| --- |
| title: README |
| emoji: 🔁 |
| colorFrom: blue |
| colorTo: indigo |
| sdk: static |
| pinned: false |
| --- |
| |
| # EnvLoop Research |
|
|
| EnvLoop builds environments and evaluation for agents. EnvLoop Research publishes the models, benchmarks and code |
| behind that work, with pre-registered evaluation and the data needed to check the numbers. |
| Homepage: [www.envloop.ai](https://www.envloop.ai) · Contact: [research@envloop.ai](mailto:research@envloop.ai) |
|
|
| ## Releases |
|
|
| **Pev** — a calibrated fast-decision model for personal agents with long-term memory, and the benchmark it was |
| evaluated on. |
|
|
| - Paper (PDF): [pev.pdf](https://github.com/EnvLoop/Pev/blob/main/docs/tech-report/pev.pdf) |
| - Model: [EnvLoop/Pev-27B-LoRA](https://huggingface.co/EnvLoop/Pev-27B-LoRA) (gated, non-commercial research) |
| - Benchmark: [EnvLoop/Pev-Bench](https://huggingface.co/datasets/EnvLoop/Pev-Bench) (gated, non-commercial research) |
| - Leaderboard: [EnvLoop/Pev-Leaderboard](https://huggingface.co/spaces/EnvLoop/Pev-Leaderboard) |
| - Code and pre-registration: [github.com/EnvLoop/Pev](https://github.com/EnvLoop/Pev) |
|
|
