Generic Triple-Latent Compression with Gated Associative Retrieval
Quick Answer
The study introduces a generic triple-latent sequence model that enhances a small Transformer baseline on byte-level WikiText-2 and MiniMind benchmarks.
Quick Take
The study introduces a generic triple-latent sequence model that enhances a small Transformer baseline on byte-level WikiText-2 and MiniMind benchmarks. A gated key-value retrieval extension improves associative recall but is slower and sensitive to seed variations.
Key Points
- Triple-latent models capture higher-order token interactions without specific parsing.
- Improved performance on byte-level WikiText-2 and tokenizer-based MiniMind benchmarks.
- Gated retrieval extension enhances recall but is slower and seed-sensitive.
- Current implementation shows significant performance deltas compared to baseline.
- Focus on generic models allows broader applicability across different tasks.
Article Excerpt
From source RSS / original summaryarXiv:2606. 05175v1 Announce Type: new 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.
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