AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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3,573 papers
MT-ProtBERT consistently outperforms PARROT, an RNN-based IDP-specific model, across all tasks, and demonstrates that combining self-supervised and biochemistry-informed tasks, and multi-scale learning enables robust modeling of unstructured proteins under data scarcity.
A small concept generator is optimized with reinforcement learning so that its concepts maximize the downstream success of a larger, frozen answer generator, and can thus be trained into an effective, reusable search policy for a much larger one.
NSFT (Neural Sub-expert Fine-Tuning), a fine-grained PEFT framework that refines MoE adaptation from experts to sub-experts, is proposed and suggests that sub-expert-level adaptation is a more precise and efficient PEFT paradigm for MoE LLMs.
Results show that persistent program growth can move recurring control out of model context and into low-cost code, yielding reusable specialist agents that remain effective with smaller deployment models.
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This work examines how reachable behavior, optimization budgets, and persistent adaptation shape exposure to proxy error, and develops a comparative framework for reward hacking across these three optimization substrates: weights, selection, and text.
Evaluated on two reasoning domains, math and multi-hop reasoning, across 1-8B models from different families, LoT consistently improves over KD and converges faster than staged curricula, highlighting the value of adaptive progression.
This work builds four modules (for drug-discovery chemistry, materials science, machine learning, and mineral geochemistry) in the ArticleMiner framework, and evaluates them on 163 papers, including a new geochemistry benchmark with expert-curated ground truth.
This work treats attention as a routing map from one forward pass, keep a small set of routes that point toward the answer, and asks whether those routes actually matter, and whether those routes actually matter.
The AI Neuroscientist is presented, a language agent for interactive data exploration that integrates a large language model with a neuroimaging toolset to perform quality control, modeling, and visualization and allows researchers to query data quality and specify analysis parameters directly in natural language.
Our research topic has inspired studies on new results on lightweight computer vision (CV) models and edge deployment strategies for crop production. With the proliferation of unmanned aerial vehicles (UAVs), autonomous harvesters, and Internet-of-Things (IoT) devices in agriculture, the demand for efficient, accurate,...
Introduction The purpose of this study was to strengthen 5th grade elementary reading, math, and ESOL teachers' instructional expertise in integrating oral and written language development within Tier 1 differentiated instruction for multilingual language learners (MLLs), through a job-embedded professional development...
Background. The rapid development of digital technology in sports education has opened new opportunities for interactive learning and skill improvement. In floorball, traditional training methods often lack personalized guidance and real-time feedback, limiting athletes’ progress. Therefore, this study aims to develop...
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
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Gemini 4 Carbon is our strongest model with state-of-the-art cybersecurity and long horizon capabilities.
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