# Beyond Episodic AI: Cognitive Field Networks for Biologically Inspired Persistent Cognition

arXiv: https://arxiv.org/abs/2609.16752 · HTML: https://mlsanity.com/paper/2609.16752/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 29 references that are in the map.
- First posted on arXiv 15 Sep 2026.

## Builds on (most cited first, 13 of 13)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention](https://mlsanity.com/paper/2006.16236/) (2020)
- [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://mlsanity.com/paper/1901.02860/) (2019)
- [Linear Transformers Are Secretly Fast Weight Programmers](https://mlsanity.com/paper/2102.11174/) (2021)
- [Recurrent Memory Transformer](https://mlsanity.com/paper/2207.06881/) (2022)
- [Block-Recurrent Transformers](https://mlsanity.com/paper/2203.07852/) (2022)
- [Memformer: A Memory-Augmented Transformer for Sequence Modeling](https://mlsanity.com/paper/2010.06891/) (2020)
- [Associative Recurrent Memory Transformer](https://mlsanity.com/paper/2407.04841/) (2024)
- [Staircase Attention for Recurrent Processing of Sequences](https://mlsanity.com/paper/2106.04279/) (2021)
- [The Recurrent Transformer: Greater Effective Depth and Efficient Decoding](https://mlsanity.com/paper/2604.21215/) (2026)
- [Cognitive Field Theory: Memory-Dressed Collective Dynamics of Intelligence](https://mlsanity.com/paper/2601.10221/) (2026)
- [Infrared Organization and Critical Cognitive Field Formation in Transformer Dynamics](https://mlsanity.com/paper/2607.10923/) (2026)

## Abstract (arXiv)

Cognitive Field Theory (CFT) proposes that cognition arises from memory-dressed collective dynamics that generate a persistent macroscopic cognitive field. Here we develop a Cognitive Field Network (CFN), a recurrent Transformer in which the organized hidden field re-enters subsequent inference through [ Phin+1=Fθ(Xn+1,Phin). ] Rather than prescribing an explicit memory operation, the CFN allows new information to act on an already history-dependent collective state. We find that learning organizes persistent, content-dependent recurrent dynamics whose timescale increases systematically with the trained recurrent horizon. Semantic continuation propagates the recurrent state far beyond this horizon without replay of the target answer. Without content-specific support, the field exhibits finite passive relaxation, whereas periodic re-exposure to relevant input repeatedly renews the surviving state and drives it toward an approximately stationary nonzero regime. Unrelated-input and recurrence-off controls do not reproduce this behavior, while near-paraphrased re-exposure produces weaker renewal, demonstrating representation-sensitive persistence. These results distinguish three dynamical processes: collective memory dressing forms and sustains a history-dependent cognitive field, structured input reorganizes this field, and cross-cycle re-entry makes the resulting state causally available to subsequent inference. The CFN therefore provides a controlled computational platform for studying persistent, history-dependent cognitive dynamics without a separately prescribed memory system.