Cognitive field networks support persistent history dependent AI thinking
Beyond Episodic AI: Cognitive Field Networks for Biologically Inspired Persistent Cognition
Artificial Intelligence
Summary
This paper shows how a special type of AI, called a Cognitive Field Network (CFN), can keep information alive over time without having a separate memory system. The CFN updates its internal state using new inputs and its past state, allowing it to remember and continue patterns like human thinking does. The authors found that the CFN’s memory lasts longer when trained for longer horizons and that reintroducing relevant information helps maintain this ongoing memory. This model could help us understand how cognition persists naturally without explicitly programming memory steps.
What this means in practice
- •For ai system designers: Build AI models that maintain persistent, context-rich internal states without explicit memory modules for better long-term understanding.
- •For cognitive robotics teams: Implement persistent cognitive states in robots to support continuous sensing and decision making over extended interactions without explicit memory recall.
Authors
Byung Gyu Chae
Abstract
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 \[ Φ_{n+1}=F_θ(X_{n+1},Φ_n). \] 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.