Full task supplied to the agent
What the run produced
Follow the evidence
Inspect the entire recorded conversation, including failed attempts and corrections.
Run details & original task
Saved model-generated code and tool outputs. Playback timing is illustrative; this replay does not run a model or Python in your browser.
Notebook is a faithful transcript of saved Python cells and outputs, including failed attempts. It has not been validated as a clean rerunnable notebook; paths and runtime dependencies may need adjustment.
Agent results · JSON
What the results mean
Lamuir's explanation
Written by the agent while it solved the task, from the values it computed; shown unedited.
6 Examples Recorded from Lamuir Runs
The answer comes with its working.
Read the input
Frozen official-source data and an explicit task.
Write and execute
Python cells run in a persistent session.
Check and revise
Inspect outputs and revisit errors when detected.
Keep the evidence
Code, outputs and a separate numerical review.
These examples were run by Lamuir v20102, the submitted version, with NVIDIA Nemotron 3 Super on Nebius Token Factory. Each result is graded against an independent reference implementation. They are selected examples, not a benchmark pass rate.
Run Lamuir on your own question.
Lamuir is open source. The repository explains how to run it with your own Nebius Token Factory key and NVIDIA Nemotron 3 Super, how to ask your own question, and how to let it find official data with Tavily.