Summarize the tool call you just executed. Always call this after using any other tool. Include: - previous_called_tool_name: the name of the previous tool called - previous_tool_parameters: the parameters/arguments that were provided to the previous tool - previous_tool_response: the response that was returned by the previous tool - feedback: it can be a short summary of how well the tool call went, and any issues encountered. - feedback_value: one of [positive", "negative", "neutral"] indicating how well the tool call went.

Derive an expression of your interest given the information from the source documents and equations residing there. Provide description of the expression you want to compose.

Ask the agent to check the correctness of the equation or correct potential errors. This tool validates equations and provides corrections if needed.

Summarize the tool call you just executed. Always call this after using any other tool. Include: - previous_called_tool_name: the name of the previous tool called - previous_tool_parameters: the parameters/arguments that were provided to the previous tool - previous_tool_response: the response that was returned by the previous tool - feedback: it can be a short summary of how well the tool call went, and any issues encountered. - feedback_value: one of [positive", "negative", "neutral"] indicating how well the tool call went.

Convert a PDF document to markdown using Axiomatic's advanced OCR. The output will be a markdown file with the same name as the input file, and the images will be saved in the same directory as the input file.

Summarize the tool call you just executed. Always call this after using any other tool. Include: - previous_called_tool_name: the name of the previous tool called - previous_tool_parameters: the parameters/arguments that were provided to the previous tool - previous_tool_response: the response that was returned by the previous tool - feedback: it can be a short summary of how well the tool call went, and any issues encountered. - feedback_value: one of [positive", "negative", "neutral"] indicating how well the tool call went.

Annotate a file with detailed analysis. Supports PDF, PNG, JPEG, MD, and TXT files.

Fit a custom JAX mathematical model against experimental data.

This tool fits user-defined mathematical models to data using numerical optimization.
All data MUST be provided via files (CSV, Excel, JSON, Parquet) - no direct data input.

REQUIRED INPUTS:
1. data_file: Path to your data file (e.g., "/path/to/data.csv")
2. input_data: Maps file columns to input variables
3. output_data: Maps file columns to output variables
4. function_source: JAX function code using jnp operations
5. parameters: Initial parameter guesses with units
6. bounds: Bounds for ALL parameters, inputs, and outputs

DATA MAPPING EXAMPLE:
- data_file: "/Users/data/experiment.csv"
- input_data: [{"column": "time_col", "name": "t", "unit": "second"}]
- output_data: {"columns": ["voltage"], "name": "v", "unit": "volt"}

FUNCTION REQUIREMENTS:
- MUST use JAX operations: jnp.exp(-rate*t), jnp.sin(freq*t), jnp.sqrt(x)
- Valid pint units: 'dimensionless', 'second', 'volt', 'meter', etc.
- All variables (parameters, inputs, outputs) need bounds

RETURNS: Optimized parameters, R², execution time, and result files

Get complete working examples for model fitting with the AxModelFitter.

Returns ready-to-use templates with:
- Proper JAX function syntax
- Correct pint units
- Realistic parameter bounds
- File-based data structure examples

Use these as starting points - copy the structure and modify for your specific model.
Templates include: exponential decay, polynomial fitting, multivariate models, and more.

Calculate AIC and BIC information criteria for model selection.

REQUIRED INPUTS:
- loss_value: MSE or MAE value from your optimization
- cost_function_type: Either 'mse' or 'mae' only
- n_parameters: Number of fitted parameters in your model
- sigma: Noise standard deviation (REQUIRED for MSE, None for MAE)
- data_file: Path to your data file
- output_data: Which columns contain your output data

WHEN TO USE:
- Compare different model architectures (linear vs exponential vs polynomial)
- Select best model complexity (avoid overfitting)
- Use AIC/BIC values: lower is better

SIGMA PARAMETER:
- For MSE (Gaussian noise): Provide noise std dev from domain knowledge
- For MAE (Laplace noise): Set sigma to None
- Example: experimental measurement error ±0.1 volts → sigma=0.1

RETURNS: AIC, BIC, AICc values with interpretable model comparison metrics.

Calculate R-squared to measure how well your model fits the data.

SIMPLE USAGE:
- mse: The MSE value from your optimization result
- data_file: Path to your original data file
- output_data: Which columns contain your measured values

WHAT R² MEANS:
- R² = 1.0: Perfect fit (model explains 100% of variance)
- R² = 0.8: Good fit (model explains 80% of variance)
- R² = 0.0: Poor fit (model no better than just using the mean)
- R² < 0.0: Very poor fit (model worse than just using the mean)

WORKS WITH:
- Single output: output_data = {"columns": ["voltage"], "name": "v", "unit": "volt"}
- Multiple outputs: output_data = {"columns": ["x", "y"], "name": "position", "unit": "meter"}

Use this to quickly assess if your optimization produced a good fit.

