Stop-and-Go Distractions: Measuring the Effects of Fragmented Inputs on Recurrent Networks

The core research question investigated how task-irrelevant interruptions, insertions, or replacements (fragmented inputs) affect frozen recurrent predictors across domains like synthetic prediction, image classification, and human-activity recognition, while examining how factors such as interruption history, placement, dose, and donor class identity influence model performance. The key findings and data results show that intermittent noise or irrelevant insertions cause a measurable, history-dependent shift in the internal computational states of recurrent networks rather than isolated, single-step errors. Performance and representations do not bounce back instantaneously after a distraction; instead, recovery occurs gradually over subsequent valid inputs. Furthermore, the temporal spacing, frequency, and placement of distractions critically dictate the severity of performance decay, proving that how inputs are broken up matters as much as the amount of noise introduced. Varying the replacement dose and utilizing donor inputs from either the same or different classes also demonstrated distinct interference patterns across synthetic, image-based, and human-activity recognition benchmarks. Based on these findings, we can claim that: Vulnerability to Intermittent Fragmentation: Recurrent architectures exhibit quantifiable vulnerabilities where specific interruption frequencies lead to predictable, structured performance degradation. Non-Instantaneous State Recovery: Recurrent networks retain the "memory" of disruptions over a short temporal window, demonstrating that hidden states require a sequence of clean inputs to flush out distraction-induced artifacts. Generalizability Across Domains: The impact of fragmented inputs is a systemic feature of recurrent processing, consistently appearing across diverse domains like synthetic tracking, image tasks, and temporal sensor streams. Future research questions include exploring mitigation strategies by asking how we can design training techniques or architectural modifications (such as attention gating or robust memory buffers) to make recurrent networks immune to stop-and-go distractions. We also aim to identify the exact mathematical thresholds that govern when a sequence of fragmentations causes complete catastrophic forgetting versus minor state perturbation, and how alternative temporal models like State Space Models (SSMs) or Transformers compare to traditional recurrent networks when exposed to the exact same temporal fragmentation protocols. Note: This experiment and research were conducted with the assistance of LLMs (such as ChatGPT, Gemini, Grok, and Claude). The author maintains full transparency by providing complete documentation, including chat links and the associated Google Colab notebook.