--- tags: notes, Projects --- # 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 ðŸ—ŋ Modality dropout during training ðŸ—ŋ Validation performance per model per brain parcel as softmax weights ðŸ“ģ Trained the model without a causal mask ðŸ“ģ Explicitly modeling the HRF 🔄 Extracted features on a strict TR-by-TR basis 🔄 A brain-inspired curriculum, early sensory regions -> higher-order association areas ðŸŠķ A shared group head plus subjectspecific residual heads 🌊 Contrastive learning training objectives ### License :seal: No license!!!!!