AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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20,463 papers
This work introduces ShIOEnv, a Gymnasium-compatible Bash environment for synthesizing execution-annotated shell input traces and introduces grammar-constrained synthesis to generate syntactically valid arguments with high input irreducibility, which is estimated through self-ablative contrasts of execution artifacts.
We present the first large-scale open-set benchmark for multilingual audio-video deepfake detection. Our dataset comprises nearly 350 hours of real and fake videos across eight languages, with 59% of data being generated. For each language, the fake videos are generated with several distinct audio and video deepfake ge...
While machine learning fairness has made significant progress in recent years, most existing solutions focus on tabular data and are poorly suited for vision-based classification tasks, which rely heavily on deep learning. To bridge this gap, we introduce DetoxAI, an open-source Python library for improving fairness in...
We consider the problem of policy optimization with noisy rewards, where the policy is constrained to be in the neighborhood of a reference policy.We study the risk of the policy optimization process, defined to be the chance of obtaining a policy worse off than the reference policy. In an idealized multivariate Gaussi...
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Language models relying on verbalized Chain-of-Thought (CoT) are constrained to token-by-token generation in the language space, where many tokens serve textual coherence rather than core reasoning. This entangles genuine differences in logical trajectories with superficial variations in verbalization. Moving reasoning...
Low-Rank Adaptation (LoRA) enables efficient adaptation of large pretrained models to downstream tasks by parameterizing weight updates with low-rank matrices. In this paper, we investigate the limitations of the LoRA parameterization from a geometric perspective. Specifically, we show that when a full fine-tuning grad...
A trained model is usually described by its weights, as if the weights fixed its behavior. In practice the same weights are run by different programs. Training uses high-precision accumulation and fixed tensor shapes, while serving uses low-precision arithmetic, fused attention kernels, a paged key-value cache, and dyn...
Large language models (LLMs) are increasingly used for molecular tasks, but it remains unclear which molecular representation to use. We present a systematic benchmark, MolRepBench, to evaluate LLM molecular competence across nine representations and eight chemical tasks. We benchmark 16 LLMs across five model families...
This work introduces a distributional framework for SAE analyses, in which each feature is represented not by a single decoder vector like in the literature, but by an activation-weighted distribution over the hidden states that express it, and provides a unified semantic metric for cross-layer feature comparison.
The proposed EDRM (Entropy Dynamics-based Reasoning Manifold), a lightweight routing framework whose default heuristic requires no router training, supports selective reasoning based on early decoding signals and demonstrates the potential of entropy-based control for efficient LLM inference.
Learning permutations is fundamental to sorting, ranking, and matching, but existing differentiable methods based on entropy-regularized Sinkhorn produce a single softened solution and collapse under ambiguity. We present PermFlow, a conditional flow matching framework that operates directly on the affine subspace of m...
Sampling from constrained distributions has a wide range of applications, including in Bayesian optimization and robotics. Prior work establishes convergence and feasibility guarantees for constrained sampling, but assumes that the feasible set is connected. However, in practice, the feasible set often decomposes into...
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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