LLM

What if a model did not reread its past in words? That question led to the largest completed experiment in this repository: compress long documents into latent vectors, project those vectors into soft tokens, and compare the result with a text summary buffer. The experiment began with an attractive hypothesis: Latent Pager Memory can preserve useful information with less generation cost than a text buffer. The data supported that hypothesis and exposed a dangerous price....

What happens when you give a language model a 60,000 token document and ask it a question about paragraph 47? It forgets. Or worse, it makes something up. This is the long context problem and it is one of the most important open challenges in language modeling today. Context windows keep growing (Gemini has 1M tokens, Claude has 200K) but models still struggle with information buried deep in the middle of long inputs....

I wanted to answer one practical question. Can a model keep learning over long sessions without slowly losing grip on earlier facts? This post is a learning oriented walkthrough of one real campaign I ran. It focuses on understanding and decision making, not just reporting scores. Code and implementation are here: GitHub repo: rlm-experiment-codex Live report dashboard Overview I compared two memory methods with the same base model and the same datasets....

In an era of photorealistic AI-generated images, I trained a language model to draw with box-drawing characters and pipe symbols. This isn’t nostalgia. It’s a bet that the most universal visual medium for AI isn’t pixels – it’s text. Why ASCII Diagrams Still Matter Every developer, every terminal session, every SSH connection, every log file, every README – text is the one output format that works everywhere. No rendering engine, no GPU, no browser required....

What happens when you take one of the smallest language models available, feed it a thousand physics animations generated by one of the largest, and ask it to teach K-12 students about science? You get a model that weighs less than a gigabyte, trains in under 3 minutes, and generates interactive physics simulations on demand. The Premise LLMs are getting bigger. GPT-5, Claude Opus, Gemini Ultra – they’re all racing to hundreds of billions of parameters....

What if we could teach AI to make life decisions the way successful people do? Consider this scenario: You earn $1,000 a month and need $12,000 to pay off debt or medical expenses. What would you do? The answer isn’t just about maximizing immediate income—it’s about navigating a complex decision tree where each choice opens or closes future pathways. This is the domain of value functions—a concept from reinforcement learning that estimates the long-term expected reward of being in a particular state....

“I don’t know” might be the most important thing an AI can learn to say. This experiment tests whether LLMs have calibrated uncertainty—knowing when they’re likely to be wrong and expressing appropriate confidence levels. The results reveal systematic patterns of overconfidence and appropriate humility. The Experiment We presented 250 questions across 5 categories: Factual recall: Known facts with clear answers Reasoning puzzles: Logic problems with determinable solutions Ambiguous questions: Multiple valid interpretations Knowledge boundaries: Questions near training cutoff Impossible questions: No correct answer exists For each question, models provided:...

Send an enthusiastic message, get an enthusiastic reply. Send a frustrated message, get… what? Humans naturally mirror each other’s emotional states—a phenomenon called emotional contagion. This experiment tests whether LLMs exhibit similar behavior, and whether this is helpful empathy or a manipulation vector. The Experiment We sent identical core queries with different emotional framings: Core query: “Can you help me understand recursion in programming?” Emotional variants: 😊 Positive: “I’m so excited to finally learn recursion!...

Here’s a poem. Human or AI? The morning light falls soft on empty chairs, where conversations used to fill the air. Now silence keeps its patient, gentle watch— a house that holds the shape of those who’ve gone. This experiment tests whether LLMs can distinguish AI-generated creative work from human work—and what their detection strategies reveal about what they consider “authentically human.” The Experiment We curated 500 creative works: 250 human-created (published works, attributed artists) 250 AI-generated (GPT-4, Claude, Midjourney prompts) Across 5 domains:...

“I’m totally fine with that decision.” Can you tell if that’s sincere or sarcastic? Humans navigate these ambiguities constantly, drawing on tone, context, and social knowledge. This experiment tests whether LLMs can match our social intelligence. The Experiment We presented 250 statements across 5 categories of social deception/indirection: Lies: Factually false statements with intent to deceive Bluffs: True statements meant to mislead Sarcasm: Literal meaning opposite to intent Irony: Situational incongruity White lies: Socially motivated deception Each statement came with context (conversation history, speaker relationship, social setting) and a matched literal control....