mlsanityData to 13 Oct 2026

Probabilistic Foundations for Metacognition via Hybrid-AI

arXiv 2502.05398 ↗First posted 8 Feb 2025cs.AIcited by 1

Not on the map. Why?

Abstract (arXiv)

Metacognition is the concept of reasoning about an agent's own internal processes, and it has recently received renewed attention with respect to artificial intelligence (AI) and, more specifically, machine learning systems. This paper reviews a hybrid-AI approach known as "error detecting and correcting rules" (EDCR) that allows for the learning of rules to correct perceptual (e.g., neural) models. Additionally, we introduce a probabilistic framework that adds rigor to prior empirical studies, and we use this framework to prove results on necessary and sufficient conditions for metacognitive improvement, as well as limits to the approach. A set of future

Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 6 of its 12 references that are in the map. First posted on arXiv 8 Feb 2025.

Builds on

6 of 12 references.

References · 6

  1. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,498
  2. 2018A Survey on Artificial Intelligence Trends in Spacecraft Guidance Dynamics and Controlcited by 13
  3. 2024Metacognitive AI: Framework and the Case for a Neurosymbolic Approachcited by 9
  4. 2023Rule-Based Error Detection and Correction to Operationalize Movement Trajectory Classificationcited by 8
  5. 2024Error Detection and Constraint Recovery in Hierarchical Multi-Label Classification without Prior Knowledgecited by 7
  6. 2024Metal Price Spike Prediction via a Neurosymbolic Ensemble Approachcited by 3

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2026: 1.

1 paper.