding. Our framework is designed for dynamic domains where data evolves rapidly and models must be continuously updated or redeployed, all without requiring LLM fine-tuning. We evaluate this framework using the ORANBenchmark-13K dataset, and compare three LLMs, namely, Llama3.2, Qwen2.5-7B, and Qwen3.0-4B, across both Direct Question Answering (Direct Q&A;) and Chain-of-Thought (CoT) prompting strategies. We show that Contextual RAG consistently improves accuracy over standard RAG and base prompting, while maintaining competitive runtime and CO2 emissions. These results highlight the potential of Contextual RAG to serve as a scalable and effective solution for domain-specific Q&A; in ORAN and broader 5G/6G environments, enabling more accurate interpretation of evolving standards while preserving efficiency and sustainability.