[HN Gopher] Dynamic Large Concept Models: Latent Reasoning in an...
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       Dynamic Large Concept Models: Latent Reasoning in an Adaptive
       Semantic Space
        
       Author : gmays
       Score  : 47 points
       Date   : 2026-01-08 16:31 UTC (6 hours ago)
        
 (HTM) web link (arxiv.org)
 (TXT) w3m dump (arxiv.org)
        
       | sorenjan wrote:
       | Would this enable a model to learn concepts in one language and
       | generate answers about it in another, as long as it learns
       | general translations between them?
        
         | notrealyme123 wrote:
         | My educated guess: Not more than any other LLM. The text-latent
         | encoder and latent-text decoder just find am more efficient
         | representation of the tokens, but it's more of a compression
         | instead of turning words/sentences into abstract concepts.
         | There will be residuals of the input language be in there.
        
       | notrealyme123 wrote:
       | Broken citations. My inner reviewer gets sad. :(
        
       | miven wrote:
       | I'm really glad that these HNet-inspired approaches are getting
       | traction, I'm a big fan of that paper.
       | 
       | Though I wonder how much of the gains in this case are actually
       | due to 75% extra parameters compared to the baseline, even if the
       | inference FLOPs are matched.
       | 
       | Can't help but see this as a just different twist on parameter
       | use sparsity idea leveraged by MoE models, as those also gain in
       | performance at constant forward pass FLOPs because of extra
       | parameters.
        
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       (page generated 2026-01-08 23:01 UTC)