axiAuto RL self-learning agent

Environments, libraries and frameworks for applied ML and foundation models.

We're an AI research lab for applied ML and foundation models. We reverse engineer, interpret and build environments, libraries and frameworks. Our dream is to accelerate ML development with precision and intent for all, enabling safer and better ML across physics, life sciences, industrial, frontier and critical verticals.

Backed by
awesome programs.

Emergent Ventures
Founders Inc
The Residency
NVIDIA Inception
Emergent Ventures
Founders Inc
The Residency
NVIDIA Inception

We work with frontier labs, research groups, universities, and companies training their own models, from classical ML to foundation models and everything in between.

Introducing axi

We're heading towards a future where foundation models play a significant role across physics, life sciences, industrial, and frontier verticals, and it's critical we find ways to develop and train them better and safer. We're building axi, an auto RL self-learning agent for controlling developer environments like aq at full efficiency. aq is the developer environment for foundation models built with frameworks, libraries, tools and autonomous agents helping design, test, and refine models. Built for precision and intent, handling architecture, experiments, and building AI the way it should be built, deliberately, not by guessing.

curl -fsSL https://aquin.app/axi/download/install.sh | bash

transformers

lora

LAION

Building an interpretable VLM that maps text and vision towers on top of OpenCLIP.

KAIST

Using interpretability tooling for preference detection analysis in LLMs.

AEIA Lab

Training SAEs on a LLM to sense conflict, ambiguity, and tone, and choose the right response.

Zyogen

Building interpretable medical vision transformers and diffusion models.

Labs we worked with

Attention as Routing Graph: Circuit Extraction from Single Forward Pass

Treat attention as a routing map from one forward pass, keep a small subgraph aimed at the answer, and check it under ablation.

Finding Usable Weight Mechanisms with Tiled SVD

Extract mechanism mounts from weight sites as trigger, write, and strength triples. Pre-registered A/B/C eval on Gemma-2-2B: 182/182 GO.

aq

Developer environment for foundation models: frameworks, libraries, tools, and agents to design, test, and refine models with precision and intent.

World Models

Talking-avatar LoRA on MiniMax-H3, an action-conditioned JEPA for Mario with probe gates, and a 21MB pixel-art world model.

Interpretability

Applied research on AQIT: transformers and LLMs, attribution, sparse autoencoders, simulation, live training watch, embedding models, evals, security, and benchmarks.

Experimental Weight Editor

Agentic ROME on Pythia 2.8B: causal trace layer location, rank-one MLP updates, and a three-check validation loop that rolls back and retries on failure.

Simulating LLM Behaviour in Different Environments

Four environments for studying LLM behaviour: activation steering for bias reduction, Chess Agents Simulation, Echoes gossip RPG, and Among Us Agents Simulation.

Latest research

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