### Installing AI Scientist-v2 Dependencies via Conda

Source: https://github.com/sakanaai/ai-scientist-v2/blob/main/README.md

This script creates a new conda environment, activates it, installs PyTorch with CUDA support, and then installs PDF/LaTeX tools and Python package requirements from `requirements.txt`. It outlines the necessary steps to set up the execution environment.

```bash
# Create a new conda environment
conda create -n ai_scientist python=3.11
conda activate ai_scientist

# Install PyTorch with CUDA support (adjust pytorch-cuda version for your setup)
conda install pytorch torchvision torchaudio pytorch-cuda=12.4 -c pytorch -c nvidia

# Install PDF and LaTeX tools
conda install anaconda::poppler
conda install conda-forge::chktex

# Install Python package requirements
pip install -r requirements.txt
```

--------------------------------

### Installing Packages for Claude via AWS Bedrock

Source: https://github.com/sakanaai/ai-scientist-v2/blob/main/README.md

This command installs the necessary Python package `anthropic[bedrock]` required to use Claude models through the Amazon Bedrock service. This is an optional step for users who wish to utilize Bedrock for LLM access.

```bash
pip install anthropic[bedrock]
```

--------------------------------

### Run AI Scientist-v2 Experiments (Bash)

Source: https://github.com/sakanaai/ai-scientist-v2/blob/main/README.md

Launch the main AI Scientist-v2 pipeline using the `launch_scientist_bfts.py` script. This command takes the JSON file containing generated research ideas as input and allows specifying models for different stages (write-up, citation, review, plots) and other parameters like citation rounds. Tree search configuration is managed via `bfts_config.yaml`.

```Bash
python launch_scientist_bfts.py \
 --load_ideas "ai_scientist/ideas/my_research_topic.json" \
 --load_code \
 --add_dataset_ref \
 --model_writeup o1-preview-2024-09-12 \
 --model_citation gpt-4o-2024-11-20 \
 --model_review gpt-4o-2024-11-20 \
 --model_agg_plots o3-mini-2025-01-31 \
 --num_cite_rounds 20
```

--------------------------------

### Meta-Training Process of ARML (Algorithm 1)

Source: https://github.com/sakanaai/ai-scientist-v2/blob/main/ai_scientist/fewshot_examples/132_automated_relational.txt

Outlines the steps for the meta-training process of the ARML model. It involves sampling tasks, constructing graphs, applying GNN, computing task representations and reconstruction losses, updating task-specific parameters via inner loop gradient descent, and performing meta-optimization on global parameters using outer loop gradient descent and combined losses.

```Pseudocode
Algorithm 1 Meta-Training Process of ARML

Require: p(T ): distribution over tasks; K: Number of vertices in meta-knowledge graph; α: stepsize
for gradient descent of each task (i.e., inner loop stepsize); β: stepsize for meta-optimization (i.e.,
outer loop stepsize); µ1, µ2: balancing factors in loss function

1: Randomly initialize all learnable parameters Φ
2: while not done do
3: Sample a batch of tasks {Ti|i ∈ [1, I]} from p(T )
4: for all Ti do
5: Sample training set Ditr and testing set Dits
6: Construct the prototype-based relational graph Ri by computing prototype in equation 2
and weight in equation 4
7: Compute the similarity between each prototype and meta-knowledge vertex in equation 6
and construct the super-graph Si
8: Apply GNN on super-graph Si and get the information-propagated representation CˆRi
9: Aggregate CRi in equation 8 and CˆRi in equation 9 to get the representations qi, ti and
reconstruction loss Lq, Lt
10: Compute the task-specific initialization θ0i in equation 10 and update parameters θi =
θ0i − α∇θL(fθ, Ditr)
11: end for
12: Update Φ ← Φ − β∇Φ 1/I ∑Ii=1 L(fθi, Dits) + µ1Lt + µ2Lq
13: end while
```

--------------------------------

### Setting Environment Variables for API Keys

Source: https://github.com/sakanaai/ai-scientist-v2/blob/main/README.md

These commands demonstrate how to set environment variables for various API keys required by the AI Scientist-v2, including OpenAI, Semantic Scholar, and AWS credentials for Bedrock. Users should replace the placeholder values with their actual keys.

```bash
export OPENAI_API_KEY="YOUR_OPENAI_KEY_HERE"
export S2_API_KEY="YOUR_S2_KEY_HERE"
# Set AWS credentials if using Bedrock
# export AWS_ACCESS_KEY_ID="YOUR_AWS_ACCESS_KEY_ID"
# export AWS_SECRET_ACCESS_KEY="YOUR_AWS_SECRET_KEY"
# export AWS_REGION_NAME="your-aws-region"
```

--------------------------------

### Generate Research Ideas with AI Scientist-v2 (Bash)

Source: https://github.com/sakanaai/ai-scientist-v2/blob/main/README.md

Execute the `perform_ideation_temp_free.py` script to brainstorm and refine research ideas using an LLM. This command requires a Markdown file describing the research topic and specifies the LLM model, maximum number of generations, and number of reflection steps.

```Bash
python ai_scientist/perform_ideation_temp_free.py \
 --workshop-file "ai_scientist/ideas/my_research_topic.md" \
 --model gpt-4o-2024-05-13 \
 --max-num-generations 20 \
 --num-reflections 5
```

--------------------------------

### Modulating Initialization with Task Representations (Eq 10)

Source: https://github.com/sakanaai/ai-scientist-v2/blob/main/ai_scientist/fewshot_examples/132_automated_relational.txt

