Frozen Memory Is Not Enough: Rethinking External Memory as Extraction
Quick summary
arXiv:2608.17050v3 Announce Type: replace-cross Abstract: Methods for improving knowledge use in large language models typically fall into two regimes. Non-parametric retrieval offers flexible access to external knowledge, but adds retrieval latency, context overhead, and only shallow integration with the backbone. Parametric adaptation is efficient at inference time, but entangles knowledge with model weights and can be hard to update, audit, or transfer. Engram-style hashed memory occupies a middle regime: it stores learned information in an external, addressable table, yet consumes that tab
Key takeaways
- arXiv:2608.17050v3 Announce Type: replace-cross Abstract: Methods for improving knowledge use in large language models typically fall into two regimes.
- Non-parametric retrieval offers flexible access to external knowledge, but adds retrieval latency, context overhead, and only shallow integration with the backbone.
- Parametric adaptation is efficient at inference time, but entangles knowledge with model weights and can be hard to update, audit, or transfer.
Why it matters
This model development creates a new option for users and a new testing obligation for developers. A fixed evaluation set comparing quality, cost and failure behavior is more useful than launch claims.
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