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.
Research Sep 2026
Treat attention as a routing map from one forward pass, keep a small subgraph aimed at the answer, and check it under ablation. On induction and IOI across GPT-2 Small/Medium and Pythia-410M, extracted edges hurt far more than random sets of the same size.
Research Aug 2026
We extract mechanism mounts directly from weight sites by column-tiled SVD, each mount a triple as trigger, write, and strength. Pre-registered A/B/C eval on Gemma-2-2B (WikiText-2): 182/182 GO.
Lab Now
Live, concept-level control of training runs: watch features form mid-run, then steer with data, loss, gradient, and representation interventions. First milestone on fine-tuning with a spurious shortcut.
Lab Now
Cross-study replication of neurodegeneration snRNA-seq findings. One disease, every public dataset, the same pipeline throughout, then a CSF and plasma biomarker shortlist from what holds. Target December 2026.
Research Summer 2026
Activation steering for bias, Chess Agents Simulation, Echoes gossip RPG, and Among Us Agents Simulation.
Partners Now
With Zyogen we are building interpretable medical vision transformers and diffusion models, including training a ViT on wound recovery imaging and interpreting it to distill a prediction system you can still inspect.
Lab 2026
We have raised about $100K in compute and funds through NVIDIA Inception, Emergent Ventures at GMU, Founders Inc, and The Residency.
Partners Now
Building an interpretable VLM that maps text and vision towers on top of OpenCLIP.
Partners Spring 2026
Using interpretability tooling for preference detection analysis in LLMs.
Partners Spring 2026
Training SAEs on a language model to sense conflict, ambiguity, and tone, and choose the right response.
Product 2025
Open-source CLI and Python SDK for the gap a loss curve does not explain. Inspect, simulate, watch, diff, steer, probe, and eval.
Research Summer 2026
Agentic ROME with causal tracing and a validation loop that rolls back and retries on failure. Case studies on factuality, bias correction, and censor auditing.
Research Summer 2026
How Reddit, X, and Hacker News discussions around Meta Ray-Ban glasses were turned into a structured JSONL training dataset through a multi-stage pipeline.
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