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# System prepended metadata

title: Algonaute 2025 models
tags: [' Projects', notes]

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tags: notes, Projects
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# Algonaute 2025 models

A little walk through how do I evaluate code base for scientific projects.

*Disclaimers*: I only read the TRIBE paper and the summary paper
[Insights from the Algonauts 2025 Winners](https://arxiv.org/abs/2508.10784v1)  in full.

## Links to the GitHub repos

[GitHub List](https://github.com/stars/htwangtw/lists/algonautes2025)

:first_place_medal: :moyai: TRIBE: https://github.com/facebookresearch/algonauts-2025

:second_place_medal: :vibration_mode: VIBE: https://github.com/Bicanski-NCG/VIBE

:third_place_medal: :repeat: Multimodal Recurrent Ensembles: https://github.com/erensemih/Algonauts2025_ModalityRNN
 
:sports_medal: :feather: Multimodal LLMs and a Lightweight Encoder: https://github.com/MedARC-AI/algonauts2025

:sports_medal: :question: CVIU-UARK: no info, very sad
 
:sports_medal: :seal: Multimodal Seq2Seq Transformer: https://github.com/Angelneer926/Algonauts_challenge


## What do I look for in a code base without running it

1. Set up instructions
2. Documentation on workflow
3. Clear versioning / release (rare)
4. **Can I identify key ideas / important features in the paper in the code?**

### Set up instructions 
:slightly_smiling_face:  - :moyai::vibration_mode::repeat::feather::seal:

All projects provide some environmental set up instructions. TRIBE model :moyai: has the least standard setup. Most projects have a simple one liner installation.

### Documentation on workflow

Everyone wrote a paper so I guess everyone pass the lowest bar, but I do like a pretty readme page. This also provide context for mapping the code base into the science :scientist: 

:slightly_smiling_face: - clear explaination on each key steps :vibration_mode: :feather: :seal:
:slightly_frowning_face: - no instruction on feature extraction :moyai:
:no_mouth: - you only need to run `main.py` for real? :repeat:

### Clear versioning

:crown: - thank you for using the release feature :vibration_mode:
:slightly_smiling_face: - at least the version control is there :feather: :seal:
:no_mouth: - Very few commits :moyai: :repeat:

### Special mention: Pretrained model download

:moyai: built into feature extraction code
:vibration_mode: built into feature extraction code
:repeat: built into feature extraction code
:feather: Should pre downloaded - no explicit instruction, but the procedure is standard
:seal: built into feature extraction code

### Discussions: Key mentions from the Insight paper

🗿 Modality dropout during training
🗿 Validation performance per model per brain parcel as softmax weights
📳 Trained the model without a causal mask
📳 Explicitly modeling the HRF
🔄 Extracted features on a strict TR-by-TR basis
🔄 A brain-inspired curriculum, early sensory regions -> higher-order association areas
🪶 A shared group head plus subjectspecific residual heads
🌊 Contrastive learning training objectives

### License

:seal: No license!!!!!
