EngramEdit: Decoupled Knowledge Updates in LLMs through Conditional Memory

arXiv:2610.10533 · cs.CL · Submitted 2026-10-07 · Read on arXiv

cs.CL

Submitted: 2026-10-07

Updated: 2026-10-07

Code: https://github.com/ModalityDance/EngramEdit

Terminology

• Embeddings — 92× in this paper · explained in 2 episodes
• Regularization — 50× in this paper · explained in 3 episodes
• Generalization — 41× in this paper · explained in 13 episodes
• Accuracy — 16× in this paper · explained in 1 episode
• Aggregation — 15× in this paper · explained in 1 episode
• Transformer — 11× in this paper · explained in 2 episodes
• Benchmark — 7× in this paper · explained in 2 episodes
• Adaptation — 5× in this paper · explained in 1 episode
• Consistency — 5× in this paper · explained in 3 episodes
• Fine-Tuning — 5× in this paper · explained in 5 episodes
• Multi-hop Reasoning — 5× in this paper · explained in 3 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Chain-of-Thought (CoT) — 2× in this paper · explained in 4 episodes
• Chain-of-Thought (CoT) Prompting — 2× in this paper · explained in 3 episodes
• Domain Adaptation — 2× in this paper · explained in 5 episodes
• F1 Score — 2× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 2× in this paper · explained in 6 episodes
• Personalization — 2× in this paper · explained in 2 episodes
• Sparsity — 2× in this paper · explained in 4 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• KV cache — 1× in this paper · explained in 3 episodes
• Mixture-of-Experts (MoE) — 1× in this paper · explained in 7 episodes
• Parameter-Efficient Fine-Tuning — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes

Sources

• DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression
• On the Design of Qwen3.8-Next Architecture: Evaluation, Efficiency, and Training Stability
• Scaling Embeddings Outperforms Scaling Experts in Language Models
• User as Engram: Internalizing Per-User Memory as Local Parametric Edits
• When to Adapt: Conditional Memory Adapters for Retention-Preserving Domain Specialization
• Modifying Memories in Transformer Models
• Memory Grafting: Scaling Language Model Pre-training via Offline Conditional Memory

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