---
title: "A Human-Inspired Reading Agent with Gist Memory of Very Long"
description: "This offers a cheap, model-agnostic route to book-length inputs, although the size of the gist memory… 53 references, cited by 45 papers here."
authors: ["Kuang-Huei Lee", "Xinyun Chen", "Hiroki Furuta", "John Canny", "Ian Fischer"]
year: 2024
venue: "arXiv:2402.09727"
source: "https://arxiv.org/abs/2402.09727"
doi: "10.48550/arxiv.2402.09727"
arxiv: "2402.09727"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/lee-2024-human-inspired-reading-agent-gist/"
---
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# A Human-Inspired Reading Agent with Gist Memory of Very Long Contexts

**Authors:** Kuang-Huei Lee, [Xinyun Chen](https://md.research.elamur.ai/free/en/authors/xinyun-chen/), [Hiroki Furuta](https://md.research.elamur.ai/free/en/authors/hiroki-furuta/), [John Canny](https://md.research.elamur.ai/free/en/authors/john-canny/) and [Ian Fischer](https://md.research.elamur.ai/free/en/authors/ian-fischer/) · **Year:** 2024 · **Venue:** arXiv:2402.09727 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2402.09727) · [PDF](https://arxiv.org/pdf/2402.09727) · [DOI 10.48550/arxiv.2402.09727](https://doi.org/10.48550/arxiv.2402.09727) · [arXiv 2402.09727](https://arxiv.org/abs/2402.09727) · [OpenAlex](https://openalex.org/W4391912783)

**Citations:** 1 citations (OpenAlex, 2026-10-14) · [45](https://md.research.elamur.ai/free/en/papers/lee-2024-human-inspired-reading-agent-gist/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Large language models have a hard limit on input length, and their accuracy often drops on long inputs well before that limit. ReadAgent, from Google DeepMind (ICML 2024), addresses this without training or architectural changes. It is a set of zero-shot prompts wrapped around an existing LLM (PaLM 2-L in the experiments), and its design follows how people read. The model first splits a long document into episodes, called "pages", by choosing natural break points itself. It then compresses each page into a short gist tagged with its page number. When given a task, it reviews the full set of gists and decides which original pages to re-read. It can fetch them all at once (ReadAgent-P) or one by one (ReadAgent-S), and it answers using the gists plus the retrieved raw text. On QuALITY, NarrativeQA and QMSum, it beats retrieval baselines, gist-only reading and, where the text fits, full-text input. It also extends effective context by 3.5–20×. This offers a cheap, model-agnostic route to book-length inputs, although the size of the gist memory still caps scalability.

## Key points
- On the NarrativeQA Gutenberg test set (texts average 71k words, maximum 343k), ReadAgent beats the strongest retrieval baseline by 12.97% in LLM rating and by 31.98% in ROUGE-L. Effective context grows about 20×.
- On QuALITY, ReadAgent outperforms feeding the full article into an 8K window and reads 20.4% fewer words overall, reaching a 3.5× effective context.
- Letting the LLM decide page boundaries beats uniform-length splitting on QuALITY (86.83% vs. 85.71%). Default pagination uses 280/600 min/max words per page.
- Prompt-based page selection outperforms neural top-1 retrieval over the same gist memory (84.13% vs. 82.65%). On QMSum, all gisted transcripts are under 5,000 words, so they fit entirely in PaLM 2-L's context.
- The authors list known limitations. Gist memory cannot provide unlimited context, and very long gist memories degrade performance. Elided details may also raise hallucination risk. An appendix shows a promising adaptation to web navigation.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Kuang-Huei Lee, Xinyun Chen, Hiroki Furuta, John Canny and Ian Fischer. 2024. A Human-Inspired Reading Agent with Gist Memory of Very Long Contexts. arXiv:2402.09727.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{lee2024human,
  title = {{A Human-Inspired Reading Agent with Gist Memory of Very Long Contexts}},
  author = {Kuang-Huei Lee, Xinyun Chen, Hiroki Furuta, John Canny and Ian Fischer},
  year = {2024},
  doi = {10.48550/arxiv.2402.09727},
  eprint = {2402.09727},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2402.09727}
}
```

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*Updated 2 November 2026*