memoir-memory-rewriting-neural-networks

name	memoir-memory-rewriting-neural-networks
description	Methodology for analyzing the effects of memory rewriting during neural network inference, comparing coupled vs read-only pondering architectures.

Memoir-Memory-Rewriting-Neural-Networks

Overview

This methodology investigates whether neural network models should write to their memory while thinking (during inference). The Memoir architecture combines per-sample fast memory, shared slow parameters, variable-depth latent recurrence, and a future-latent energy objective to test the coupling between memory reading and writing during pondering iterations.

Key Contributions

• Coupled vs Read-Only Comparison: Direct comparison between architectures where pondering iterations can rewrite the fast memory tier they read from vs. read-only architectures
• Learning Speed Analysis: Demonstrates that coupled memory rewriting incurs a learning-speed penalty at fixed training budgets but reaches equivalent final performance
• Energy Signal Integrity: Shows that memory rewriting does not corrupt the energy signal - the energy margin grows and holds during training
• Efficient Implementation: Provides kernel restructuring that reduces delta-rule forward time from 0.907 ms to 0.351 ms

Core Methodology

1. Architecture Design: Implement Memoir with per-sample fast memory, shared slow parameters, variable-depth latent recurrence, and future-latent energy objective
2. Controlled Experiment: Compare coupled arm (memory rewriting allowed) against read-only pondering arm with identical parameters, data, optimizer, schedule, and seeds
3. Procedural Associative Recall: Test on procedural associative recall tasks with key interference
4. Performance Measurement: Measure recall accuracy at different training steps (240 vs 960 steps) across multiple random seeds
5. Statistical Analysis: Perform paired t-tests to determine significance of differences between architectures

Experimental Results

• Early Training (240 steps): Read-only recall = 0.6557 [0.5953, 0.7160], Coupled recall = 0.5203 [0.4522, 0.5883]
• Statistical Significance: Paired t = 3.23 on 11 degrees of freedom, 95% CI [0.0431, 0.2277] on difference
• Final Performance (960 steps): Both architectures reach 日晚间000 recall accuracy
• Conclusion: Memory rewriting causes learning-speed penalty but not capability penalty

Applications

• Neural network architecture design
• Memory-augmented neural networks
• Recurrent neural network optimization
• Learning dynamics analysis
• Energy-based neural network models

Implementation Guidelines

• Use matched parameter counts (81,738 total, 76,362 trainable)
• Ensure identical forward multiply-accumulate counts between conditions
• Implement proper energy objective with future-latent constraints
• Apply kernel restructuring for efficient delta-rule computation
• Use sufficient training steps to observe convergence behavior

Activation Keywords

• memory rewriting
• neural network inference
• coupled memory
• read-only pondering
• learning speed penalty
• energy objective
• variable-depth recurrence
• procedural associative recall

References

• arXiv:2607.20792
• Original paper: "Memoir: Should a Model Write to Its Memory While It Thinks?"
• Authors: Jaber Jaber, Osama Jaber
• Code: https://github.com/RightNow-AI/Memoir