Contextual Feedback Loops: Amplifying Deep Reasoning with Iterative Top-Down Feedback

TL;DR

This paper introduces Contextual Feedback Loops (CFLs), a lightweight iterative mechanism that enhances deep network reasoning by re-injecting top-down context into earlier layers, leading to improved performance across multiple tasks.

Contribution

The paper proposes CFLs, a novel feedback mechanism that unifies feed-forward and feedback inference, demonstrating stable convergence and performance gains with minimal overhead.

Findings

CFLs improve accuracy on CIFAR-10, ImageNet-1k, SpeechCommands, and GLUE SST-2.

CFLs converge stably under mild Lipschitz conditions.

Modest feedback significantly enhances deep model reasoning.

Abstract

Conventional deep networks rely on one-way backpropagation that overlooks reconciling high-level predictions with lower-level representations. We propose \emph{Contextual Feedback Loops} (CFLs), a lightweight mechanism that re-injects top-down context into earlier layers for iterative refinement. Concretely, CFLs map the network's prediction to a compact \emph{context vector}, which is fused back into each layer via gating adapters. Unrolled over multiple feedback steps, CFLs unify feed-forward and feedback-driven inference, letting top-level outputs continually refine lower-level features. Despite minimal overhead, CFLs yield consistent gains on tasks including CIFAR-10, ImageNet-1k, SpeechCommands, and GLUE SST-2. Moreover, by a Banach Fixed Point argument under mild Lipschitz conditions, these updates converge stably. Overall, CFLs show that even modest top-down feedback can substantially improve deep models, aligning with cognitive theories of iterative perception.