Trained Persistent Memory for Frozen Decoder-Only LLMs

📰 ArXiv cs.AI

Researchers explore trained persistent memory for frozen decoder-only LLMs, enabling persistent latent-space memory

advanced Published 25 Mar 2026

Action Steps

Investigate the application of trained memory adapters to decoder-only LLMs
Analyze the lateral-memory framework and its potential for enabling persistent memory
Evaluate the effectiveness of trained persistent memory in decoder-only LLMs through experiments and comparisons

Who Needs to Know This

ML researchers and AI engineers benefit from this research as it improves the capabilities of decoder-only LLMs, allowing for more effective use in applications where memory is crucial

Key Insight

💡 Trained memory adapters can provide decoder-only LLMs with persistent latent-space memory, even in the absence of cross-attention pathways

Key Takeaways

Researchers explore trained persistent memory for frozen decoder-only LLMs, enabling persistent latent-space memory

Full Article

Title: Trained Persistent Memory for Frozen Decoder-Only LLMs

Abstract:
arXiv:2603.22329v1 Announce Type: cross Abstract: Decoder-only language models are stateless: hidden representations are discarded after every forward pass and nothing persists across sessions. Jeong (2026a) showed that trained memory adapters give a frozen encoder-decoder backbone persistent latent-space memory, building on the lateral-memory framework of Jeong (2026b,c). Here we ask whether the same principle transfers to the decoder-only setting, where no cross-attention pathway exists and me

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