# Efficient Context Propagating Perceiver Architectures for Auto-Regressive Language Modeling

arXiv: https://arxiv.org/abs/2412.06106 · HTML: https://mlsanity.com/paper/2412.06106/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 24 references that are in the map.
- First posted on arXiv 8 Dec 2024.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Pointer Sentinel Mixture Models](https://mlsanity.com/paper/1609.07843/) (2016)
- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://mlsanity.com/paper/1901.02860/) (2019)
- [Rethinking Attention with Performers](https://mlsanity.com/paper/2009.14794/) (2020)
- [Linformer: Self-Attention with Linear Complexity](https://mlsanity.com/paper/2006.04768/) (2020)
- [Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality](https://mlsanity.com/paper/2405.21060/) (2024)
- [Perceiver: General Perception with Iterative Attention](https://mlsanity.com/paper/2103.03206/) (2021)
- [Compressive Transformers for Long-Range Sequence Modelling](https://mlsanity.com/paper/1911.05507/) (2019)
- [Long Range Arena: A Benchmark for Efficient Transformers](https://mlsanity.com/paper/2011.04006/) (2020)
- [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://mlsanity.com/paper/2107.14795/) (2021)
- [Hungry Hungry Hippos: Towards Language Modeling with State Space Models](https://mlsanity.com/paper/2212.14052/) (2022)
- [xLSTM: Extended Long Short-Term Memory](https://mlsanity.com/paper/2405.04517/) (2024)
- [LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models](https://mlsanity.com/paper/2309.12307/) (2023)
- [Block-Recurrent Transformers](https://mlsanity.com/paper/2203.07852/) (2022)
- [Long-Short Transformer: Efficient Transformers for Language and Vision](https://mlsanity.com/paper/2107.02192/) (2021)
- [General-purpose, long-context autoregressive modeling with Perceiver AR](https://mlsanity.com/paper/2202.07765/) (2022)

## Built on by (most cited first, 1 of 1)

- [Neural Attention: A Novel Mechanism for Enhanced Expressive Power in Transformer Models](https://mlsanity.com/paper/2502.17206/) (2025)

## Abstract (arXiv)

One of the key challenges in Transformer architectures is the quadratic complexity of the attention mechanism, which limits the efficient processing of long sequences. Many recent research works have attempted to provide a reduction from the O(n2) time complexity of attention to semi-linear complexity. However, it remains an unsolved problem in the sense of maintaining high performance when complexity is reduced. One of the important works in this respect is the Perceiver class of architectures that have demonstrated excellent performance, while reducing the computation complexity. In this paper, we use the PerceiverAR as a basis and explore the design space of different trade-offs between preserving context and reducing attention complexity. To this end, we develop four new architectural paradigms, the best performing of which we denote as the Efficient Context propagating Perceiver (ECP). ECP has two major advantages over the PerceiverAR. First, the ECP architecture overcomes the main drawback of PercieverAR by utilizing both the context and the latent sequences in autoregressive training. Second, the ECP architecture operates with the same attention complexity as LongLoRA, making it computationally efficient. More importantly, via pairwise segment attention, it extracts better information resulting in improved language modeling. Empirically, we demonstrate that the ECP architecture significantly outperforms other state-of-the-art Transformer models on Wikitext-103, PG-19 and sCIFAR-10.