AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work introduces CAST (Clipping with Adaptive Scales and Thresholds), a history-adaptive extension of SSC that achieves lower training and mean validation losses than nine gradient-processing baselines, with the fewest relative gradient spikes.
Training long-horizon language model agents requires assigning credit to intermediate decisions from sparse terminal feedback. Within a finite group of sibling rollouts, useful action comparisons depend on their context, evidence for both alternatives, and how that evidence enters learning. We propose Matched-Context G...
Multi-Head Latent Attention (MLA) enables expressive multi-head attention with compact caches for its content and decoupled RoPE paths, yet cache memory still scales linearly with context length and batch size. In this work, we establish a systematic model of MLA's dual-path quantization errors, characterizing their di...
Experiments on synthetic and real-world benchmarks show that FG$^2$-GDN and its variant improve associative recall and long-context understanding over GDN and KDA, with comparable computational efficiency.
Decay-Aware State Compression (DASC), which derives retention horizons from model weights, selects long-horizon state units, and packs them into a ragged state checkpoint layout to integrate efficiently with tensor-parallel inference engines.
DAMP uses both quantization-error energy and decay-based persistence to identify high-risk channels during offline calibration and stores these channels at higher precision and the remainder in INT8, the first to study post-training quantization of recurrent states in GDN and KDA based language models.
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