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Can reasoning scale in latent space without tokens?
A broader line of inquiry — a family of 40 specific questions the research asks around this. Follow one into its inquiring-line page, or move sideways to a related line below.
Questions in this line of inquiry 40
Specific inquiring lines the field asks around this — ordered from the most general framing down to the most specific angle.
- Why might latent reasoning capture types of thinking that verbalized CoT cannot?
- Can latent reasoning stay readable without explicit token-by-token decoding?
- Can models hide their reasoning in continuous space rather than natural language?
- Can latent reasoning scale test-time compute without verbalized tokens or special training?
- Can latent reasoning in continuous space scale beyond supervised reasoning tasks?
- Can latent reasoning achieve the same substitution without tokens?
- Can latent reasoning scale test-time compute without verbal tokens?
- Can reasoning happen in latent space without chain of thought?
- How do compact latent dynamics enable planning without explicit chain of thought?
- Can continuous latent reasoning match discrete chain-of-thought without training modifications?
- Why does latent-level prediction beat token-level prediction for reasoning?
- Can latent space represent reasoning dimensions that text cannot?
- Does the latent-explicit gap widen beyond 3B parameters on reasoning tasks?
- How do recursive language models rethink where to store reasoning?
- Can language models generate plausible latent thoughts without human annotation?
- Does latent manipulation outperform token-level prediction for efficiency?
- Can latent reasoning architectures work as retrofits to existing models?
- Can LLMs decode their own hidden activations into natural language?
- How do soft thought tokens differ from decoded assistant outputs?
- Can activation probes detect reasoning that models omit from text?
- How do covert thoughts differ from chain-of-thought reasoning in language models?
- Do latent sequence vectors outperform per-token latent iterative computation for reasoning?
- Can structured workflows unlock latent reasoning abilities that raw models don't show?
- Do models leak their true associations through reasoning traces and behavior?
- Can articulating latent reasoning processes improve transfer across domains?
- Why does recursion on latent state drive generalization better than hierarchy?
- How much explicit verbal signal must latent chains retain to perform well?
- What makes looped latent computation more efficient than scaling attention capacity?
- Does activation masking prevent the decoder from taking interpretability shortcuts?
- Can latent reasoning mechanisms and recursive tracking mechanisms be combined effectively?
- How do hidden embeddings preserve more information than discrete tokens?
- What affordances do normalizing flows add over opaque vector reasoning?
- How does latent state recursion differ mechanistically from chain-of-thought prompting?
- Can activation decoders discover hidden system prompts from user-model conversations?
- How does this compare to trained autoencoder approaches for thought sharing?
- How does LatentQA differ from predefined concept steering like representation engineering?
- How do LLMs infer information that was explicitly censored?
- What makes internal embeddings useful as multimodal input for language model training?
- What does leveraging internal representations during training actually mean operationally?
- Why do foundation models fail at hidden state prediction despite sequence accuracy?