---
title: "Tracking the World State with Recurrent Entity Networks (2016)"
description: "The authors propose the Recurrent Entity Network (EntNet). 26 references, cited by 89 papers here."
authors: ["Mikael Henaff", "Jason Weston", "Arthur Szlam", "Antoine Bordes", "Yann LeCun"]
year: 2016
venue: "arXiv:1612.03969 · ICLR 2017"
source: "https://arxiv.org/abs/1612.03969"
doi: "10.48550/arxiv.1612.03969"
arxiv: "1612.03969"
licence: "arXiv — non-exclusive licence to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/henaff-2016-tracking-world-state-recurrent-entity/"
---
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# Tracking the World State with Recurrent Entity Networks

**Authors:** Mikael Henaff, [Jason Weston](https://md.research.elamur.ai/free/en/authors/jason-weston/), [Arthur Szlam](https://md.research.elamur.ai/free/en/authors/arthur-d-szlam/), [Antoine Bordes](https://md.research.elamur.ai/free/en/authors/antoine-bordes/) and [Yann LeCun](https://md.research.elamur.ai/free/en/authors/yann-lecun/) · **Year:** 2016 · **Venue:** [arXiv:1612.03969 · ICLR 2017](https://md.research.elamur.ai/free/en/venues/international-conference-on-learning-representations-iclr/) ([ICLR 2016](https://md.research.elamur.ai/free/en/venues/international-conference-on-learning-representations-iclr/2016/)) · **Licence:** [arXiv — non-exclusive licence to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/1612.03969) · [PDF](https://arxiv.org/pdf/1612.03969) · [DOI 10.48550/arxiv.1612.03969](https://doi.org/10.48550/arxiv.1612.03969) · [arXiv 1612.03969](https://arxiv.org/abs/1612.03969) · [OpenAlex](https://openalex.org/W2563734883)

**Citations:** 139 citations (OpenAlex, 2026-10-14) · [89](https://md.research.elamur.ai/free/en/papers/henaff-2016-tracking-world-state-recurrent-entity/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper addresses how a model can keep an up-to-date internal picture of a changing situation while it reads, such as the state of a short story, instead of reasoning only once a question arrives. The authors propose the Recurrent Entity Network (EntNet). It is a memory-augmented model with a fixed set of slots, each holding a learned key and a content vector. A gated recurrent update, shared across all slots, modifies them in parallel. A slot's gate opens when the encoded sentence matches its key (location addressing) or its current content, and a normalization step lets older information fade. Sentences are encoded with a learned multiplicative mask over word embeddings, and a one-hop attention readout produces answers. On the 20 bAbI tasks, EntNet is the first method to solve all of them with 10k training examples. It also succeeds on a new grid-world task that needs many supporting facts and generalizes to longer sequences than it saw in training. On the Children's Book Test it is competitive while reading in a single pass. It offers a simpler, parallel alternative to NTM/DNC-style memories.

## Key points
- The memory is split into blocks: m ≈ 5–20 slots, each with about 20–100 units. The update matrices U, V, W are shared across slots, and keys change only during training. A "tied" variant binds keys to entity word embeddings.
- The new world-model task places two agents on a 10 × 10 grid. They turn or move 1–5 steps, and the model must answer where each agent ends up. EntNet beats LSTMs and MemN2N on this task.
- With plain BoW input encoding on bAbI 10k, EntNet reaches a mean error of 10.9 with 6 failed tasks, compared with MemN2N at 15.6 and 9. Tying keys to entities gave no benefit in this setting.
- With only 1k training samples, EntNet performs worse than MemN2N, with a mean error of 29.6 versus 13.9 and 15 versus 11 failed tasks.
- In joint training on all tasks, averaged over seeds, EntNet's mean error is 9.7 ± 2.6 with about 5 failed tasks. The DNC scores 16.7 ± 7.6 with about 11.2 failed tasks. The DNC's best single seed is stronger, but its results vary much more between runs.

*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: Mikael Henaff, Jason Weston, Arthur Szlam, Antoine Bordes and Yann LeCun. 2016. Tracking the World State with Recurrent Entity Networks. arXiv:1612.03969 · ICLR 2017.

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

```bibtex
@misc{henaff2016tracking,
  title = {{Tracking the World State with Recurrent Entity Networks}},
  author = {Mikael Henaff, Jason Weston, Arthur Szlam, Antoine Bordes and Yann LeCun},
  year = {2016},
  doi = {10.48550/arxiv.1612.03969},
  eprint = {1612.03969},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1612.03969}
}
```

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## References

26 references, 6 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 2 November 2026*