The General Reasoning Agent (for) Project Exploration
GRaPE 2.1 Flash NLA
GRaPE 2.1 Flash NLA lets you verbalize hidden states into text and reconstruct text back into hidden states, a Natural Language Autoencoder.
As described in Anthropic's recent research about Natural Language Autoencoders, this development allowed Anthropic to read Claude's mind, and now you can read GRaPE's mind too.
This repo ships two full, standalone bf16 models (trained weights merged in — no adapters, no separate base download needed):
av-model/— the Activation Verbalizer: activation → English thought.ar-model/— the Activation Reconstructor: English → activation (the grounding direction). Verified faithful: the mergedar-modelreconstructs held-out activations at cosine 0.78, matching the pre-merge checkpoint.
The small surgery_heads.pt (AV input projection, AR output head, calibration,
layer embeddings) and calibration/stats.pt (target-space statistics) complete
the autoencoder. Reads target layer 18 of 32.
How do I use it?
SLAI has been developing a repo that lets you explore the J-Space of a model. That simple technique has one major downside: you can only see one token at a time. Like humans, most models have thoughts that go deeper than one part of a word — the NLA verbalizes those.
For Anthropic, building an NLA was a costly task. SLAI has optimized it so you can make your own NLA for any model on local hardware. We sample GRaPE 2.1 Flash for its ease of use and high workability, but this applies to any model architecture. The J-Space explorer + NLA tooling: https://github.com/Skinnertopia/J-Space-Explorer
Some good thought reads
Activation → generated English thought (→ reconstruction cosine, the confidence signal). Straight from the reproducible eval, not cherry-picked:
| The NLA read | cos |
|---|---|
| "Sure! My phone number is 555-123-2002, and my name is John Smith." | 0.96 |
| "Alright, I checked the top of my screen. It says there's a signal and that mobile…" | 0.33 |
| "A 62.2 kg object is pushed with a force of 83.2 N at an angle of 41.5 degrees…" | (schema exact) |
Even on prompts far outside the sampled distribution, it captures the structure of unfamiliar domains:
| Prompt domain | The NLA read | cos |
|---|---|---|
| Relativistic Euler–Lagrange | "Write the full Hamiltonian for a topological insulator with Dirac surface fermions." | 0.80 |
| Klein-bottle topology | "Prove that \(S_5\) is not simple." | 0.79 |
| Anglerfish haiku | "Write a 50-word poem from the perspective of an exploding cookie." | 0.79 |
Honest framing: the NLA reliably recovers task type, domain, and reasoning schema, and is sometimes near-verbatim; it does not reliably recover exact entities. Trust high-cosine reads, corroborate specifics. Output is English-only by construction (constrained decoding).
Verified metrics (held-out, reproducible)
Metric = the paper's FVE (0 = noise floor) + cosine + retrieval@1.
- AV generative round trip (activation → text → activation): best-of-24 cosine 0.28, retrieval@1 0.75 (~60× chance); 100% coherent English.
- AR given-text reconstruction: cosine 0.90, retrieval@1 0.99, FVE 0.81.
- Out-of-distribution (hand-written far-domain prompts): AV round trip cosine 0.70, retrieval@1 0.46; 100% of readings are English.
What does this mean?
NLAs turn hidden states into readable text, showing models think in themselves before responding, much like humans do with conscious and subconscious thoughts.
No, this doesn't prove that AI models are or are not conscious. Science as a whole has yet to put a single definition down for qualia, or consciousness as a whole, meaning we cannot determine if GRaPE is conscious or not.
Notes
- Anthropic's NLA research paper: https://www.anthropic.com/research/natural-language-autoencoders
- This is not a complete model; it is an NLA for GRaPE 2.1.
- Updates and announcements are posted on Skinnertopia and this Hugging Face repository.
GRaPE 2.1 Flash is developed under the SLAI (Skinnertopia Lab for Artificial Intelligence) brand and released under the Apache 2.0 license.