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Introduces RELIC, a framework for learning interpretable and composable skills in multi-agent planning via revealed principles, enabling privacy-preserving coordination and cross-agent skill transfer without sharing code.
This blog explores using LLM-guided autotuning to accelerate kernel configuration search in PyTorch's Helion DSL, replacing the slower Likelihood-Free Bayesian Optimization approach.
This paper presents a case study using an LLM-driven tree search algorithm (ERA) combined with a coding agent (AntiGravity) to autonomously generate high-efficiency three-dimensional photovoltaic structures, overcoming limitations of flat solar panels at mid-latitudes. The workflow includes iterative patching to eliminate reward hacking and discovers improved designs under various constraints.