Tree　of　Thoughts　(ToT)　has　emerged　as　a　powerful　framework　for　complex　reasoning　with　Large　Language　Models　(LLMs).　However,　existing　ToT　methods　often　generate　highly　similar　paths,　leading　to　inefficient　resource　utilization.　We　present　Entropy-Guided　Tree　of　Thoughts,　a　novel　approach　that　enhances　the　diversity　and　efficiency　of　path　generation　in　LLM　reasoning.　Our　method　introduces　three　key　innovations:　(1)　a　dynamic　entropy　evaluation　mechanism　that　combines　branch-specific　and　global　probability　distributions,　(2)　a　path　similarity　penalty　that　prevents　redundant　exploration　(3)　an　adaptive　diversity　constraint　that　dynamically　adjusts　search　strategies.　Preliminary　experiments　on　the　Game　of　24　showed　promising　results　in　promoting　solution　diversity　while　maintaining　reasoning　quality.　©　2025　IEEE.