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A researcher-in-the-loop pipeline combining LLM-guided program evolution, exact evaluation, and Lean formal proof checking discovers a new family of entanglement-assisted quantum error-correcting codes, closing 54 gaps in archived EAQECC tables with 115 verified records and 10 certified nonexistence results.
EliteTable is a self-improving, LLM-guided program-search algorithm that maintains a fixed-capacity table of high-scoring solutions to propose, edit, and remix candidate executable control policies, evaluated across six Gymnasium control tasks showing generalization gains in some tasks but needing ablations to establish relative performance.
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.