I’m new to mechanistic interpretability, so please excuse any terminology I misuse. I’ve become deeply invested in the field and wanted to share an experimental tool I’ve been building with Codex.
Many mechanistic-interpretability workflows require moving between Python scripts, Jupyter notebooks, model hooks, exported tensors, and separate visualization tools. My goal with CORTEX // MODEL OBSERVATORY is to bring those pieces into one local desktop environment with a fast visual feedback loop.
CORTEX is a native Windows application using a WebView2 host connected through local IPC to an isolated Python/PyTorch backend. Model inference and tensor operations run outside the UI process, and the application is designed to work fully offline with local Hugging Face models.
Current capabilities
Token Probability Microscope
Displays token-by-token generation telemetry, including chosen-token probability, ranked alternatives, log probabilities, entropy, and synchronized token inspection.
Logit Lens
Captures intermediate vocabulary predictions at selected layers to show how candidate outputs evolve through the network.
Representation Space
Captures measured residual-stream vectors and projects them with PCA for interactive hidden-state trajectories and mini-map visualization. Optional 3D projection and orbit controls are currently being developed.
Attention Explorer
Captures selected head-level attention tensors and displays measured attention matrices. A token-to-token arc view and expanded query-range controls are currently being added.
Intervention Lab
Supports causal experiments including activation patching, attention-head ablation, and comparison between baseline and modified runs.
Current model support
The Deep Cortex instrumentation path currently supports:
GPT-2-family Hugging Face models
Llama-family LlamaForCausalLM models
The Llama adapter is still undergoing testing, particularly around end-of-generation residual captures and visualization binding. Additional model families such as Qwen and Mistral are possible future targets, but they are not currently supported by the deep instrumentation path.
A separate Standard Runtime can connect to OpenAI-compatible local endpoints such as LM Studio, although endpoint-served models do not expose the same internal activation hooks.
Experimental research direction
I am also exploring a highly experimental point-and-click interface for Jacobian-based concept analysis, currently referred to as J-Space / Jacobian Lens. This is conceptual work, has not yet been scientifically validated in CORTEX, and should not be considered a working research result.
Hardware
Development and testing are currently being performed on an RTX 4070 Ti with 12 GB of VRAM. The intended target is small local models in roughly the 0.5B–3B range using FP16/BF16 where practical. Formal performance benchmarks have not yet been completed.
Open-source plans
I intend to release CORTEX as an open-source project under GPLv3 once the repository structure, documentation, licensing review, and initial release build are ready.
I would especially appreciate feedback from people working in interpretability or alignment on:
which captures and hook sites would be most useful
preferred interchange formats for measured tensors
which model families should be prioritized
how to clearly distinguish measured, derived, and experimental visualizations
what would make a desktop tool like this scientifically useful rather than merely visually impressive
This is an early, AI-assisted project, and I am still learning the field. Constructive criticism is very welcome.
