Use Cases
Auto Research
Evaluate whether research agents improve the metric that matters. Connect diagnosis across the research loop with reproducible tasks, expert reference solutions, and traces of the decisions behind measurable results.
Where models
fall short
Below-baseline results
An experiment can run cleanly and still fail to outperform a random baseline. Executable code alone does not establish progress on the task.
Fragile generalization
A solution may work for one data pool or model, then lose its advantage when the seed, source mix, or checkpoint changes.
Resource limits
Training runs can exceed GPU, memory, or wall-clock budgets without returning a usable result. These limits are part of the research task.
From diagnosis to improvement
BakeLens
Audit the research pipeline
- Trace task understanding, planning, code, training, evaluation, and retries to follow the agent’s decisions through the entire research loop.
- Score attempts against the task baseline and reference solution, using the graded metric to assess what each run achieved.
- Identify wasted compute, unsuitable heuristics, and silent budget limits that prevent an otherwise executable approach from producing useful results.
Proof
Provide verified research environments
- Build reproducible Docker task environments with fixed training pipelines, datasets, and graders for executable research work.
- Include baseline and reference solutions written by working ML researchers, with metric scores attached to the results.
- Provide step-by-step expert traces showing how researchers move from a task specification to a solution that improves the metric.
What you get
- Verified Task Environments
- Sandboxed Docker tasks with fixed pipelines, seeds, hardware budgets, and graded metrics, following the structure of AutoLab’s data_select_ifeval task.
- Expert Solution Traces
- Reference solutions that outperform the baseline, accompanied by researcher reasoning, code changes, and ablations behind the decisions.
- Research Evaluation Suite
- Held-out tasks that measure metric improvements, allowing teams to assess research outcomes alongside the agent’s ability to execute code.
Have a use case in mind?
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