--- title: "Akhiad Bercovich — author profile and papers · Reasoning" description: "Akhiad Bercovich: 4 papers on Elamur Research (2025), h-index 1 — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/akhiad-bercovich/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/akhiad-bercovich/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Akhiad Bercovich # Akhiad Bercovich Akhiad Bercovich has 4 papers in our library, in 2025, most of them on [Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/), [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/); often with [Abhinav Khattar](https://md.research.elamur.ai/free/en/authors/abhinav-khattar/), [Aleksander Ficek](https://md.research.elamur.ai/free/en/authors/aleksander-ficek/), [Aarti Basant](https://md.research.elamur.ai/free/en/authors/aarti-basant/). 4 papers in our library · 2025 [OpenAlex](https://openalex.org/A5121353884) · [**h-index 1**](https://openalex.org/authors/A5121353884) · [13 works](https://openalex.org/works?filter=author.id:A5121353884) · [4 citations](https://openalex.org/works?filter=author.id:A5121353884&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 4 works in our library](https://md.research.elamur.ai/free/en/authors/akhiad-bercovich/#papers) · [🔗 Cited 187 times in our library](https://md.research.elamur.ai/free/en/authors/akhiad-bercovich/cited-by/) · [1 more in our library without a page yet](#also) · [⭐ Paper Stars ranking: #5855](https://md.research.elamur.ai/free/en/paper-stars/page/59/#star-5855) The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Akhiad+Bercovich) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Akhiad+Bercovich) · [dblp](https://dblp.org/search?q=Akhiad+Bercovich) · [ACL Anthology](https://aclanthology.org/search/?q=Akhiad+Bercovich) ## Fields, topics and concepts Of its works in our library, with the number of works on each. 📚 Shelves 5 The field of science this work belongs to (OpenAlex fields).[Arts and Humanities · 4 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Computer Science · 4 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Artificial Intelligence · 4 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Computer Vision and Pattern Recognition · 4 works](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · [Language and Linguistics · 4 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) 🧭 Topics 2 Research subjects and languages the work is about.[Reasoning · 3 works](https://md.research.elamur.ai/free/en/topics/reasoning/) · [Language models & pretraining · 1 work](https://md.research.elamur.ai/free/en/topics/language-models/) ## Works by year ### 2020s [**2025** 4 papers](https://md.research.elamur.ai/free/en/authors/akhiad-bercovich/year/2025/) ## Papers The most cited first (OpenAlex and our library). 1. **[Llama-Nemotron: Efficient Reasoning Models](https://md.research.elamur.ai/free/en/papers/bercovich-2025-llama-nemotron-efficient-reasoning-models/)** *(2025 · 76 citations)* Reasoning-oriented language models write long chains of thought, so inference cost increasingly limits how useful they are. 2. **[NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model](https://md.research.elamur.ai/free/en/papers/basant-2025-nvidia-nemotron-nano-2-accurate/)** *(2025 · 39 citations)* Reasoning models spend most of their compute producing long chains of thought. In standard Transformers, the attention KV cache makes such long generations slow and memory-hungry. NVIDIA addresses this with Nemotron-Nano-9B-v2, which uses the Nemotron-H hybrid design: most self-attention layers are replaced by… 3. **[Nemotron-H: A Family of Accurate and Efficient Hybrid Mamba-Transformer Models](https://md.research.elamur.ai/free/en/papers/blakeman-2025-nemotron-h-family-accurate-efficient/)** *(2025 · 38 citations)* Self-attention costs grow with sequence length, both in compute per generated token and in KV-cache memory. 4. **[Nemotron 3 Nano: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning](https://md.research.elamur.ai/free/en/papers/blakeman-2025-nemotron-3-nano-open-efficient/)** *(2025 · 27 citations)* NVIDIA presents Nemotron 3 Nano 30B-A3B, an open language model for agentic use, reasoning and chat that aims for a better balance between accuracy and inference speed. ## All works 5 works of theirs in our library, the most cited first; 4 with a page here, the others link their original until we summarise them. 1. [**Llama-Nemotron: Efficient Reasoning Models arXiv:2505.00949**Akhiad Bercovich, Itay Levy, Izik Golan, et al.*2025 · 76 citations*](https://md.research.elamur.ai/free/en/papers/bercovich-2025-llama-nemotron-efficient-reasoning-models/) 2. [**NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model arXiv:2508.14444**NVIDIA: Aarti Basant, Abhijit Khairnar, Abhijit Paithankar, et al.*2025 · 39 citations*](https://md.research.elamur.ai/free/en/papers/basant-2025-nvidia-nemotron-nano-2-accurate/) 3. [**Nemotron-H: A Family of Accurate and Efficient Hybrid Mamba-Transformer Models arXiv:2504.03624**NVIDIA: Aaron Blakeman, Aarti Basant, Abhinav Khattar, et al.*2025 · 38 citations*](https://md.research.elamur.ai/free/en/papers/blakeman-2025-nemotron-h-family-accurate-efficient/) 4. [**Nemotron 3 Nano: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning arXiv:2512.20848**NVIDIA: Aaron Blakeman, Aaron Grattafiori, Aarti Basant, et al.*2025 · 27 citations*](https://md.research.elamur.ai/free/en/papers/blakeman-2025-nemotron-3-nano-open-efficient/) 5. **[Puzzle: Distillation-Based NAS for Inference-Optimized LLMs ↗](https://arxiv.org/abs/2411.19146) arXiv:2411.19146 · no summary yet**Akhiad Bercovich et al.*2024 · 10 citations* ## Co-authors here 10 co-authors with a page, the most papers together first. - [Abhinav Khattar](https://md.research.elamur.ai/free/en/authors/abhinav-khattar/) ×3 - [Aleksander Ficek](https://md.research.elamur.ai/free/en/authors/aleksander-ficek/) ×3 - [Aarti Basant](https://md.research.elamur.ai/free/en/authors/aarti-basant/) ×2 - [Adithya Renduchintala](https://md.research.elamur.ai/free/en/authors/adithya-renduchintala/) ×2 - [Alex Kondratenko](https://md.research.elamur.ai/free/en/authors/alex-kondratenko/) ×2 - [NVIDIA: Aaron Blakeman](https://md.research.elamur.ai/free/en/authors/nvidia-aaron-blakeman/) ×2 - [Aaron Grattafiori](https://md.research.elamur.ai/free/en/authors/aaron-grattafiori/) - [Ameya Sunil Mahabaleshwarkar](https://md.research.elamur.ai/free/en/authors/ameya-sunil-mahabaleshwarkar/) - [Andrew Tao](https://md.research.elamur.ai/free/en/authors/andrew-tao/) - [Itay Levy](https://md.research.elamur.ai/free/en/authors/itay-levy/) ## Cited by Papers of our library cite their works 187 times (of the 149,439 papers whose bibliographies we read); [41 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/akhiad-bercovich/cited-by/). Updated 30 October 2026