Reinforcement learning the chromatic symmetric function

We propose a conjectural counting formula for the coefficients of the chromatic symmetric function of unit interval graphs using reinforcement learning, providing a machine-learning-proof of the Stanley-Stembridge positivity conjecture. The formula counts specific disjoint cycle-tuples in the graphs, referred to as Eschers, which satisfy certain concatenation conditions. These conditions are identified by a reinforcement learning model and are independent of the particular unit interval graph and depend only on a small set of discrete combinatorial properties, resulting in a universal counting expression.