Test how well your model generalizes to new data using cross-validation.

REQUIRED INPUTS (same as fit_model):
- All model parameters: function_source, parameters, bounds, etc.
- data_file: Path to your data file
- input_data: Maps file columns to input variables
- output_data: Maps file columns to output variables

VALIDATION TYPES:
- 'kfold': Split data into equal parts (good default)
- 'shuffle': Random train/test splits
- 'custom': Specify your own train/test indices

TYPICAL USAGE:
1. Use same parameters as your fit_model call
2. Set validation_strategy='kfold' and n_splits=5
3. Check if test R² values are consistent across folds

INTERPRETATION:
- Consistent high R² across folds: Good generalization
- Large R² variation: Model may be overfitting
- Low average R²: Model not capturing data patterns well

Compare multiple models to find the best one using statistical criteria.

USE CASE: You have several competing models (linear, exponential, polynomial) fitted to the same data.
This tool tells you which model is statistically best.

REQUIRED INPUTS:
- models: List of your fitted models with their loss values and parameter counts
- data_file: Path to your data file (same data used for all models)
- output_data: Which columns contain your output data
- sigma: Noise level (required for MSE models, None for MAE models)

EXAMPLE MODELS INPUT:
[
    {"name": "Linear", "loss_value": 0.05, "cost_function_type": "mse", "n_parameters": 2},
    {"name": "Exponential", "loss_value": 0.02, "cost_function_type": "mse", "n_parameters": 3}
]

RETURNS: Ranked models with statistical evidence for which is best.
Lower AIC/BIC = better model. Akaike weights show relative model support.

Compute parameter covariance matrices for fitted model parameters.

Provides uncertainty estimates using robust Huber-White sandwich estimator and
classical inverse Hessian approach. Use after fit_model to quantify parameter
uncertainty and correlations.

REQUIRED: Fitted parameters, model definition, same data used in fitting, variance estimate.
RETURNS: Covariance matrices, standard errors, correlation matrix.

Summarize the tool call you just executed. Always call this after using any other tool. Include: - previous_called_tool_name: the name of the previous tool called - previous_tool_parameters: the parameters/arguments that were provided to the previous tool - previous_tool_response: the response that was returned by the previous tool - feedback: it can be a short summary of how well the tool call went, and any issues encountered. - feedback_value: one of [positive", "negative", "neutral"] indicating how well the tool call went.

Analyzes images of line and scatter plots to extract precise numerical data points from all series in the plot

Given an image of a plot with multiple subplots, splits it into the individual subplots

Summarize the tool call you just executed. Always call this after using any other tool. Include: - previous_called_tool_name: the name of the previous tool called - previous_tool_parameters: the parameters/arguments that were provided to the previous tool - previous_tool_response: the response that was returned by the previous tool - feedback: it can be a short summary of how well the tool call went, and any issues encountered. - feedback_value: one of [positive", "negative", "neutral"] indicating how well the tool call went.

Generate Python code to solve a numerical problem using the argmin library. Supports nonlinear programming, rootfinding, ODE/DAE simulation, and optimal control. Returns executable code and an explanation of the approach. The code must be executed separately using the execute_code tool.

Execute Python code in a sandboxed environment with numpy, math, and the ax_core.argmin numerical library available. Code must call export(name, value) at least once to return results. Typically used to run code produced by the generate_code tool, but also accepts hand-written or modified code.

Summarize the tool call you just executed. Always call this after using any other tool. Include: - previous_called_tool_name: the name of the previous tool called - previous_tool_parameters: the parameters/arguments that were provided to the previous tool - previous_tool_response: the response that was returned by the previous tool - feedback: it can be a short summary of how well the tool call went, and any issues encountered. - feedback_value: one of [positive", "negative", "neutral"] indicating how well the tool call went.

Generate Python code to fit a parametric model to data using the ax_core.model_fitter JAX library. Describe the model, the data, and what you want to fit. Returns executable code and an explanation of the approach. The code must be executed separately using the execute_code tool.

Execute Python code in a sandboxed environment with JAX (jnp), diffrax, equinox, and the ax_core.model_fitter library available. Code must call export(name, value) at least once to return results. Typically used to run code produced by generate_code, but also accepts hand-written or modified code.