Going Deeper With Memories
DeepMem is a causal multiscale byte-level architecture that separates semantic compression from exact byte preservation.
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- Primary sourceHugging Face
- Discovery linkHugging Face
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DeepMem is a causal multiscale byte-level architecture that separates semantic compression from exact byte preservation. A learned invertible subband transform builds a hierarchy of shorter sequences, while a lossless carrier and detail sidebands preserve byte identity. Fast weight memory blocks operate along the encoder, and an attention-based super-resolution decoder restores byte-resolution representations. The paper reports stronger language modeling than a matched byte-level Transformer on FineWeb-Edu and PG19, the best macro average across evaluated FLORES languages, and about 7,600x fewer counted FLOPs for bottleneck refinement than equivalent full-resolution reasoning.
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Source claims not independently checked; the FLOP reduction is a counted-FLOPs figure and the benchmark results are reported by the author.
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Source claims not independently checked; the FLOP reduction is a counted-FLOPs figure and the benchmark results are reported by the author.
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DeepMem outperforms a matched byte-level Transformer on FineWeb-Edu and PG19 language modeling.
Source: Hugging Face ↗Stored source-based brief · key point 1 · AINewsIQ paraphraseIt reports the best macro average performance across the evaluated FLORES languages.
Source: Hugging Face ↗Stored source-based brief · key point 2 · AINewsIQ paraphraseBottleneck refinement needs roughly 7,600 times fewer counted FLOPs than equivalent full-resolution reasoning.
Source: Hugging Face ↗Stored source-based brief · key point 3 · AINewsIQ paraphraseLossless carriers and explicit detail sidebands preserve exact byte identity during compression.
Source: Hugging Face ↗Stored source-based brief · key point 4 · AINewsIQ paraphraseLimits from the stored brief
Source claims not independently checked; the FLOP reduction is a counted-FLOPs figure and the benchmark results are reported by the author.
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Why this may matter
Byte-level models avoid tokenizer dependence but are costly at full resolution. DeepMem proposes a way to compress sequences while keeping exact byte information, which could make tokenizer-free modeling more practical if the reported results hold.
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Source claims not independently checked; the FLOP reduction is a counted-FLOPs figure and the benchmark results are reported by the author.
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