AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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AdaMem is a relevance-guided soft-compression framework that maps learned passage-relevance estimates to a query-dependent allocation of a fixed memory-token budget and retains an efficiency profile comparable to the uniform-compression baseline, while achieving up to $4\times lower inference latency than full-context...
This work proposes Enoki, an Open Information Extraction framework for multi-level hallucination detection that supports LLM-based, encoder-based, and rule-based extraction regimes, balancing accuracy and inference cost through a common interface.
A unified toolkit and interactive web UI for detecting contextual hallucinations in retrieval-augmented, agentic, and memory-grounded LLM systems, and as a faithfulness gate within long-term memory systems is demonstrated.
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