AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Hand anthropometry supports protective-glove design, but existing measurement methods often require trained operators, specialized hardware, or manual landmarking. We present HandAnthro, which estimates 44 projected hand dimensions from a smartphone photograph of a palm-up hand on US letter-size paper. The pipeline rec...
This work applies MRT to power outage report generation for the Outage Data Initiative Nationwide (ODIN), transforming heterogeneous reports into standardized XML compliant with CIM IEC 61968-3.
This work introduces APQF, an agentic profiling-guided framework that combines structured pruning, mixed-precision quantization-aware training, and accuracy recovery in one automated pipeline, and is the first framework to combine LLM-guided, profiling-grounded decisions with a fully training-aware pruning and quantiza...
The results show that candidate count alone is not enough to describe the systems cost of multi-candidate test-time scaling and Evaluations should report not only candidate count and accuracy, but also generation schedule and GPU-level systems metrics.
This work proposes an autotuning framework that designs a machine learning-based ensemble LLVM Intermediate Representation ranker, Neural Configuration Scorer (NCS), which ranks the performance of IRs sampled by a transfer-learning-based autotuner, improving the efficiency of the tuning process by reducing tuning overh...
Adaptive test-time scaling with a lightweight fuzzy controller that maps interpretable signals, including estimated prompt complexity and model confidence, to a per-query sampling budget is proposed, suggesting that interpretable adaptive sampling is a practical direction for more efficient test-time reasoning in large...
VarRate is presented, a training-free KV codec that assigns each token a variable low-rank budget by its query salience, keeping every token at a nonzero rank, and is the strongest matched-memory compressor.
Empirical findings suggest that CudaPerf significantly outperforms strong baselines, including Qwen-3-32B and CUDA Agent by achieving up to 5X and 3.32X improvements in speedup, and 17%&7% improvements in correctness, respectively.
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