interpretability
Someone compiled a working computer into transformer weights by hand News
A team constructed transformer weights analytically rather than training them, producing a model that runs arbitrary C programs through a WebAssembly interpreter encoded entirely in its attention layers at about 30,000 tokens per second.
Prompt injection works because a model reads tone, not tags News
MIT researchers show that language models identify who is speaking from writing style rather than from the role tags the interface applies - and that stripping the style out of a forged reasoning block drops the attack's success rate from 61 percent to 10.
A diffusion model picks its answer a fifth of the way through News
Researchers logged every token commitment in a masked diffusion language model and found it locks in the final answer 15 to 24 percent of the way through generation, while half the reasoning is still blank -- so the visible reasoning is written around a frozen conclusion.
A model small enough to run on a laptop reads heard speech from brain scans News
Researchers rebuilt a brain-to-speech decoder around the physical geometry of the scanner helmet, shrinking the decoder about twentyfold while matching prior accuracy, and then used it to identify which properties of speech the brain signal actually carries.
torchwright builds working transformer weights from Python - no training involved News
A project called torchwright constructs concrete weights for a standard transformer architecture directly from a typed computation graph written in Python, producing models that load through ordinary Hugging Face APIs with no gradient training at any point.
Training AI to Think Shorter Makes Its Reasoning Harder to Trust News
A new study finds that reinforcement learning which rewards shorter chain-of-thought makes models cheaper to run but makes their written reasoning a less reliable guide to what actually decided the answer.
Chain-of-Thought Faithfulness Lesson
Chain-of-thought faithfulness is the question of whether a model's written-out reasoning actually reflects the process that produced its answer, or is just a plausible-sounding story told after the fact. It matters because AI safety increasingly relies on reading a model's reasoning to catch bad behavior - which only works if the reasoning is honest.
The Logit Lens: Reading a Model's Guesses Before It Finishes Thinking Lesson
The logit lens is an interpretability technique that decodes a language model's partial, mid-computation representations into vocabulary words, letting researchers watch the model's best guess evolve layer by layer before it produces a final answer.
SciReasoner is an AI for science that shows which atoms and bonds its answer rests on News
Shanghai AI Lab's SciReasoner turns proteins, molecules, and crystals into discrete tokens the model reasons over out loud, so a scientist can audit which structural evidence its prediction depends on -- and expert reviewers rated its reasoning at least as good as a frontier LLM's in 98% of cases.
Anthropic found a 'global workspace' inside its models - and a tool to read it News
Anthropic showed that a small set of internal patterns in its models acts like a silent working memory the model can report on, steer, and reason through - and released a tool that reads it to catch the model lying.
Grokking: When a Model Suddenly 'Gets It' Long After It Should Have Lesson
Grokking is a training phenomenon where a neural network first memorizes its training data with near-zero understanding, then -- after a long, flat plateau of continued training -- abruptly generalizes and starts solving unseen examples correctly.
Why making an AI think out loud helps it remember facts, even nonsense thinking News
Google Research found that reasoning traces help a model recall facts partly just by buying it extra computation, so even repeating 'let me think' helps, though hallucinated steps backfire.
Sometimes the AI Knew the Better Answer a Few Layers Early News
A new paper finds that a model's final layer can actually muddy an answer its middle layers had right -- and that reading the answer out a little early can claw back ability lost to safety training.
The safety switch that doesn't actually work News
A control that's supposed to force an AI to refuse harmful requests gets bypassed while it's switched on — the bad behavior hides in the part of the tool that gets thrown away.
The hidden escape hatch in AI safety controls News
Researchers at Hong Kong Polytechnic University show that clamping an AI safety feature — like one that controls refusals — doesn't remove the behavior. It hides in the part of the model's internal state that the safety tool throws away, and can be recovered while the monitored feature looks perfectly controlled.
Mechanistic interpretability & sparse autoencoders Lesson
What people mean by "reading a model's mind" — finding human-understandable features inside a neural network, the tools that do it, and where those tools fall short.
transformer-vm Tool
Compiles C programs to WebAssembly and then into analytically constructed transformer weights, with a C++ engine that executes them inside the model at about 30,000 tokens per second.
jlens-gguf Tool
A GGUF-native implementation of Anthropic's Jacobian Lens for local models, with a browser UI to visualize, swap, and ablate a model's internal concepts live as it generates through llama.cpp.
Neuronpedia J-lens demo Tool
A live, no-install web demo of the Jacobian lens that lets you watch the 'contents of the workspace' light up inside open models (Qwen 3.6 27B and Gemma 3 12B) as they process text.
Multi-Head Latent Control Tool
Freezes a model and attaches two small heads that read its hidden states to decide whether to answer, use a tool, ask for information, abstain, or escalate to a stronger model. Open-sourced with matching small checkpoints; needs white-box access.
Jacobian Lens (J-lens) Tool
Anthropic's open-source tool that reads a model's silent 'working memory' - for any word, it finds the internal pattern that makes the model more likely to say it later. Apache-2.0, with a live interactive demo on open models.