Year: 2025
Type: preprint
Abstract: This paper introduces an enhanced RWKV architecture with adaptive temporal gating mechanisms for improved long-context language modeling. We propose two principal innovations: (1) a position-aware convolutional shift operator that captures local syntactic patterns while preserving global coherence, and (2) a neurally-gated information routing mechanism that dynamically regulates inter-token information flow. Through comprehensive experiments on text generation tasks, our enhanced model demonstrates superior performance compared to the baseline RWKV, achieving 96.5 relative improvement in ROUGE-L scores with only 2.95 increased inference latency. Ablation studies validate the individual contributions of each component, while linguistic analysis reveals the model's adaptive attention to syntactic boundaries and entity coherence. The proposed modifications maintain RWKV's linear computational complexity while significantly enhancing its contextual modeling capabilities, establishing new state-of-the-art performance for recurrent-style architectures in long-form text generation.
Source: arXiv (Cornell University)
Author: Pan, Xinghan
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4414834059
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