Dot

Dot is an experimental 9.8B-parameter text reasoning model built by MTEnt. This repository contains the complete BF16 Dot backbone, its separately trained recurrent-depth core, the exact loader, and measured release evidence.

The current stable release is Dot v0.4 Thinking. The name and runtime identity are Dot.

What changed in this release

Dot inserts a weight-tied recurrent-depth core after decoder layer 15. The core copies native layers 12 through 15 and runs them four times through learned residual gates.

Component	Parameters
Dot backbone	8,953,803,264
Recurrent-depth core	864,945,224
Total instantiated model	9,818,748,488

The v0.4 repair trained the recurrent core for 938 optimizer steps and processed 8,642,015 tokens from 60,000 generated reasoning records. The backbone stayed frozen during this stage. Final active gate values were 0.157696, 0.079852, 0.059039, and 0.059151.

Measured results

These are narrow internal measurements, not general model benchmarks.

On 4,096 held-out prompts from the same eight executable task generators used to construct the curriculum:

Teacher-forced metric	Zero-gate baseline	Dot v0.4
Response NLL	2.128394	0.017367
Response token accuracy	66.60%	99.43%
Exact response rate	0.00%	87.77%
Reasoning token accuracy	60.47%	99.32%
Final-answer token accuracy	99.61%	99.99%

On 256 separately seeded free-running prompts from those same task families, Dot produced a valid, non-empty thinking envelope in 100% of cases and matched the exact final answer in 248/256 cases (96.875%). No response hit the 256-token generation limit.

An adversarial spatial generalization probe was much weaker: Dot v0.4 scored 14/64 (21.875%) exact match on a newer spatial suite, including 0% on its orientation subset. That failure is why v0.4 should not be described as an ARC-AGI-capable model. A spatial repair is being evaluated separately and is not part of this stable release.

The machine-readable release metrics are in eval/thinking-v0.4.json.

Run Dot

This is a custom architecture. A normal AutoModelForCausalLM.from_pretrained call loads only the backbone and silently omits Dot's trained recurrent core. Use the included loader.

git clone https://huggingface.co/MTEnt/dot
cd dot
python -m pip install .
python examples/chat.py --model . --prompt "Explain why a passing build does not prove the UI works."

The verified runtime used Python 3.12, PyTorch 2.8.0, Transformers 5.15.0, Safetensors 0.8.0, BF16, SDPA, and an NVIDIA H200. Other hardware and precision paths have not been verified for this release.

Cache-backed decoding is deliberately disabled. The recurrent passes do not yet have correct cache ownership, so enabling a normal KV cache would risk silently wrong state. Generation recomputes the sequence at every token and is therefore slow.

Files that matter

• model-00001-of-00004.safetensors through model-00004-of-00004.safetensors: the complete Dot v0.2 semantic backbone.
• reasoning_core.safetensors: the v0.4 recurrent-depth weights.
• dot_recurrent_manifest.json: architecture, source step, metrics, and core checksum.
• dot_rd/: the required architecture and integrity-checking loader.
• release.json: release lineage, hashes, training scope, and known limits.

Scope and limitations

• Dot v0.4 is a text-only research release.
• The reported reasoning score measures the same generator families used for training, with separate seeds and hash-disjoint records. It does not prove broad reasoning, coding, world knowledge, ARC-AGI, or safety performance.
• No independent safety, bias, multilingual, coding, or production-agent audit has been completed for v0.4.
• Thinking responses can expose intermediate text. Do not put secrets in a prompt and assume the reasoning channel will conceal them.
• The BF16 package is roughly 20 GB before runtime allocations. Only the H200 path described above is verified.

Technical lineage and license

Dot's semantic backbone was initialized from Qwen/Qwen3.5-9B and then modified by MTEnt through a merged semantic LoRA stage and the recurrent-depth training described here. The original training manifest recorded the source repository but did not record its exact commit hash. The complete modified backbone is included so this release does not depend on reconstructing that missing adapter.

The upstream work and this repository are distributed under Apache License 2.0. See LICENSE, NOTICE, and release.json for attribution and provenance. This model is provided as-is, without a warranty of correctness, safety, or fitness for a particular purpose.