EchoLSTM: A Self-Reflective Recurrent Network for Stabilizing Long-Range Memory
This addresses a key bottleneck in sequence modeling for applications like natural language processing, though it appears incremental as it builds on existing LSTM and attention mechanisms.
The paper tackles the problem of long-range dependency modeling in recurrent neural networks, particularly in noisy sequences, by proposing EchoLSTM with Output-Conditioned Gating, achieving 69.0% accuracy on a Distractor Signal Task (33 percentage points over LSTM) and competitive performance on ListOps with 5 times more parameter efficiency.
Standard Recurrent Neural Networks, including LSTMs, struggle to model long-range dependencies, particularly in sequences containing noisy or misleading information. We propose a new architectural principle, Output-Conditioned Gating, which enables a model to perform self-reflection by modulating its internal memory gates based on its own past inferences. This creates a stabilizing feedback loop that enhances memory retention. Our final model, the EchoLSTM, integrates this principle with an attention mechanism. We evaluate the EchoLSTM on a series of challenging benchmarks. On a custom-designed Distractor Signal Task, the EchoLSTM achieves 69.0% accuracy, decisively outperforming a standard LSTM baseline by 33 percentage points. Furthermore, on the standard ListOps benchmark, the EchoLSTM achieves performance competitive with a modern Transformer model, 69.8% vs. 71.8%, while being over 5 times more parameter-efficient. A final Trigger Sensitivity Test provides qualitative evidence that our model's self-reflective mechanism leads to a fundamentally more robust memory system.