kernelevole-agentic

Automate compute kernel optimization via agentic AI and retrieval-augmented prompting. Unified context-aware transformation function, hardware-specific constraint KB, self-improving state machine exploring kernel variants—achieving 1.25-17× speedups on production recommendation workloads in hours vs weeks of manual effort.

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Repositorio: ADu2021/skillXiv
Última actividad en el origen: 24 de marzo de 2026 a las 19:42
Idioma detectado de SKILL.md: inglés
Estrellas: 6
Forks: 0

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npx skills add https://github.com/ADu2021/skillXiv --skill kernelevole-agentic

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meaningful-kebab-case-name

Convert arXiv papers into ready-to-use agent skills using category-aware extraction. First classifies the paper into one or more of 11 research categories, then applies a specialized extraction pipeline for each category — because different types of papers produce different types of usable knowledge. A single paper can yield multiple skills if it spans categories. Use this skill whenever the user wants to turn a paper into a skill, extract practical techniques from research, build a skill library from papers, convert arXiv papers into reusable agent instructions, or batch-process multiple papers into skills. Also trigger when someone asks about extracting actionable knowledge from papers, making research practical for LLM agents, or systematically converting academic contributions into structured agent capabilities.

2026-03-26

action-quantization-behavior-cloning

Establish regret bounds for behavior cloning with discretized actions combining statistical error and quantization error terms. Prove smoothness requirements for safe quantizer design, show that learning-based quantizers fail these requirements, and propose model-based augmentation to reduce error dependence from H² to H.

2026-03-26

adaptive-lora-personalized-ranks

Dynamically allocate LoRA ranks per-layer during fine-tuning instead of using fixed uniform ranks. Learn optimal rank for each layer and subject via variational framework with discretized exponential distribution, reducing memory footprint while maintaining fidelity and text-alignment.

2026-03-26

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name	kernelevole-agentic
title	KernelEvolve: Scaling Agentic Kernel Coding for AI Accelerators
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2512.23236
keywords	["kernel-optimization","agents","automated-synthesis","hardware-aware"]
description	Automate compute kernel optimization via agentic AI and retrieval-augmented prompting. Unified context-aware transformation function, hardware-specific constraint KB, self-improving state machine exploring kernel variants—achieving 1.25-17× speedups on production recommendation workloads in hours vs weeks of manual effort.

Overview

Autonomous kernel optimization across heterogeneous hardware via agentic synthesis.

Core Technique

Context-Aware Transformation:

# Single universal operator adapts via context
kernel = universal_operator.transform(
    operator_spec,
    hardware_context=gpu_type,
    optimization_history=prior_attempts
)

Retrieval-Augmented Prompting: Hardware-specific constraints guide generation.

Performance

1.25-17× speedup on production workloads
Hours vs weeks of manual optimization

References

Context-aware kernel generation
Hardware constraint knowledge bases
Self-improving kernel optimization

kernelevole-agentic

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Más de este repositorio

Overview

Core Technique

Performance

References

Más de este repositorio

Overview

Core Technique

Performance

References