Tag
This paper proposes an emotion analysis interface using Natural Semantic Metalanguage (NSM) to generate faithful, interpretable explanations for emotion classifications, trading slight accuracy for verifiability.
This paper introduces a benchmark of ten complex systems for validating causal abstraction metrics, evaluates over thirty candidate metrics, and proposes the Causal Abstraction Error (CAE) as a general-purpose validity metric that reliably discriminates valid from invalid explanations.
NEO is a new type of world model that learns to discover reusable building blocks of explanation from raw observations without supervision or language, selected as an ICML 2026 oral presentation.
A tweet promotes Stanford's free CS324 course on large language models, which uses a simple example of a mouse eating cheese to explain how LLMs work, and includes interactive demos.
An explanation of why diffusion models work well for images: low-frequency spectral components dominate, so denoising recovers coarse structure first, then fine detail — analogous to spectral autoregression.
This paper proposes a definition of good explanations based on counterfactuals and prior beliefs, and discusses the inherent difficulties in explaining LLM outputs under this definition.
This article explains the key difference between computer-use agents that operate on full desktop interfaces using pixel screenshots and browser-use agents that can leverage the DOM's hidden structure, making the former a harder technical problem.
This paper applies philosophy of science to argue that LLMs offer epistemic value as minimal models for how-possibly explanations in linguistics, but do not yet qualify as how-actually explanations of human language.
A short explanation of Deep Agents by Sydney Runkle, presented by LangChain.
Martin, an expert from IBM Technology channel, explains in a simple and profound way seven key concepts in the AI world through the video: Agents, large reasoning models, vector databases, RAG, MCP protocol, MoE architecture, and artificial superintelligence. It is considered the most information-dense AI concept explainer of the year.
A tweet thread listing 25 commonly used but often misunderstood AI concepts, such as tokens, embeddings, RAG, agents, and LoRA.
A tweet shares a link to a clear, accessible explanation of why LLM inference differs from traditional inference, presented in a casual walking video.
A thread explaining the mathematical foundations behind key transformer concepts including attention, scaling factor, backpropagation, gradient descent, cross-entropy loss, RoPE, and RMSNorm.
A tweet recommends a clear explanation of transformers, urging readers to read it twice.
The author describes a talk given at a university about the memory limitations of AI agents, using Christopher Nolan's film Memento as an analogy to explain why agents struggle with memory.
This article explains the architecture of DALL-E, focusing on its transformer component that correlates language with discrete image representations to generate high-quality images from text prompts.