Provides verified, model-attested end-to-end agent coding and debugging trajectories (JSONL). Each whole-session trace was produced by moonshotai/kimi-k3 on the pi/openrouter runtime, passed acceptance tests and independent model screening — useful for SFT, distillation, and analyzing tool-use behavior.
[next-mdx-remote-client] error compiling MDX: Unexpected character `1` (U+0031) before name, expected a character that can start a name, such as a letter, `$`, or `_` More information: https://mdxjs.com/docs/troubleshooting-mdx
Provides self-contained synthetic environments for training data-analysis agents. This slice contains 1,538 tasks, each graded with a rubric designed to evaluate analytical findings.
Evaluates data agents on 100 tasks that require evidence-backed conclusions from raw files spanning multiple business systems. Each task comes from a synthetic world and tests either causal explanations or downstream impact analysis against a task-specific rubric.
Diagnoses physical plausibility in generated videos by scoring adherence to 13 observable laws. It pairs benchmark prompts and model outputs with quality-controlled human ratings for fine-grained evaluation.
