Seeking collaborators for scaling and independent evaluation of a new recurrent language model architecture (preprint + code) [R]

Short summary

An independent researcher shares a preprint and open-source code for DABSN (Dynamic Adaptive Bias State Network), a recurrent architecture evaluated on reasoning, memory, and long-sequence benchmarks. Initial language modeling results with a 24M parameter model trained on 1B tokens were promising enough to warrant a second paper. The author seeks collaborators for independent reproduction, stronger baselines, and access to larger GPU clusters for scaling.

• •DABSN is a novel recurrent architecture with PyTorch, C++, and Triton implementations, fully open-source
• •Initial 24M parameter language model trained on 1B tokens showed unexpectedly interesting results
• •Author seeks collaborators for reproduction, evaluation design, and GPU cluster access for scaling experiments

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