# EpMAN: Episodic Memory AttentioN for Generalizing to Longer Contexts

arXiv: https://arxiv.org/abs/2502.14280 · HTML: https://mlsanity.com/paper/2502.14280/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2025.

## Built on by (most cited first, 4 of 4)

- [A Survey of Context Engineering for Large Language Models](https://mlsanity.com/paper/2507.13334/) (2025)
- [TrajAudit: Automated Failure Diagnosis for Agentic Coding Systems](https://mlsanity.com/paper/2605.26563/) (2026)
- [A Review of Incorporating Psychological Theories in LLMs](https://mlsanity.com/paper/2505.00003/) (2025)
- [Beyond Sliding Windows: Learning to Manage Memory in Non-Markovian Environments](https://mlsanity.com/paper/2512.19154/) (2025)

## Abstract (arXiv)

Recent advances in Large Language Models (LLMs) have yielded impressive successes on many language tasks. However, efficient processing of long contexts using LLMs remains a significant challenge. We introduce EpMAN -- a method for processing long contexts in an textitepisodic memory module while textitholistically attending to semantically relevant context chunks. The output of textitepisodic attention is then used to reweigh the decoder's self-attention to the stored KV cache of the context during training and generation. When an LLM decoder is trained using EpMAN, its performance on multiple challenging single-hop long-context recall and question-answering benchmarks is found to be stronger and more robust across the range from 16k to 256k tokens than baseline decoders trained with self-attention, and popular retrieval-augmented generation frameworks.