causality

The paper presents FCx, an algorithm for generating feasible counterfactual explanations by using a modified Variational Autoencoder with causal inference to ensure modifications are realistic, low-cost, and compatible with real-world changes.

Z. Ghahramani, VP of Research at Google DeepMind, criticizes current AI models for lacking rigorous mathematical foundations in probabilities, certainty, and cause-effect reasoning, describing them as a 'mishmash'.

The article discusses a research paper where LLMs used to write and review a trading feature missed a future-data bug, highlighting the need for structural redesigns in agent systems to prevent such issues.

Tweet highlights the 4th edition of the classic AI textbook 'Artificial Intelligence: A Modern Approach,' covering deep learning, transfer learning, multi-agent systems, robotics, and causality, with a link to Amazon.

The article explores whether in agentic systems the causal chain of the process is as valuable as the outcome, and questions if memory architectures should store causal threads instead of just raw outputs.

This paper presents a zero-shot time-series foundation model applied to prediction and causal analysis of functional MRI and synthetic signals.

This paper introduces logical physical clocks and methods for consistent snapshots in globally distributed databases, addressing causality and consistency challenges.

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.