Controlled natural languages (CNLs) are subsets of natural languages that are constructed by restricting the grammar and vocabulary in order to reduce or eliminate ambiguity and complexity. Traditionally, controlled languages fall into two major types: those that improve readability for human readers (e.g. non-native speakers), and those that enable reliable automatic semantic analysis of the language.[1][2]
What Kind of Programming is Natural Language Programming?
In previous posts I’ve written about Natural Language Programming, Dijkstra’s Ghost – the End of The Symbolic Supremacy and Ephemeral Editable Specifications (aka Extract, Edit, A…
Simply Typed Reverse-Mode Automatic Differentiation with Variants: Denotational Correctness via Idempotent Completion
Reverse-mode automatic differentiation can be derived denotationally as a structure-preserving interpretation of program syntax. In the usual simply typed model, each source type has one cotangent type. Variants break this representation because the valid cotangent space depends on the branch selected at run time; established correctness results therefore use primal-indexed families of cotangent spaces, whose direct internal language is dependent.
A Rational Analysis of the Effects of Sycophantic AI
People increasingly use large language models (LLMs) to explore ideas, gather information, and make sense of the world. In these interactions, they encounter agents that are overly agreeable. We...
ICML Poster Learning syntax without semantics: Disentangled tiny language models
Language models acquire syntax and world knowledge together, entangling the two in ways that limit efficiency and controllability. We show that syntax can be learned while suppressing semantic plausibility and world‑knowledge cues, yielding more efficient and controllable models. We train tiny LMs on grammatical nonsense — syntactically well-formed text with semantic content ablated via constrained relexicalization (SAMBAL). Models trained on this data perform comparably to standard pretraining on syntactic benchmarks (BLiMP, SyntaxGym) while scoring at chance on world knowledge probes (EWoK). On targeted grammar-plausibility conflict probes, content-neutral models prefer grammaticality where standard models prefer plausibility, and their representations show more syntactic vs lexical alignment. On efficiency, disentanglement yields substantial sample and parameter gains: in low‑resource regimes, a 5M‑parameter model matches a 30M‑parameter baseline at the same data budget. On controllability, content-neutral models adapt rapidly to a new domain with minimal exposure, suggesting the feasibility of modular post‑hoc knowledge specialization.
Large language models are cultural technologies. What might that mean?
Four different perspectives
There are no lossless transformations of natural-language text
In my work at Clay I recently wrote an internal policy on acceptable use of AI writing by engineers, and I’m sharing it here. It’s my hope that one day better AI tools might be able to help us think, but until then I fear that using AI to write does the exact opposite.
agentlanguages.dev — Programming languages designed for AI agents to write
A community-edited catalogue of programming languages designed for AI agents to author code, organised around three philosophical camps: syntactic, verification, and orchestration.
Recursive Language Models: the paradigm of 2026
How we plan to manage extremely long contexts
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Slim Lim: "Concrete syntax matters, actually"
Syntax and semantics of dependent types
In this chapter we fix a particular syntax for a dependently typed calculus and define an abstract notion of model as well as a general interpretation function mapping syntactical objects to entities in a model. This interpretation function is shown to be sound with respect to the syntax.
The control layer for AI
The industry spent two years teaching LLMs to speak JSON. That work mattered: free-form text was unusable in production. Today, every serious inference provider uses some implementation (often open-source) of structured output. Structured output has become essential infrastructure that the team at .txt is proud to have spearheaded. Even as the ecosystem evolves, and open-source alternatives emerged, our engine remains the state of the art.