VectraYX-Nano: A 42M-Parameter Spanish Cybersecurity Language Model with Curriculum Learning and Native Tool Use
Researchers present VectraYX-Nano, a 42M-parameter decoder-only language model trained from scratch in Spanish for cybersecurity, featuring native tool invocation via the Model Context Protocol (MCP). The model uses a 170M-token corpus and curriculum learning with replay buffer, achieving monotonic loss descent and a conversational gate of 0.78. It is the first Spanish-native cybersecurity LLM with end-to-end MCP integration, running sub-second on commodity hardware.
[2605.13989] VectraYX-Nano: A 42M-Parameter Spanish Cybersecurity Language Model with Curriculum Learning and Native Tool Use
[Submitted on 13 May 2026]
Title:VectraYX-Nano: A 42M-Parameter Spanish Cybersecurity Language Model with Curriculum Learning and Native Tool Use
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Abstract:We present VectraYX-Nano, a 41.95M-parameter decoder-only language model trained from scratch in Spanish for cybersecurity, with a Latin-American focus and native tool invocation via the Model Context Protocol (MCP). Four contributions: (i) Corpus: VectraYX-Sec-ES, a 170M-token Spanish corpus from an eight-VM pipeline (~$25 USD) partitioned into conversational (42M tokens, OpenSubtitles-ES, OASST1), cybersecurity (118M tokens, NVD, Wikipedia-ES, CVE mirror, security blogs), and offensive-security tooling (10M tokens, ExploitDB, HackTricks, OWASP) phases. (ii) Architecture: 42M-parameter Transformer decoder with GQA, QK-Norm, RMSNorm, SwiGLU, RoPE, z-loss, and a 16,384-token byte-fallback BPE. (iii) Curriculum with replay: continual pre-training with a replay buffer yields monotonic loss descent (9.80->3.17->3.00->2.16); after SFT on OASST-ES, Alpaca-ES, CVE Q&A, and 6,327 tool-use traces, the model attains a conversational gate of 0.78+-0.05 (N=4 seeds). (iv) Two findings: a bootstrap-corpus ablation reveals a loss-vs-register inversion at nano scale; a LoRA study shows the B4 tool-selection floor of 0.000 is a corpus-density artifact, not a capacity gate -- a tool-dense corpus (2,801 examples) raises B4 to 0.145+-0.046 on Nano 42M and 0.445+-0.201 on a 260M mid-tier. The GGUF artifact is 81 MB (F16), runs at sub-second TTFT on commodity hardware under this http URL, and is to our knowledge the first Spanish-native cybersecurity LLM with end-to-end MCP integration. Corpus recipe, training scripts, GGUF weights, and B1-B5 benchmark are released.
Comments: 15 pages, 4 figures, preprint
Subjects:
Computation and Language (cs.CL)
ACM classes: I.2.7; K.6.5
Cite as: arXiv:2605.13989 [cs.CL]
(or arXiv:2605.13989v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.13989
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Juan Salas [view email] [v1] Wed, 13 May 2026 18:03:07 UTC (100 KB)
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