Muawiz Chaudhary @GuideLabsAI

Introducing the Causal Diffusion Engine (CDE): the inference engine behind Steerling-8B. CDE serves an interpretable diffusion LLM with concept explanations and steering alongside every generated token, with no added latency. Interpretability doesn’t have to slow down inference.

The first inherently interpretable AI platform is finally here. Welcome to Clarity.

Meet the minds behind Steerling-8B, the world's first interpretable LLM. At Guide Labs, we’re building models you can reliably understand and control, not just use.

At @guidelabsai we’ve proven we don’t need to sacrifice intelligence to gain transparency. Standard AI systems have representations that are, by default, entangled but Steerling-8B takes a different path. It learns disentangled representations by construction through architectural and training-time constraints. Steerling-8B has already learned thousands of novel concepts that it was never explicitly constrained to represent, spanning domains from linguistics and programming to geography, culture, and abstract reasoning. Consequently we shift the question from: “Can we reverse-engineer what this model knows?” to: “What did this model learn?” Here we show that we can easily discover thousands of novel concepts from the model; concepts it was never explicitly trained to learn. The model discovered: - British English - spelling as a coherent system - unified “you” across six languages - separated spelled-out numbers from digit - typographic errors - a dedicated concept for broken Unicode These first ~100K concepts Steerling-8b discovered are evidence of what becomes possible when interpretability is a design choice rather than a retrofit. We’re unlocking an entirely new category of AI. Test the model LIVE: Guide Labs: guidelabs.ai/post/concept-di… Gitbub: github.com/guidelabs/steerli… Hugging Face: huggingface.co/guidelabs/ste… #Steerling8b #GuideLabs #AI #MachineLearning

Current methods for controlling model behavior modify behavior globally and often unpredictably. What if you could directly amplify or suppress specific concepts in the model's reasoning, with predictable effects on the output? 📣 Enter Steerling-8B, the world’s first large-scale inherently interpretable LLM Its concept bottleneck architecture enables users to perform ‘algebra’ in the concept space to control the model’s output. For the first time, you can add, remove, and compose human-understandable concepts at inference time to directly control what the model generates, without retraining or prompt engineering. This means we can not only explain what the model is doing, but we can actually control it natively, by directly modifying concept activations at inference time. Steerling-8B enables: ⚙️ Concept injection — steering a generic prompt toward any target domain ↩️ Concept suppression — unlearning a concept the model would otherwise express 🔗 Multi-concept composition — combining and opposing multiple concepts simultaneously Learn more: guidelabs.ai/post/steerling-… Test and play with Steerling-8B: GitHub: github.com/guidelabs/steerli… Hugging Face: huggingface.co/guidelabs/ste… #Steerling8B #GuideLabs #AI #MachineLearning

It’s official: the first large-scale inherently interpretable language model is here. Steerling-8B from @guidelabsai is the first and largest model that can trace every token it generates back to: →  Input Context → Training data → Human-understandable concepts In other words, we've successfully trained Steerling-8B to trace its outputs and explain what has impacted that decision for more reliable manipulation. This isn’t post-hoc explainability. Interpretability is built directly into the model. 🔓Steerling-8B can self-monitor for memorized content and suppress it at inference time without retraining. That makes interpretability a first-class design principle, not an afterthought. This is a major step toward models we can actually understand, debug, and trust. Over the coming days, we’ll be sharing investigations into what Steerling-8B’s interpretability enables in practice. Stay tuned as we dive deeper into our research & how we are building LLMs we can trust. 🚨 Try it LIVE and help improve it: Guide Labs: guidelabs.ai/post/steerling-…  GitHub: github.com/guidelabs/steerli… Hugging Face: huggingface.co/guidelabs/ste… Huge thank you to @TimFernholz and @TechCrunch for featuring this breakthrough. techcrunch.com/2026/02/23/gu… #Steerling8B #GuideLabs #AI #MachineLearning

Today we're releasing Causal Diffusion Language Models: a new architecture that combines AR-style scaling with block-level generation. We're building interpretable language models at @GuideLabsAI. That requires controlling concepts that span multiple tokens, which led us to rethink diffusion architectures. ❌ Autoregressive: generates one token at a time ❌ Diffusion: harder to scale effectively ✅ Solution: Causal Diffusion Models 🧵

Excited to announce our $9M USD seed round led by @Initialized and the first large-scale interpretable LLM: an 8 billion parameter model capable of explaining its outputs through mechanisms humans can actually understand.