Tag
This paper introduces Communicative Language Symbolism Routing (CLSR), where multiple LLM agents autonomously invent and evolve compact symbolic languages for reasoning, achieving 3-6x token reduction over chain-of-thought while maintaining accuracy.
This paper introduces the Red Queen Gödel Machine (RQGM), an evolutionary framework for recursive self-improvement under non-stationary utilities, where agents and evaluators co-evolve, improving performance on coding tasks, scientific writing, and Olympiad-level proof grading.
BenchEvolver is an evolutionary framework that automatically generates harder coding problems from existing ones, creating challenging benchmarks that maintain validity and diversity while enabling model self-improvement and enhanced training performance.
The paper introduces Kernel Discovery, an LLM-driven evolutionary framework for high-dimensional Bayesian optimization that searches a broader kernel space and achieves state-of-the-art results on benchmarks.