🧪 Test?View on arXiv
baseVultra: Compact Nonlinear Recurrence with Reallocated Capacity
Not specified in the provided content
recurrent neural networkslanguage modelingmodel efficiencycandidate generation
2610.30154
Builder Relevance
2h ago80%
Abstract
baseVultra is a compact recurrent cell that reallocates recurrent computation towards candidate generation, achieving competitive performance in language modeling tasks.
Reality Card
Core Claim
baseVultra achieves a validation loss of 1.594 on TinyShakespeare, outperforming parameter-matched LSTM and GRU while using only 54% of the computation of GRU.
Method / Result
Validation loss of 1.594 on TinyShakespeare with a compact one-layer 200K setting.
Limitations
The benefits of baseVultra are concentrated in compact regimes and may not scale well with larger hidden sizes.
Paper to code
Verified implementation resources so builders can test the paper’s claims instead of stopping at the abstract.
No verified implementation link has been attached yet. AIBuzzHub will keep this panel separate from unverified search results.