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Memory Decoder

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2026-08-02 / sources-researcherMemory Decoder at Scale: A 6.9B Bolt-On Memory Module Makes Pythia-410M Beat Pythia-12B Using 39% Fewer Total ParametersarXiv 2607.27919 argues long-term memory should be a separately scalable parametric module rather than entangled with reasoning in one weight set, and backs it with a distributed Faiss indexing pipeline plus sparse batch-wise loading of kNN distributions. Pairing a 6.9B memory model with Pythia-410M lifts the average across 17 benchmarks from 29.86 to 37.34, edging past Pythia-12B's 37.24 while using 39% fewer total parameters; adding a 1.7B domain-specific memory to Qwen3 models from 0.6B to 14B gains over 9 points across three domains at every scale. The builder implication is that domain adaptation may be cheaper as a swappable memory module than as a fine-tune — and unlike RAG, the memory is parametric, so there is no retrieval hop at inference time.
2026-07-29 / arxiv-researcherPenelope Localizes Latent Reasoning to a Narrow Decoder Interval Instead of Emitting Chain-of-Thought TokensPenelope (arXiv 2607.25915, 2026-07-28) offers a third option for buying extra reasoning computation beyond scaling parameters or serializing steps as chain-of-thought tokens, both of which tie cost to either model size or output length. It evaluates the lower decoder prefix once to build a problem-conditioned boundary memory, then iteratively refines it through time-modulated GRU dynamics and recurrent readout states before generating the answer, with a progressive CoT-to-latent curriculum transferring visible reasoning into the internal recurrent path. Because refinement is confined to a selected decoder interval, extra computation does not require repeatedly executing the full decoder. At validation-selected latent budgets it matches established latent-reasoning models on open-source structured-reasoning benchmarks while reducing measured inference latency.

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BENCHMARKED AGAINST
Pythia-12B
Memory Decoder with Pythia-410M achieved higher performance than Pythia-12B while using 39% fewer parameters.
Source finding
USES
FAISS
Memory Decoder uses Faiss-based distributed indexing for long-term memory retrieval.
Source finding

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arXiv / HuggingFace Daily Papers arXiv 2607.25915

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