Sequence models recall content with adaptive time delay system
Time Delay System for Sequence Modeling
Computation and Language
Summary
Many current computer models that handle sequences struggle to remember specific important past events clearly. The authors introduce a new method that dynamically figures out which past information is most relevant based on meaning similarity, allowing the model to focus its memory like a thoughtful human might. This method is efficient to run, interpretable, and helps balance when to use immediate information versus past context. Tests on text tasks show it works better than some existing techniques.
What this means in practice
- •For natural language processing engineers: Improve text classification and generation models by incorporating content-sensitive temporal recall mechanisms for better contextual understanding.
- •For speech recognition developers: Enhance speech-to-text systems by dynamically selecting relevant historical audio features based on semantic content for improved transcription accuracy.
Authors
Youshan Zhang, Jialu Li
Abstract
Existing sequence models share a critical limitation: they lack an explicit, interpretable mechanism for content-aware temporal recall. LSTMs compress everything into a single hidden state, thereby losing information about specific past events. Transformers attend to all tokens indiscriminately at quadratic cost, while state-space models like Mamba compress history into a fixed-dimensional state without explicit content-addressable memory. To address this gap, we introduce a novel framework grounded in nonlinear time-delay systems, in which the effective delay is dynamically estimated based on the semantic similarity between current and historical inputs. Our approach features three key innovations: (i) a differentiable semantic lag estimator that computes content-aware soft attention weights over history, producing a weighted historical context and similarity score; (ii) a diagonal state space with closed-form cumulative sum solution, achieving O(1) per-step complexity with no sequential loops; and (iii) an interpretable fuzzy semantic modulation mechanism that learns context-dependent rules for balancing instantaneous processing versus historical recall, where rule centers correspond to prototypical semantic contexts. Experimental results on text classification and generation outperform state-of-the-art methods.