Neuralese
In artificial intelligence (AI) research, neuralese is a method where large language models (LLMs) perform intermediate reasoning steps in their high-dimensional latent space (vector embeddings) rather than outputting human-readable text tokens. While standard chain-of-thought reasoning forces a model to generate a sequence of words, neuralese allows the model to pass raw, continuous vectors between computational layers, creating a high-bandwidth, non-linguistic reasoning channel.[1][2]
See also
[edit]
Look up neuralese in Wiktionary, the free dictionary.
References
[edit]- ↑ Hao, Shibo, et al. "Training Large Language Models to Reason in a Continuous Latent Space." arXiv preprint arXiv:2412.06769, 9 December 2024.
- ↑ "Translating Neuralese". ar5iv. Retrieved 2026-08-24.
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