AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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20,463 papers
This work addresses the attribution problem with an observable evidence standard that separates opportunity to act, executed-plan change, operational consequence, equivalence, uncertainty, and operating scope from heterogeneous planning additions, and applies it to heterogeneous planning additions.
Progress in artificial intelligence is widely assumed to scale with effort: more researchers, papers and investment should accelerate discovery. Theories of technological growth instead locate the difficulty of discovery in the space of ideas. Here we show that neither scale nor space is enough: breakthroughs depend on...
Safe Multi-Agent Reinforcement Learning (SMARL) aims to learn cooperative control policies under prescribed safety constraints. Al- though existing SMARL methods focus on enforcing safety during policy optimization, their vulnerability remains largely unexplored. This paper investigates inference-time observation attac...
TEX, a collaborative test-time scaling framework based on execution-driven cross-validation that parallelizes agents to generate tests and patches, cross-executing each test against every patch to form a feedback matrix, enabling objective validation without verbose trace sharing.
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The development of embodied models of animal behavior is a major goal of the neuro-AI community. Embodied models will enhance our ability to make direct comparisons between biological and artificial agents. To make such comparisons effective, though, we need embodied models that actually move like animals. Previous wor...
Analyzing merchant transaction histories requires dynamic temporal filtering, entity resolution, and aggregation over irregular event streams--operations poorly captured by static tabular or time-series forecasting benchmarks. We introduce TxnQA, an analytical QA benchmark comprising 27 task families (81 templates) alo...
Enterprise adoption of LLMs in agentic workflows is gated by evaluations that span a wide arc of abilities: CRM operations, heterogeneous-document retrieval, personal data understanding, multi-turn planning, tool use, function calling, long-horizon research, and long-context software engineering. These evaluations live...
Chemistry has recently entered a high-data regime as molecular databases from high-throughput platforms or computational studies have become available. Increasing automation within the chemical sciences leads to a more digital and precision chemistry. Dedicated scientific agents are becoming central to guiding the chem...
This paper proposes a sparse relaxation method for RL (called SpaRLax), which leverages the differentiability of soft-CoT reasoning over time steps and makes training more stable and effective, and adopts an entmax sparsity method that adaptively determines the degree of softness.
Layer-Specific Adaptation with Prototype-Anchored Adapter-Space Transport (LSA-PAST) is proposed, a parameter-efficient method tailored to composition-IL that achieves state-of-the-art accuracy-forgetting trade-off performance for composition-IL.
A control variate built from a learned classifier of the outage, trained on cheap observable features such as temperature and load forecasts is proposed and shows that learned control variates can help planners design distribution grids that stay reliable against rare outages.
Transformer-based k-space motion-trajectory estimation enables fast retrospective correction of standard clinical TSE brain MRI and reduces visible artefacts while preserving anatomical detail.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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