AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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19,710 papers
Study of 2PL, GPCM, and NRM decoders paired with LSTM, Transformer, and DKVMN encoders shows why item parameters, especially discrimination and option slopes, must be evaluated separately from response prediction.
This work proposes MoEZip, a routing-aware KV cache compression for sparse MoE language models, and proposes a novel metric based on the Fisher information matrix of routing distributions that combines attention weights for routing-aware scoring of contexts.
Artificial intelligence (AI) is reshaping Human Resource Management (HRM) by transforming how organizations acquire, develop, deploy, evaluate, and retain human capital while expanding HR’s strategic role in decision-making and value creation. Despite growing scholarly attention, AI-driven HRM research remains fragment...
It is argued that AI should remain an auxiliary tool, while final forensic conclusions and procedural decisions must be made by competent human professionals in accordance with the law.
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This study develops a jurisprudential, algorithmic, and systems framework for automated regulatory intelligence within the structurally fragmented administrative environment of the United States. Mechanical and HVAC regulation is used as a test domain because a single field operation may simultaneously implicate federa...
This study examines how the rapid expansion of generative artificial intelligence (AI) challenges traditional copyright law by shifting legal concerns beyond authorship and ownership toward transparency, disclosure, traceability, and accountability in AI-assisted artistic production.The study adopts a doctrinal and com...
Long-horizon agents must learn heterogeneous capabilities from interleaved trajectories. Updating shared parameters to learn new capabilities can degrade previously acquired ones, resulting in catastrophic forgetting. As a trajectory unfolds, different stages may require different capabilities, yet conventional joint t...
This work proposes WSR-Tune, a Weight Space Reparameterization-based fine-tuning framework that preserves safety during downstream adaptation and achieves a more favorable trade-off between downstream performance and safety retention, demonstrating its effectiveness for reliable LLM fine-tuning.
Diffusion Transformers (DiTs) deliver high-fidelity image and video generation, but inference requires many sequential model evaluations. Two acceleration strategies are increasingly important: model compression makes each evaluation cheaper, while adaptive inference avoids selected teacher computation through reuse, f...
This work investigates a human-directed workflow in which coding agents iteratively propose, implement, evaluate, diagnose, and eventually refine algorithms guided by simulation feedback and explicit correctness verification and shows that this workflow robustly outperformed any manually engineered solutions.
falcon{}, a counterexample-grounded verification framework that separates attack exploration from behavioral verification and connects them through execution traces of the program under test, is introduced, suggesting that execution-grounded feedback can mitigate reward overoptimization while preserving useful guidance...
MC-LDM represents the first diffusion-based framework to leverage multimodal priors for MRI forecasting, and enables patient-specific trajectory generation that captures aspects of AD heterogeneity not observable from structural anatomy alone, bridging the gap between rich multimodal supervision and the strict constrai...
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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