codestral-22b-7d08ebdf·2 events·first seen Aliases: Codestral 22B, Codestral-22B
Researchers introduce CAV-STIXGen, a dataset mapping connected and autonomous vehicle (CAV) CVE descriptions to STIX threat intelligence objects, CWE entries, and MITRE ATT&CK techniques. They benchmark 11 open-weight LLMs (4B–120B parameters) across prompting strategies, achieving F1 scores up to 0.94 for STIX domain objects and 0.99 for CWE mapping, while MITRE ATT&CK mapping remains harder. A multi-agent configuration using Gemma-4-31B and Codestral-22B is also evaluated, and co-occurrence analysis of CWE/ATT&CK patterns is used to surface recurring threat patterns in the CAV domain.
Mistral AI has released Codestral Mamba, a 7.3B-parameter code-focused language model built on the Mamba state-space architecture rather than the Transformer architecture. The model offers linear-time inference and theoretically infinite sequence length, tested up to 256k tokens in-context retrieval. Developed with Mamba co-creators Albert Gu and Tri Dao, it is released under Apache 2.0 and available via HuggingFace, mistral-inference SDK, TensorRT-LLM, and Mistral's la Plateforme API. Mistral positions it as a local code assistant that performs on par with state-of-the-art transformer-based code models.