AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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20,463 papers
A universal controller for any robot morphology would greatly improve computational and data efficiency. Steps have been made towards such multi-robot control by utilizing contextual information about the properties of individual robots and exploiting their modular structure in the architecture of deep reinforcement le...
This work introduces ID-WM (Implicit Dynamics World Models), a hierarchical world model that decouples state-space reachability from local control and yields a 135% performance gain over single-level world model MPC, particularly in complex, unseen configurations for long-horizon tasks.
RoboArchitect shows that harness design can be automated, offering a strong alternative to finetuning on a novel setup, and its design can be effectively automated and optimized without weight updates.
This paper derives an asymptotically subquadratic sparse attention method based on a hierarchical, tree-structured indexer, which it is demonstrated can be cheaply applied to existing pretrained models, and maintains performance on long-context retrieval and challenging reasoning, mathematical, and agentic coding evalu...
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Reinforcement learning with verifiable rewards (RLVR) has become the standard method for training language models to reason. However, RLVR typically relies solely on outcome correctness, which does not guarantee that the CoT causally contributes to the final answer. Without this connection, outcome rewards may fail to...
We study the stochastic second-order oracle complexity of solving smooth convex finite-sum optimization with $n$ components. Very recently, Tsedenov \& Kovalev (2026) showed that the minimax optimal complexity under averaged smoothness and convexity is $\tilde{\Theta}(n + n^{6/7} \epsilon^{-2/7})$ by establishing nearl...
We study how to learn a generative model of clean images using only noisy observations. Diffusion processes and their generalizations to non-Gaussian noise require posterior predictions at every noise level. We first show that it is possible to learn these predictions from independent noisy views using Noise2Noise regr...
Looping re-applies a block of layers so that each token passes through the block more than once. Adding a loop to a trained model is attractive because it leaves the parameter count, and hence the disk size, the memory footprint and the training cost unchanged. Unfortunately, every generated token pays for the loop. Re...
LLM-SR-UQ is introduced, a framework that retains competing equation structures and uses their disagreement to characterize uncertainty about the underlying functional form and substantially improves coverage attainment by incorporating structural disagreement while preserving in-distribution coverage.
Automated, self-updating meta-analysis of when humans and AI work better together, and when they work better alone is introduced, finding that, on average, human-AI combinations performed significantly better than humans alone, but they performed significantly worse than the better of humans or AI alone.
An idealized reference model---an injection-invariant, utility-preserving oracle---can nevertheless be approximated, as its output distribution in training is interestingly accessible, which motivates a theoretically principled SecPO loss.
Chronos-2 is introduced, a foundation model for zero-shot forecasting that models the interactions between multivariate time series and heterogeneous covariates, which commonly arise in scientific applications and shows that a single pretrained model can capture structure shared across temporal systems.
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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