Caterpillar of Thoughts: The Optimal Test-Time Algorithm for Large Language Models

ArXi:2603.22784v1 Announce Type: new Large language models (LLMs) can often produce substantially better outputs when allowed to use additional test-time computation, such as sampling, chain of thought, backtracking, or revising partial solutions. Despite the growing empirical success of such techniques, there is limited theoretical understanding of how inference time computation should be structured, or what constitutes an optimal use of a fixed computation budget.