Why Misinformation Must Not Be Ignored

• Recent academic debate has seen the emergence of the claim that misinformation is not a significant societal problem. We argue that the arguments used to support this minimizing position are flawed, particularly if interpreted (e.g., by policymakers or the public) as suggesting that misinformation can be safely ignored. Here, we rebut the two main claims, namely that misinformation is not of substantive concern (a) due to its low incidence and (b) because it has no causal influence on notable political or behavioral outcomes. Through a critical review of the current literature, we demonstrate that (a) the prevalence of misinformation is nonnegligible if reasonably inclusive definitions are applied and that (b) misinformation has causal impacts on important beliefs and behaviors. Both scholars and policymakers should therefore continue to take misinformation seriously.

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

• Deep neural networks typically rely on a single forward pass for inference, which can limit their capacity to resolve ambiguous inputs. We introduce Contextual Backpropagation Loops (CBLs) as an iterative mechanism that incorporates top-down feedback to refine intermediate representations, thereby improving accuracy and robustness. This repeated process mirrors how humans continuously re-interpret sensory information in daily life-by checking and re-checking our perceptions using contextual cues. Our results suggest that CBLs can offer a straightforward yet powerful way to incorporate such contextual reasoning in modern deep learning architectures.

GPT Agents

• Put the images in the paper and added a paragraph of description for each.