Advancing the frontier of artificial intelligence through open research and collaborative innovation.
| # | Project Name | Type | Details | Release Date | Size |
|---|---|---|---|---|---|
| 0 | LV Agent | Agent | Local-first autonomous agent powered by Claude Code. Run any model, keep your data, open-source and free. | 06/10/25 | 252 MB |
| 1 | Super IDE | IDE | AI-native development environment. Coding agent that runs your projects end to end. | 06/10/25 | 116 MB |
| 2 | Cleveris Math 1 | Model | Under development. | 04/15/25 | N/A |
| 3 | CANCRI // 55-E | Agent | AI 对话终端 · 多模型路由 + 本地向量记忆。自动路由 Gemini / Mistral 等模型,IndexedDB 语义记忆库,支持文本 / 图片 / OCR / 文档多模态输入与实时语音对话。 | 06/15/25 | N/A |
| 4 | Cleveris 4 Technical Report | Paper | Comprehensive technical report detailing architecture, training methodology, and evaluation results. | 04/05/25 | N/A |
| 5 | Cleveris-4-Llama-3.1-405B | Model | Frontier hybrid-mode reasoning model based on Llama-3.1-405B. | 04/01/25 | 810 GB |
| 6 | Cleveris-4-Llama-3.1-70B | Model | Smaller hybrid-mode reasoning model with 70B parameters. Shares the same improvements as the 405B variant. | 03/28/25 | 140 GB |
| 7 | Cleveris-4-14B | Model | Small and dense Cleveris variant for local inference. | 03/25/25 | 28 GB |
| 8 | Measuring Thinking Efficiency in Reasoning Models | Research | The Missing Benchmark for evaluating reasoning efficiency in large language models. | 03/20/25 | N/A |
| 9 | Cleveris 3 Dataset | Dataset | Complete dataset used in pretraining of Cleveris 3 models. | 03/15/25 | N/A |
| 10 | Sequential Monte Carlo for LLMs | Research | Taming LLMs with Sequential Monte Carlo methods. | 03/10/25 | N/A |
| 11 | Cleveris Network | Training | Open infrastructure democratizing AI development through distributed training. | 03/05/25 | N/A |
| 12 | Atropos Framework | Framework | Language Model RL Environments for advanced reinforcement learning research. | 03/01/25 | N/A |
Cleveris Research is dedicated to advancing artificial intelligence through open research, collaborative development, and transparent publication of our findings. We believe that AI should be developed in the open, with contributions from researchers worldwide.
Our work spans foundation models, reasoning systems, autonomous agents, and the infrastructure needed to train them at scale. We publish our models, datasets, and research papers to accelerate progress in the field.