# Epistemic Memory: A Validity Layer for Self-Maintaining Intelligent Systems

arXiv: https://arxiv.org/abs/2510.16899 · HTML: https://mlsanity.com/paper/2510.16899/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 57 references that are in the map.
- First posted on arXiv 19 Oct 2025.

## Builds on (most cited first, 28 of 28)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Dream to Control: Learning Behaviors by Latent Imagination](https://mlsanity.com/paper/1912.01603/) (2019)
- [Concrete Problems in AI Safety](https://mlsanity.com/paper/1606.06565/) (2016)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [World Models](https://mlsanity.com/paper/1803.10122/) (2018)
- [MemGPT: Towards LLMs as Operating Systems](https://mlsanity.com/paper/2310.08560/) (2023)
- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift](https://mlsanity.com/paper/1906.02530/) (2019)
- [Learning Latent Dynamics for Planning from Pixels](https://mlsanity.com/paper/1811.04551/) (2018)
- [Mastering Diverse Domains through World Models](https://mlsanity.com/paper/2301.04104/) (2023)
- [Object-Centric Learning with Slot Attention](https://mlsanity.com/paper/2006.15055/) (2020)
- [Neural Turing Machines](https://mlsanity.com/paper/1410.5401/) (2014)
- [Memory Networks](https://mlsanity.com/paper/1410.3916/) (2014)
- [Multi-Object Representation Learning with Iterative Variational Inference](https://mlsanity.com/paper/1903.00450/) (2019)
- [Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes](https://mlsanity.com/paper/1610.09027/) (2016)
- [Model-Based Bayesian Exploration](https://mlsanity.com/paper/1301.6690/) (2013)
- [ColoRadar: The Direct 3D Millimeter Wave Radar Dataset](https://mlsanity.com/paper/2103.04510/) (2021)

## Abstract (arXiv)

AI memory mechanisms primarily focus on preserving information content, often neglecting the validity conditions under which knowledge remains applicable, leading to semantic coordinate drift when agents move, change sensors, or encounter novel environments. This paper proposes epistemic memory as a validity-maintenance layer that governs when stored knowledge remains applicable. We formalize the dynamic epistemic quotient, an observation-induced equivalence structure over hypotheses that evolves with an agent's interaction history and sensing capabilities. We derive a pairwise incompatibility lower bound showing that any fixed semantic representation must incur unavoidable error across changing epistemic boundaries. We further identify a failure mode under symmetric crossing quotients, where overlap-based class-level transport becomes independent of pre-transition belief, motivating preservation of within-class hypothesis provenance. We introduce Observable Belief Memory (OBM) as a constructive epistemic governance architecture, combining the current epistemic quotient, belief over quotient classes, and within-class provenance through a posterior-consistent update. Controlled experiments demonstrate that explicit epistemic tracking improves robustness under changing observation conditions, particularly when class-level correspondence becomes insufficient. These results suggest that self-maintaining intelligent systems may benefit from reasoning not only about hidden states, but also about the evolving validity of the representations through which those states become knowable.