Meta Expands Long-Context Memory with SDM
burny_tech · x · 2026-07-12
A repost introduces Meta's new paper proposing Sparse Delta Memory (SDM) to address a key bottleneck in AI efficiency: long-context memory.
Core Idea
- Linear RNNs are computationally efficient, but their fixed hidden state limits long-context performance.
- SDM uses a sparse addressing mechanism to extend read/write to a much larger explicit memory.
- This increases state capacity by orders of magnitude without adding extra FLOPs.
Claimed Effects
- Under strict isoFLOP constraints, SDM significantly improves:
- In-context learning
- Long-context retrieval
- General reasoning
- The author believes this is important for future long-chain AI agents, as it expands memory capacity without linearly increasing computation.
More from Infra
- LLM Serving Metrics Thread: Why TPOT and Uptime Make or Break User Experience — abhijithneil · 2026-09-11
- PlanetScale launches sharded Postgres: 768 servers acting as one, 1PB scale — dhruv2038 · 2026-09-11
- Can a 7900 XTX 24GB run Qwen locally? Reddit seeks ROCm tok/s benchmarks — thenomadexplorerlife · 2026-09-11
- RTK Terminal Compression Cuts Tokens but Leaves Your AI Coding Bill Unchanged — Bartaseth · 2026-09-11
- SF Compute founder: buying compute is 'an absolutely awful experience' right now — IgorCarron · 2026-09-11
- SmolVM open-sources persistent computer infrastructure for agents that outlive chat sessions — aniketmaurya · 2026-09-11