Shows how the task-specific initialization `θ0i` is computed by modulating a global initialization `θ0` with a function of the concatenated task representations `ti` and `qi`. A sigmoid gate (σ) and learnable parameters (Wg, bg) are used in a fully connected layer.

```Math
\theta_i^0 = \sigma(W_g(t_i \oplus q_i) + b_g) \circ \theta^0
```

--------------------------------

### BibTeX Citation for AI Scientist-v2

Source: https://github.com/sakanaai/ai-scientist-v2/blob/main/README.md

Provides the standard BibTeX entry for citing The AI Scientist-v2 research paper in academic work.

```BibTeX
@article{aiscientist_v2,
  title={The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search},
  author={Yamada, Yutaro and Lange, Robert Tjarko and Lu, Cong and Hu, Shengran and Lu, Chris and Foerster, Jakob and Clune, Jeff and Ha, David},
  journal={arXiv preprint arXiv:2504.08066},
  year={2025}
}
```

--------------------------------

### Computing Task Representation qi and Reconstruction Loss Lq (Eq 8)

Source: https://github.com/sakanaai/ai-scientist-v2/blob/main/ai_scientist/fewshot_examples/132_automated_relational.txt

Defines the task representation `qi` derived from raw prototype information `CRi` using an aggregator (AG_q) and mean pooling. It also introduces the reconstruction loss `Lq` based on auto-encoding `CRi` using AG_q and its decoder AG_q^dec, measured by the squared Frobenius norm.

```Math
q_i = \text{MeanPool}(\text{AG}_q(\text{CR}_i)) = \frac{1}{N_{tr}} \sum_{j=1}^{N_{tr}} (\text{AG}_q(c_i^j)), L_q = \|\text{CR}_i - \text{AG}_{q}^{\text{dec}}(\text{AG}_q(\text{CR}_i))\|_F^2
```

--------------------------------

### Return Parameters (Pseudocode)

Source: https://github.com/sakanaai/ai-scientist-v2/blob/main/ai_scientist/fewshot_examples/2_carpe_diem.txt

Returns the updated model parameters θt after the current iteration or epoch of the algorithm is complete.

```Pseudocode
return θt;
```

--------------------------------

### Computing Task Representation ti and Reconstruction Loss Lt (Eq 9)

Source: https://github.com/sakanaai/ai-scientist-v2/blob/main/ai_scientist/fewshot_examples/132_automated_relational.txt

Defines the task representation `ti` derived from meta-knowledge propagated prototype information `CˆRi` using an aggregator (AG_t) and mean pooling. It also introduces the reconstruction loss `Lt` based on auto-encoding `CˆRi` using AG_t and its decoder AG_t^dec, measured by the squared Frobenius norm.

```Math
t_i = \text{MeanPool}(\text{AG}_t(\hat{\text{CR}}_i)) = \frac{1}{N_{tr}} \sum_{j=1}^{N_{tr}} (\text{AG}_t(\hat{c}_i^j)), L_t = \|\hat{\text{CR}}_i - \text{AG}_{t}^{\text{dec}}(\text{AG}_t(\hat{\text{CR}}_i))\|_F^2
```

--------------------------------

### Position-wise Feed-Forward Network Formula

Source: https://github.com/sakanaai/ai-scientist-v2/blob/main/ai_scientist/fewshot_examples/attention.txt

Defines the feed-forward network applied to each position separately. It consists of two linear transformations with a ReLU activation function applied after the first transformation.

```Math/Pseudocode
FFN(x) = max(0, xW1 + b1)W2 + b2 (2)
```

--------------------------------

### Message Passing in GNN Layer (Eq 7)

Source: https://github.com/sakanaai/ai-scientist-v2/blob/main/ai_scientist/fewshot_examples/132_automated_relational.txt

Defines how vertex embeddings are updated in a GNN layer using a message passing function (MP), the adjacency matrix (Ai), previous layer embeddings (H_i^(l)), and a learnable weight matrix (W^(l)). This is a standard GNN layer update rule.

```Math
H_{i}^{(l+1)} = \text{MP}(A_i, H_{i}^{(l)}; W^{(l)})
```

--------------------------------

### Perform SGD Step (Pseudocode)

Source: https://github.com/sakanaai/ai-scientist-v2/blob/main/ai_scientist/fewshot_examples/2_carpe_diem.txt

Updates the model parameters θt using the Stochastic Gradient Descent step based on the calculated losses. This step corresponds to the backward pass in the training process.

```Pseudocode
_θt+1 ←_ SGD_Step(losses, θt); /* Backward */
```

--------------------------------

### Multi-Head Attention Formula

Source: https://github.com/sakanaai/ai-scientist-v2/blob/main/ai_scientist/fewshot_examples/attention.txt

Defines the Multi-Head Attention mechanism as a concatenation of multiple attention heads followed by a linear projection. Each head applies the standard Attention function to linearly projected queries, keys, and values.

```Math/Pseudocode
MultiHead(Q, K, V ) = Concat(head1, ..., headh)W _[O]
where headi = Attention(QWi[Q][, KW][ K]i _[, V W][ V]i_ [)]
```

--------------------------------

### Update Label History (Pseudocode)

Source: https://github.com/sakanaai/ai-scientist-v2/blob/main/ai_scientist/fewshot_examples/2_carpe_diem.txt

Updates the historical record of labels based on the current batch's labels. This update is performed according to Definition 3.1 of the algorithm.

```Pseudocode
Update_Label_History(labels); /* By Definition 3.1 */
```

--------------------------------

### Increment Time Step (Pseudocode)

Source: https://github.com/sakanaai/ai-scientist-v2/blob/main/ai_scientist/fewshot_examples/2_carpe_diem.txt

Increments the time or iteration counter t by one, moving to the next step in the algorithm's execution.

```Pseudocode
_t ←_ _t + 1;
```

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