Fusion Matters: Length-Aware Analysis of Positional-Encoding Fusion in Transformers

TL;DR

This paper investigates how different fusion strategies of positional encodings affect Transformer performance, especially on long sequences, revealing that fusion choice significantly impacts results in long-document tasks.

Contribution

The study provides a controlled empirical comparison of fusion methods in Transformers, demonstrating the importance of fusion design for long-sequence modeling and introducing a lightweight convolutional gating mechanism.

Findings

Fusion choice has negligible impact on short texts.

Fusion strategies significantly improve long document performance.

Learnable fusion generalizes across positional encoding types.

Abstract

Transformers require positional encodings to represent sequence order, yet most prior work focuses on designing new positional encodings rather than examining how positional information is fused with token embeddings. In this paper, we study whether the fusion mechanism itself affects performance, particularly in long-sequence settings. We conduct a controlled empirical study comparing three canonical fusion strategies--element-wise addition, concatenation with projection, and scalar gated fusion--under identical Transformer architectures, data splits, and random seeds. Experiments on three text classification datasets spanning short (AG News), medium (IMDB), and long (ArXiv) sequences show that fusion choice has negligible impact on short texts but produces consistent gains on long documents. To verify that these gains are structural rather than stochastic, we perform paired-seed analysis and cross-dataset comparison across sequence-length regimes. Additional experiments on the ArXiv dataset indicate that the benefit of learnable fusion generalizes across multiple positional encoding families. Finally, we explore a lightweight convolutional gating mechanism that introduces local inductive bias at the fusion level, evaluated on long documents only. Our results indicate that positional-encoding fusion is a non-trivial design choice for long-sequence Transformers and should be treated as an explicit modeling decision rather than a fixed default.