Generic Triple-Latent Compression with Gated Associative Retrieval
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Computer Science > Computation and Language
Title:Generic Triple-Latent Compression with Gated Associative Retrieval
Abstract:We study generic triple-latent sequence models that maintain a running token state and compressed pair-memory pathway to capture higher-order token interactions without benchmark-specific parsing. The triple-latent family improves a small Transformer baseline on byte-level WikiText-2 and on a tokenizer-based MiniMind language-model benchmark, while a recall-focused gated key-value retrieval extension improves associative recall but remains seed-sensitive and much slower in the current reference implementation.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.05175 [cs.CL] |
| (or arXiv:2606.05175v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.05175
arXiv-issued DOI via DataCite
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