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SAGE Celer 2.6 Technical Card

arXiv 2604.14168 ↗First posted 24 Mar 2026cs.CL cs.AIcited by 0

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Abstract (arXiv)

We introduce SAGE Celer 2.6, the latest in our line of general-purpose Celer models from SAGEA. Celer 2.6 is available in 5B, 10B, and 27B parameter sizes and benefits from extensive architectural modifications and further pre-training on an undisclosed model. Using our Inverse Reasoning (IR) pipeline, SAGEA natively trains Celer 2.6 to validate its own logic paths, minimizing cascading error and hallucination in complex reasoning tasks. Celer 2.6 also boasts natively integrated multimodal functionality with an end-to-end vision encoder to avoid common pitfalls in adapter-based approaches. Celer 2.6 provides highly competitive results on mathematics, coding, and general intelligence benchmarks (ACUMEN), along with low latency. Most importantly, Celer 2.6 is specifically optimized for South Asian language support, with a custom tokenizer for the Devanagari script and strong performance in both Nepali and Hindi without sacrificing English reasoning ability.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 20 references that are in the map. First posted on arXiv 24 Mar 2026.

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References · 16

  1. 2024The Llama 3 Herd of Modelscited by 14,715
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  11. 2024MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmarkcited by 1,848
  12. 2022Red Teaming Language Models with Language Modelscited by 1,026
4 more
  1. 2022Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learnedcited by 729
  2. 2023XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Modelscited by 505
  3. 2020How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Modelscited by 318
  4. 2026SAGE-32B: Agentic Reasoning via Iterative Distillationcited by 2

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