AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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3,629 papers
It is found that models generally learn more reliably from rules than from examples alone, and additional examples on top of rules or simply scaling up the number of examples do not lead to consistent and significant gains.
This work proposes Adaptive Local Relational Alignment (ALRA), a position-specific framework combining student proposals with teacher guidance, which replaces the teacher-mass coefficients of the two conditional terms with unit coefficients, preventing either term from being downweighted solely because its region has l...
RecurTrace introduces Loop Memory Attention, which lets each looped layer attend to its own states from previous iterations along the loop-time axis, so the model can revisit earlier computations instead of relying on the latest state alone.
It is found that the largest public Armenian corpora overlap web-derived evaluation panels heavily, including a train/test self-overlap inside FineWeb-2.0, and that a small share of verified translated STEM data reverses the loss.
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Many recurring text functions are easy to describe but difficult to implement with rules, while calling a large remote model for every input introduces repeated cost, latency, and dependency on a provider. We present compile by training, which turns a natural-language specification into a reusable neural function. At c...
Gaps remain in our understanding of how large language models (LLMs) acquire knowledge during pre-training. We posit that auxiliary views, reformulations of knowledge, are causally helpful for learning. We design controlled experiments to isolate this. First, we confirm that repetition is necessary for acquisition and...
Two restart-controlled truncation probes separate when a solution fits the continuation budget from when a prefix carries value that fresh computation cannot buy, and a pre-registered, difficulty-controlled test finds no detectable outcome information in early-window internal signals beyond a problem-difficulty baselin...
This study evaluates the effectiveness of large language models for CKD screening under zero-shot and few-shot in-context learning settings and compares them with traditional ML and DL methods and proposes a framework that uses clinically selected tabular features and structured prompt templates to enable LLM-based inf...
Synthetic semantic supervision yields statistically significant gains over pretraining baselines of the same inference-time size on five of eight tasks, with parity on two more; once fine-tuned, it matches or exceeds zero-shot models two orders of magnitude larger on classification, and it stays on par with execution-a...
This record contains the human annotation study underpinning the MSc thesis"Exploring Semantic Consistency in Image Super-Resolution" (Master in ComputerVision, Universitat Autònoma de Barcelona / Computer Vision Center). Super-resolution is normally evaluated on fidelity and perceptual quality — howclose an output is...
A small-language-model architecture for AI-driven detection, prediction and safe defence of operational technology. Operational technology (OT) and industrial control systems (ICS) increasingly connect information technology, industrial networks, programmable logic controllers (PLCs), SCADA, distributed control systems...
Meta-Student is a collaborative research platform, coordinated by the Sports Science Replication Centre, that pools independently collected undergraduate research datasets into statistically powerful individual-participant-data (IPD) meta-analyses. Students at multiple institutions each run a small, standardised study...
A Blog post by NVIDIA on Hugging Face
Data Mining & Modeling
A Blog post by Ai2 on Hugging Face
A Blog post by IBM Research on Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
A Blog post by IBM Granite on Hugging Face
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