Alex Doboli

The work proposes dynamic context adaptation: a validation-generation loop in which a validation agent extracts structured diagnostic feedback from execution traces, a generation agent proposes multiple candidates per iteration, a knowledge graph supplies semantic constraints, and simulated annealing performs non-greedy selection, targeting the LLM code-generation limitation the authors call static binding; across eight problems the method outperforms zero-shot, Reflexion, and OpenEvolve on seven of eight problems at both 300 and 600 evaluations (p < 0.01), achieves the best score at 1000 evaluations on the primary motivating problem of cross-coupled optimization (0.694 vs. 0.681, p = 0.019, d = 0.52), and ablations identify structured execution feedback as the primary driver.

The work proposes dynamic context adaptation: a validation-generation loop in which a validation agent extracts structured diagnostic feedback from execution traces, a generation agent proposes multiple candidates per iteration, a knowledge graph supplies semantic constraints, and simulated annealing performs non-greedy selection, targeting the LLM code-generation limitation the authors call static binding; across eight problems the method outperforms zero-shot, Reflexion, and OpenEvolve on seven of eight problems at both 300 and 600 evaluations (p < 0.01), achieves the best score at 1000 evaluations on the primary motivating problem of cross-coupled optimization (0.694 vs. 0.681, p = 0.019, d = 0.52), and ablations identify structured execution feedback as the primary driver.