Time Delay System for Sequence Modeling

The paper introduces a new sequence modeling framework based on nonlinear time-delay systems that dynamically estimates effective delay using semantic similarity between current and past inputs. It proposes a differentiable semantic lag estimator for content-aware soft attention, a diagonal state space with O(1) per-step complexity, and an interpretable fuzzy semantic modulation mechanism that balances immediate processing with historical recall. Experiments on text classification and generation show that this approach outperforms state‑of‑the‑art methods.

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• llms
• nlp
• benchmarks