ProTTT: Learning to Learn Semantic User Memory with Test-Time Training

arXiv:2609.32564 · cs.AI · Submitted 2026-09-26 · Read on arXiv

cs.AI

Submitted: 2026-09-26

Updated: 2026-09-26

Terminology

• Personalization — 52× in this paper · explained in 2 episodes
• Meta-learning — 39× in this paper · explained in 2 episodes
• Benchmark — 15× in this paper · explained in 2 episodes
• RAG — 15× in this paper · explained in 3 episodes
• Accuracy — 9× in this paper · explained in 1 episode
• Hypernetwork — 7× in this paper · explained in 3 episodes
• AUC — 6× in this paper · explained in 2 episodes
• Adaptation — 4× in this paper · explained in 1 episode
• Generalization — 4× in this paper · explained in 13 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Lexical Diversity — 3× in this paper · explained in 2 episodes
• Core — 2× in this paper · explained in 2 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 4 episodes
• Distribution Shift — 1× in this paper · explained in 6 episodes
• Fine-Tuning — 1× in this paper · explained in 5 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
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Parameter-Efficient Fine-Tuning — 1× in this paper · explained in 2 episodes

Sources

• GradMem: Learning to Write Context into Memory with Test-Time Gradient Descent
• delta-mem: Efficient Online Memory for Large Language Models
• User-LLM: Efficient LLM Contextualization with User Embeddings
• Integrating Summarization and Retrieval for Enhanced Personalization via Large Language Models
• End-to-End Test-Time Training for Long Context

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