echo — Echo-Memory ablation arm (ucpe + two-chunk memory)

This is an unofficial derivative artifact, not the official Echo-Memory release. It contains one ablation arm re-trained from the Echo-Memory codebase, produced for internal study. For the official code, paper and checkpoints, use the links below.

Upstream / attribution

Original work	Echo-Memory: A Controlled Study of Memory in Action World Models
Authors	Echo Team @ Joy Future Academy, JD
Paper	arXiv:2606.09803
Official code	https://github.com/Echo-Team-Joy-Future-Academy-JD/Echo-Memory
Official checkpoints	https://huggingface.co/Echo-Team/Echo-Memory
Code license	CC BY 4.0
Base model	Wan2.1-T2V-1.3B (Apache 2.0)

The Echo-Memory code included here is redistributed under CC BY 4.0 with attribution to the Echo Team. The weights are derived from Wan2.1-T2V-1.3B, which is Apache 2.0; that license and its notices continue to apply to the derived weights.

What this repository contains

code/                       Echo-Memory codebase as used for this run
checkpoints/
  Step-10000.safetensors    3.5 GB
  Step-20000.safetensors    3.5 GB
  Step-30000.safetensors    3.5 GB   (final, max_train_steps = 30000)

Training configuration

Single arm of a memory-mechanism ablation, run name memory_baselines_basic_abl_ucpe_memory_two_chunk.

• Base weights: Wan2.1-T2V-1.3B (DiT + umt5-xxl text encoder + Wan2.1_VAE)
• Trainable: dit only, --remove_prefix_in_ckpt pipe.dit.
• Memory: --enable_context_memory --context_source replay --prev_chunk_frames 81 --context_memory_frames 5
• Mechanism under test: --use_cgla_memory --cgla_mechanism ucpe --cgla_every_n_blocks 4 --cgla_aux_loss_weight 0.01
• Also enabled: --use_moc --moc_temperature 1.0, --train_cam_pose, --add_action_attn, --use_rt_relative, --use_anchor_frame, --cfg_target_only
• Resolution / length: 352 x 640, 81 frames
• Optimisation: lr 2e-5, 1 epoch, batch 1/device, grad-accum 1, --timestep_shift 15, --context_drop_prob 0.1, 30,000 steps
• Hardware: 1x NVIDIA H20

Data — not redistributed here

Training used the Context-as-Memory dataset (Unreal-Engine rendered environments, ~331 GB of frames). It is not included in this repository: it is third-party data and the copy used here carried no redistribution licence. Obtain it from its original source.

Status and caveats

• This is one arm of an ablation sweep, uploaded as a research artifact. Its results had not been analysed at the time of upload — no quality claim is made, and it should not be read as the best or recommended configuration.
• It is not a reproduction of, or a replacement for, the official Echo-Memory checkpoints. Comparisons against the paper's numbers are not valid without matching the official evaluation protocol.

Citation

Please cite the original work:

@article{echomemory2026,
  title  = {Echo-Memory: A Controlled Study of Memory in Action World Models},
  author = {Echo Team, Joy Future Academy, JD},
  journal= {arXiv preprint arXiv:2606.09803},
  year   = {2026}
}