New Neural Trick Helps Models Think in Longer Patterns

Written by extrapolate | Published 2025/04/01
Tech Story Tags: neural-memory | pointer-augmented-model | ai-length-extrapolation | sequence-modeling | panm-architecture | symbolic-reasoning-ai | generalization-in-transformers | machine-learning-memory

TLDR

Researchers introduced PANM, a plug-and-play memory module that mimics symbolic processing using pointers. PANM improves neural networks’ generalization to longer, unseen sequences and boosts performance in symbolic tasks, QA, and translation by modeling memory access more like a computer does—with physical addresses and pointer arithmetic.via the TL;DR App

Authors:

(1) Hung Le, Applied AI Institute, Deakin University, Geelong, Australia;

(2) Dung Nguyen, Applied AI Institute, Deakin University, Geelong, Australia;

(3) Kien Do, Applied AI Institute, Deakin University, Geelong, Australia;

(4) Svetha Venkatesh, Applied AI Institute, Deakin University, Geelong, Australia;

(5) Truyen Tran, Applied AI Institute, Deakin University, Geelong, Australia.

Table of Links

Abstract & Introduction

Experimental Results

Experimental Results Part 2

Related Works, Discussion, & References

Appendix A, B, & C

2.3 Pointer-Augmented Neural Memory (PANM)

2.3.1 Pointer Unit

2.3.2 Pointer-based Addressing Modes

2.3.3 The Controller

This paper is available on arxiv under CC BY 4.0 DEED license.

Written by extrapolate | Extrapolate: We uncover new insights.

Published by HackerNoon on 2025/04/01