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All SourcesarXiv
#Retrieval-augmented generation

CADEvolve: Creating Realistic CAD via Program Evolution

Intermediate
Maksim Elistratov, Marina Barannikov et al.Feb 18arXiv

AI models that make CAD designs used to learn mostly from simple “draw-then-extrude” examples, so they struggled with real, complex parts.

#CAD#CadQuery#Image2CAD

Towards Automated Kernel Generation in the Era of LLMs

Intermediate
Yang Yu, Peiyu Zang et al.Jan 22arXiv

AI programs called LLMs can now help write the tiny, super-fast pieces of code (kernels) that make GPUs run AI models efficiently.

#LLM-driven kernel generation#GPU kernels#CUDA

Controlled Self-Evolution for Algorithmic Code Optimization

Intermediate
Tu Hu, Ronghao Chen et al.Jan 12arXiv

The paper introduces Controlled Self-Evolution (CSE), a smarter way for AI to write and improve code quickly under a tight budget of tries.

#Controlled Self-Evolution#Code optimization#Self-evolving agents

Memory in the Age of AI Agents

Intermediate
Yuyang Hu, Shichun Liu et al.Dec 15arXiv

This survey explains how AI agents remember things and organizes the whole topic into three clear parts: forms, functions, and dynamics.

#Agent memory#LLM memory#Retrieval-augmented generation