---
license: apache-2.0
language:
- en
base_model: dllm-hub/Qwen3-0.6B-diffusion-mdlm-v0.1
tags:
- pytorch
- diffusion-language-models
- multi-agent
- latent-communication
- adapter
- arxiv:2610.28905
---

# Hidden Messages: Tiny-A2D HM-Synth communication channel

Task-specific, supervised communication-channel weights accompanying
[Hidden Messages Between Diffusion Language Model Agents: Causal Evidence for Latent Communication](https://arxiv.org/abs/2610.28905),
accepted at the DiffuLM Workshop at NeurIPS 2026.

This is a communication adapter, **not a standalone language model**. The frozen
Tiny-A2D backbone is downloaded separately. The adapter consists of a sender-state
compressor and a gated residual receiver fuser, with four 128-dimensional message
slots. It was trained on HM-Synth distributed chain-lookup tasks.

## Files and provenance

- `tiny_a2d_channel.pt`: original PyTorch state dictionary.
- `config.yaml`: original backbone revision, synthetic-task settings, channel
  architecture, training settings, and evaluation settings.
- `LICENSE`: Apache-2.0 license for this release's channel weights and documentation.
- `THIRD_PARTY_LICENSES.md`: attribution for external dependencies and datasets.

These are the existing paper artifacts, mirrored without retraining or checkpoint
conversion from [the public code release](https://github.com/alanrbtx/hidden-messages-diffusion-agents/tree/1d1477b423ba4245b564840ce46aee039c79bcc8).
The checkpoint source is `checkpoints/tiny_a2d_channel.pt`.
No new inference or benchmark validation was performed for this Hub mirror.

## Download

```python
from huggingface_hub import hf_hub_download

checkpoint_path = hf_hub_download(
    repo_id="RobotLearningLab/hidden-messages-tiny-a2d",
    filename="tiny_a2d_channel.pt",
)
config_path = hf_hub_download(
    repo_id="RobotLearningLab/hidden-messages-tiny-a2d",
    filename="config.yaml",
)
```

The download example does not run inference. The original `ReceiverChannel` class
is defined in
[`phase5_hm_synth_tiny.py`](https://github.com/alanrbtx/hidden-messages-diffusion-agents/blob/1d1477b423ba4245b564840ce46aee039c79bcc8/code/scripts/phase5_hm_synth_tiny.py),
with compressor and fusion modules in `code/src/hidden_messages/communication/`.
Use that architecture and the accompanying config to load the trusted state
dictionary with `torch.load(..., weights_only=True)`. The original driver includes
training and evaluation, not just a checkpoint-only inference CLI. This repository
does not implement Transformers `AutoModel.from_pretrained` or a hosted
text-generation endpoint.

Required backbone: `dllm-hub/Qwen3-0.6B-diffusion-mdlm-v0.1`, revision
`c8d24a3f4adaeef46881b450e1bf7d1005203bd7`. Use the pinned dependencies in the code
repository and a CUDA environment for the reported model workflow. The full
backbone weights are not included here.

## Scope and limitations

Training uses union-context teacher states and paired matched-versus-deranged
supervision. Evaluation uses split private contexts. The adapter is task-specific;
it is not an emergent language, a privacy mechanism, or a demonstrated universal
cross-task protocol. It should not be assumed interchangeable with the Dream QASC
adapter. Independent agent trajectories, the designated receiver, and causal
message interventions are part of the evaluated setup.

For reported results and experimental scope, see the paper and
[`evidence/tiny_controlled_results.json`](https://github.com/alanrbtx/hidden-messages-diffusion-agents/blob/1d1477b423ba4245b564840ce46aee039c79bcc8/evidence/tiny_controlled_results.json).

## Citation

```bibtex
@misc{gazzaev2026hiddenmessages,
  title={Hidden Messages Between Diffusion Language Model Agents: Causal Evidence for Latent Communication},
  author={Alan-Barsag Gazzaev and Alexey Gavrilov and Sergey Muravyov and Ilya Makarov},
  year={2026},
  eprint={2610.28905},
  archivePrefix={arXiv}
}
```