AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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9,568 papers
It is demonstrated that model selection, retrieval behaviour, and hardware limits must be evaluated separately when deploying open-weight LLMs for Turkish domain documents.
This work reformulates eviction as a deployment risk-control problem: a material degradation occurs when eviction lowers task utility by more than a deployment-specified tolerance relative to full-KV inference on the same request, and deployment risk is the population frequency of such events.
This work presents an approach that ranks encoder layers by the leave-one-layer-out change in Word Error Rate (WER), and presents the pruned model, which is simply a more shallow encoder with fewer layers.
The findings are descriptive and do not support causal claims of progress: technological coexistence and uneven reporting are part of the map, but the A2+ to A3+ cliff is the contribution, not a quality ladder.
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Overall, these findings highlight the trade-off on the use of pre-trained models with novel AI-assisted educational questions and how strategic feature enhancements help address loss in performance.
DeltaS is proposed, a query-agnostic, training-free method that retains video chunks inducing larger normalized state change, or state drift, in a controlled comparison with the budget and retention policy held fixed, which suggests that the two memories of hybrid architectures can work cooperatively.
This work introduces WISE (Working-set Inference with Support Exploitation), a training-free method that uses unrestricted attention during early recurrent steps to discover a block-structured working set, then reuses its support in later steps while keeping attention weights and recurrent refinement dynamic.
This work introduces RECAP (REdundancy-aware Credit Assignment via Propagation), which addresses this limitation by assigning credit where it is due based on both a step's downstream role in the reasoning structure and its contribution to solving the problem correctly.
Vision-language models (VLMs) are deployed as zero-shot judges of image aesthetics, and panels of several models are recommended, on thin evidence, as the way to make such judges reliable. On two human-rated datasets, EVA and PARA, we find that a panel of holistic judges never significantly beats its best member, wheth...
Large language models have enabled rapid deployment of language classification and analysis, allowing users to describe complex constructs in natural language, such as pedagogical moves or expressions of learning and engagement, and label them in conversational datasets such as classroom or tutoring transcripts. Howeve...
LexLattice is introduced, an extractive summarizer that reifies a legal act's hierarchy as a two-dimensional semantic lattice and consolidates over it with a masked 2D neural cellular automata before selection and position explicit consolidation over document structure as a compact and traceable alternative to scale fo...
This work shows that LLMs can be used to strategically target expert attention, shortening months of codebook revision to days without sacrificing labeling performance.
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A Blog post by Ai2 on Hugging Face
A Blog post by IBM Research on Hugging Face
Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.
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