arXiv:2605. 23809v2 Announce Type: replace-cross Abstract: The Open Radio Access Network (O-RAN) architecture allows AI to be embedded directly into the RAN through modular xApps and rApps, yet creating these applications collecting data, training models, writing code, and deploying them safely remains slow and largely manual.
arXiv:2606. 02862v1 Announce Type: new Abstract: The rise of Large Language Models (LLMs) has enabled agentic AI capable of complex reasoning and tool use; however, deploying such autonomy in pervasive computing environments remains challenging due to the strict memory and energy constraints of embedded microcontrollers.
arXiv:2607. 28629v1 Announce Type: new Abstract: The rapid transition from reactive large language models (LLMs) to persistent, action-capable systems has exposed critical gaps in the architectural understanding of Agentic AI, particularly in separating inference, orchestration, and execution layers for autonomous AI agents.
arXiv:2609.10226v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated remarkable capabilities in reasoning and code generation, raising the prospect that they could assist in...
Maia 200 is a software‑defined dataflow system that delivers high performance AI acceleration, achieving 10,145 Tflop/s in FP4 and 5,072 Tflop/s in FP8 within a 750 W TDP and 7 TB/s HBM bandwidth. It exemplifies a new class of Software Defined Locally Accessed Dataflow Architectures (SDLA), which program dataflow engines to orchestrate specialized memories and data‑movement engines, shifting focus from thread‑centric to data‑movement‑centric design. The system offers significant cost and energy savings while supporting massive parallelism for AI inference workloads, positioning it as a compelling solution for next‑generation high‑performance computing.
arXiv:2609.12551v2 Announce Type: replace-cross Abstract: AI is beginning to make substantive contributions to LLM inference optimization. Existing AI optimizations are predominantly profiling-based....
arXiv:2606. 03557v1 Announce Type: new Abstract: As generative AI capabilities expand, AI-driven virtual worlds face a growing architectural challenge.
arXiv:2511. 10480v3 Announce Type: replace-cross Abstract: Optimizing the performance of large language models (LLMs) on large-scale AI training and inference systems requires a scalable and expressive mechanism to model distributed workload execution.
The paper introduces a taxonomy for resource management systems that span IoT, edge, and cloud layers, extending Wang et al.’s Continuum Orchestration Systems with two new dimensions: the AI Augmentation Paradigm and the Feedback channel. It evaluates six recent architectures and finds that none combine full LLM orchestration with full agent‑layer feedback in a Cloud Continuum setting, highlighting a gap in cross‑tier feedback abstraction. The study emphasizes the need for a unified feedback mechanism to bridge disparate per‑tier signals to the LLM orchestrator.
arXiv:2606. 15079v1 Announce Type: cross Abstract: Efficient and scalable agentic intelligence requires models that can deliver both low-latency responses and strong reasoning capabilities while remaining practical to train, serve, and deploy.
arXiv:2606. 15994v1 Announce Type: new Abstract: Translating deep learning models from PyTorch's flexible, object-oriented design to JAX's functional, stateless setup is usually a manual and error-prone task.
arXiv:2607. 22805v1 Announce Type: cross Abstract: We propose OrchNAS, an energy-aware, personalised, federated edge intelligence framework that leverages a Neural Architecture Search Service to automatically design service-adaptive models for heterogeneous edge environments.
arXiv:2606. 21401v2 Announce Type: replace-cross Abstract: Agentic AI applications compose multiple model calls and tool executions, creating new scheduling challenges for GPU-CPU clusters.