causality

This article offers an in-depth analysis of the Causal World Model (CWM) proposed by Aether AI (原识之智), arguing that the next AI paradigm will shift from correlation to causation. It discusses the theoretical foundations, technical architecture, and potential impact on video generation and embodied intelligence.

Introduces Temporal Difference in Vision (TDV), a new paradigm for representation learning that relies solely on causality, eliminating the need for augmentations, masking, or cropping, and matches state-of-the-art methods like DINO and iBOT on dense spatial tasks.

A reflection on the broad implications of transformer architectures beyond LLMs, including potential impacts on linguistics, genetics, and causal modeling, comparing their significance to the Haber-Bosch process.

The paper identifies a failure mode where predictors collapse to a point on unidentified counterfactual couplings and proposes a framework using a positive semidefinite coupling kernel to bound counterfactuals, showing that prediction cannot represent uncertainty over cross-world couplings and that enforcing kernel constraints yields tractable bounds.

This paper introduces YoCausal, a benchmark based on the Violation of Expectation paradigm from cognitive science, to evaluate whether video diffusion models truly understand causality or merely overfit to temporal patterns. Evaluation of 13 state-of-the-art models reveals a significant gap compared to human-level causal cognition.

The article explores the concept of quantum jamming, a process that could break quantum cryptographic protocols, and discusses efforts to understand causality at a deeper level to ensure security even beyond quantum mechanics.

BrainCause framework uses generative and brain models to identify causal neural representations in the human brain, demonstrating that activation alone is insufficient for confirming concept representation.

A technical deep-dive into common causes of failed pretraining runs in large language models, including causality-breaking issues in expert routing and numerical precision bugs, with examples from Llama 4, Gemini 2 Pro, and GPT-4.

Judea Pearl argues that there are mathematical limitations to learning solely from data, citing the inability to infer causation from correlation. The article prompts discussion on whether pure data-driven learning is sufficient.

The article explores the concept of illusions of understanding in scientific practice, discussing how ambiguous language, incomplete causal accounts, and satisfying but incomplete explanations can lead scientists to overlook deeper understanding.

This academic paper establishes connections between Consistency-Based Diagnosis and Actual Causality within the context of Explainable AI (XAI). It aims to integrate these two areas to improve explanations in AI and Explainable Data Management.