With Cantrip, deepfates reimagines the fundamentals of language model agents. Available as a ghost library with generative test specification.
Cantrip: On summoning entities from language in circles | deepfates
With Cantrip, deepfates reimagines the fundamentals of language model agents. Available as a ghost library with generative test specification.
Language Machines
How generative AI systems capture a core function of language Looking at the emergence of generative AI, Language Machines presents a new theory of meaning i...
Tokenization: A Survey for Modern NLP
While modern language models take raw text as their input and produce raw text as output, they do not operate over text directly. Hidden in the very first step of language model pipelines is...
elvis on Twitter / X
Small Language Models are the Future of Agentic AILots to gain from building agentic systems with small language models.Capabilities are increasing rapidly!AI devs should be exploring SLMs.Here are my notes: pic.twitter.com/7dhmz9V2jB— elvis (@omarsar0) July 1, 2025
Understanding Understanding: A Pragmatic Framework Motivated by...
Motivated by the rapid ascent of Large Language Models (LLMs) and debates about the extent to which they possess human-level qualities, we propose a framework for testing whether any agent (be it...
Generative Agents: Interactive Simulacra of Human Behavior
Believable proxies of human behavior can empower interactive applications ranging from immersive environments to rehearsal spaces for interpersonal communication to prototyping tools. In this paper, we introduce generative agents--computational software agents that simulate believable human behavior. Generative agents wake up, cook breakfast, and head to work; artists paint, while authors write; they form opinions, notice each other, and initiate conversations; they remember and reflect on days past as they plan the next day. To enable generative agents, we describe an architecture that extends a large language model to store a complete record of the agent's experiences using natural language, synthesize those memories over time into higher-level reflections, and retrieve them dynamically to plan behavior. We instantiate generative agents to populate an interactive sandbox environment inspired by The Sims, where end users can interact with a small town of twenty five agents using natural language. In an evaluation, these generative agents produce believable individual and emergent social behaviors: for example, starting with only a single user-specified notion that one agent wants to throw a Valentine's Day party, the agents autonomously spread invitations to the party over the next two days, make new acquaintances, ask each other out on dates to the party, and coordinate to show up for the party together at the right time. We demonstrate through ablation that the components of our agent architecture--observation, planning, and reflection--each contribute critically to the believability of agent behavior. By fusing large language models with computational, interactive agents, this work introduces architectural and interaction patterns for enabling believable simulations of human behavior.
Forcing Generative Models to Degenerate Ones: The Power of Data...
Growing applications of large language models (LLMs) trained by a third party raise serious concerns on the security vulnerability of LLMs.It has been demonstrated that malicious actors can...
Andrej Karpathy on Twitter / X
We'll be spending a lot more time trying to understand the outputs of language models. A few thoughts, tips & tricks:Writing. Something I've had success with: Ask your LLM to explain something in ASD-STE100, it's a controlled language specification originally developed for… pic.twitter.com/uMFWTcBCsl— Andrej Karpathy (@karpathy) October 2, 2026
Recursive Language Models
We propose Recursive Language Models (RLMs), an inference strategy where language models can decompose and recursively interact with input context of unbounded length through REPL environments.
Caveman | Lithic Token Compression
A semantic constraint engine for Claude Code & Codex. Forces agentic communication into minimal-token lithic structures. Retain 100% technical accuracy while destroying up to 87% of output latency.
blowdart/idunno.AtProto.Lexicons
A collection of lexicon implementations for AT Protocol for use with idunno.AtProto
First Languages AI Reality | Mila
The FLAIR initiative aims to develop a method for the rapid creation of customized ASR models for Indigenous languages.
51 Ways to Spell the Image Giraffe: The Hidden Politics of Token Languages in Generative AI
Generative AI models don't operate on human languages – they speak in **tokens**. Tokens are computational fragments that deconstruct lan...
Circuit Tracing: Revealing Computational Graphs in Language Models
We describe an approach to tracing the “step-by-step” computation involved when a model responds to a single prompt.
