The sixth-generation (6G) of mobile networks will be shaped not only by artificial intelligence (AI)-enabled network automation and optimization, but also by the need to serve AI as a 6G-native workload. Emerging AI services introduce traffic and compute demands that differ from conventional mobile broadband. Their user experience depends on how quickly useful information is delivered, how bursty and asymmetric multimodal flows are handled, and where inference, retrieval, caching, and content processing are executed. This article presents a joint connectivity-compute view of AI-native 6G. We first characterize representative AI service traffic in terms of uplink/downlink throughput skew, burstiness, and token latency. Next, we discuss how fifth-generation extended reality awareness mechanisms can evolve toward AI traffic characteristics awareness in 6G. Finally, we introduce AI Grid as a distributed AI infrastructure platform for placing workloads according to latency, cost, policy, and service-level constraints. Together, AI-aware connectivity and AI Grid enable 6G as a distributed intelligence platform.
Lopamudra Kundu, Xingqin Lin, Shuvo Chowdhury et al.· 0 citations
This empirical study covers broad knowledge, symbolic reasoning, and competition mathematics, and it introduces an evaluation profile whose coordinates and simple functionals recover or bound common repeated-sampling metrics, and require compute accounting and uncertainty estimates that match the protocol.
Mohsen Hariri, Weicong Chen, Nahal Shahini et al.· 4 citations
Large language models can solve substantially harder reasoning problems with more inference-time compute. The term"test-time scaling,"however, now covers diverse inference algorithms that extend deliberation along a single trajectory, sample completed candidates and aggregate them through voting or verification, or search over unfinished partial states. These algorithms differ in their statistical structure, compute accounting, and failure modes. Treating these procedures as interchangeable under a single scalar"budget,"or reporting accuracy without the inference protocol that produced it, makes results difficult to compare across studies. We develop a systematic account of test-time scaling along three axes. First, we formalize test-time scaling as budgeted inference over the implicit prefix tree of an autoregressive model and distinguish three structural regimes: single-trajectory sequential scaling, leaf-level scaling with terminal reduction, and prefix-level scaling. Second, we treat the evaluated object as the entire inference system and develop evaluation principles that separate end-to-end system performance from candidate-bank diagnostics. We introduce an evaluation profile whose coordinates and simple functionals recover or bound common repeated-sampling metrics, and prescribe protocol-matched reporting of compute and uncertainty. Third, we specify reproducibility requirements for inference protocols, distinguishing exact replay from distributional reproducibility and identifying the artifacts needed to support each. We also organize the open-weight reasoning ecosystem by model-side and interface mechanisms, apply these principles to broad-knowledge, symbolic-reasoning, and competition-mathematics benchmarks, and assemble over 2 billion full reasoning traces for release with progressively richer verifier and token-level signals.
Mohsen Hariri, Weicong Chen, Nahal Shahini et al.· 4 citations
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