AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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9,568 papers
Enterprises commonly deploy heterogeneous database systems, each of which owns a distinct SQL dialect with different syntax rules, built-in functions, and execution constraints. However, most existing NL2SQL methods assume a single canonical dialect (e.g., SQLite) and struggle to produce queries that are both semantica...
This work introduces Iterative Latent Representation Refinement (ILRR), an efficient framework for steering DLMs using a reference text as a high-level semantic blueprint and introduces Spatially Modulated Steering, an extension that enables long-form generation to be guided by shorter references by regulating intensit...
As humans delegate more tasks and decisions to artificial intelligence (AI), we risk losing control of our individual and collective futures. Relatively simple algorithmic systems already steer human decision-making, such as social media feed algorithms that lead people to unintentionally and absent-mindedly scroll thr...
This work introduces Frame Selection Sensitivity (FSS), a per-sample diagnostic that measures how much VLM accuracy changes when the most relevant frames are replaced with the least relevant ones, and constructs TempCore, compact evaluation subsets that isolate temporal samples from existing benchmarks.
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This study offers the first work to systematically explore efficient, low-resource personalization of jargon detection using open-source language models, offering a practical path toward scalable, user-adaptive NLP system.
Calpric, which combines automatic text selection and segmentation, active learning and the use of crowdsourced annotators to generate a large, balanced training set for privacy policies at low cost, and produces models that are accurate over a wider range of data categories, and provide more detailed, fine-grain labels...
Chance-calibrated neighborhood discordance (CANDOR) is applied to 22 frozen encoders on 605,443 images from 20 public datasets, covering 8 binary tasks in 7 domains, and finds that on every task, the best encoder is below 1/2.
This work presents a general framework for training large language models (LLMs) to "Connect the Dots", a meta-capability required by long-lifecycle agents, and presents proof-of-concept implementations of the CoD framework, including a GRPO-style RL algorithm with fine-grained credit assignment.
We consider Representation Misdirection (RM), a class of large language model (LLM) unlearning methods that achieve forgetting by redirecting the latent representations of forget-samples toward a target vector. Despite being important, the roles of the target vector used in RM, however, remain underexplored. Here, we a...
Retrieval of information from graph-structured knowledge bases represents a promising direction for improving the factuality of LLMs. While various solutions have been proposed, a comparison of methods is difficult due to the lack of challenging QA datasets with ground-truth targets for graph retrieval. We present Synt...
PerSyn (Personalized data Synthesis), a novel synthesis strategy that operates under a new ``Route then Generate''paradigm to create data tailored to each student model, enabling it to learn more effectively, is proposed.
Large language model pretraining is compute-intensive, yet many tokens contribute marginally to learning, resulting in inefficiency. We introduce Efficient Selective Language Modeling (ESLM), a risk-aware algorithm that improves training efficiency and distributional robustness by performing online token-level batch se...
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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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