AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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LLM compression reduces inference costs and memory requirements, but selecting a method and configuration remains largely empirical because comparable resource reductions can produce different capability losses. We systematically investigate capability scaling-down laws for LLM compression across pruning, quantization,...
Multimodal models increasingly reach for tools when solving visual tasks (crop, zoom, rotate, brighten), a paradigm known as thinking-with-images. The central challenge is one of perception: tools mostly serve to expose visual evidence, reasoning over that evidence stays in language, and most targets are ones a human c...
AnyJev is described, which reads a typed decision from one prefill of a pretrained instruction-tuned language model, which divides out a label prior estimated from unlabelled inputs, and it averages log-probabilities over the K cyclic rotations of the option list.
It is found that compression degrades reliability before solvability and proposed PAIR (Prompt Adaptation using Interventional Rollouts) for adapting structured compression prompts achieves the strongest cross-run reliability among compressed methods in every main benchmark-scope combination.
SPECTRA is developed, a training-free, drop-in codec that re-encodes the cache into a coordinate system and concentrates the bit budget on the channels that carry the signal, pushing usable compression past the 2-bit cliff so the same GPU holds longer contexts and larger batches.
TRACE is introduced, a verifier-guided framework that evaluates individual compaction events through paired closed-loop continuations from the same environment state and uses summary preferences to optimize a natural-language compression prompt while keeping all models frozen.
The results show that skill representation itself matters, and that simply preserving the structure already present in skill files can substantially improve retrieval.
Large Language Models (LLMs) have demonstrated remarkable performance across diverse tasks, from text summarization to question answering. Despite these capabilities, their black-box nature obscures internal decision-making processes. Mechanistic interpretability (MI) aims to address this by reverse-engineering neural...
MV-WSA (Marginal-Value Working-Set Allocation), which splits memory by marginal latency benefit per byte while enforcing a KV-admission floor, is implemented in WiSP (Working-Set Paging), a routing-aware expert pager that plugs into an unmodified serving engine and preserves byte-identical outputs.
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