>> Large language models (LLMs) have demonstrated incredible reasoning abilities, penetrating an increasing number of domains in our lives. This is now establi

>> Large language models (LLMs) have demonstrated incredible reasoning abilities, penetrating an increasing number of domains in our lives. This is now established orthodoxy, a bit like astrology in ancient times but it is complete nonsense. Nope, LLMs have not demonstrated any credible, or incredible reasoning abilities. They have demonstrated an excellent ability for approximate retrieval of previously observed answers (with variations, which should not surprise anyone given that those are generative models) but they fail spectacularly when they have to "reason" in contexts where they really can't have seen the answer anywhere before. For example, the "randomised mystery blocksworld" from this paoer: LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench https://arxiv.org/abs/2409.13373 https://arxiv.org/abs/2409.13373 "Randomised Mystery Blocksworld" is a version of the good old blocksworld planning benchmark where the names of obejcts and actions have been changed to random strings. The vast majority of LLMs score pathetically low in this, but much better in non-randomised versions, very clearly demonstrating excellent memorisation skills, but pitiful reasoning ability. As you 'd expect from a, well, language. model. >> A possible explanation for this is that the thought tokens allow the model to explore multiple possible branches before committing to a specific path, whereas chain-of-thought reasoning chooses a direction from the start. This ability is somewhat similar to Breadth-First Search (BFS). Why BFS in particular? Why not DFS or A*? I can't see any breadth-first bias in those graphs. BFS is not the only graph-traversing algorithm.