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Aleksander Ficek

Aleksander Ficek has 5 papers in our library, in 2025–2026, most of them on Reasoning, Language models & pretraining, Efficient models & compression; often with Abhinav Khattar, Akhiad Bercovich, Aarti Basant.

5 papers in our library · 2025–2026

OpenAlex · h-index 2 · 19 works · 15 citations OpenAlex, 2026-10-14

📄 5 works in our library · 🔗 Cited 311 times in our library · ⭐ Paper Stars ranking: #6322

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 5 worksComputer Science · 5 worksArtificial Intelligence · 5 worksLanguage and Linguistics · 5 worksComputer Vision and Pattern Recognition · 3 worksComputer Science Applications · 1 workInformation Systems · 1 work

🧭 Topics 3Research subjects and languages the work is about.Reasoning · 3 worksEfficient models & compression · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20261 paper20254 papers

 Papers

The most cited first (OpenAlex and our library).

  1. NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model (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…
  2. Nemotron-H: A Family of Accurate and Efficient Hybrid Mamba-Transformer Models (2025 · 38 citations)
    Self-attention costs grow with sequence length, both in compute per generated token and in KV-cache memory.
  3. OpenCodeReasoning: Advancing Data Distillation for Competitive Coding (2025 · 33 citations)
    Distilling reasoning from large models into smaller ones has strongly improved coding performance. However, the best recipes usually depend on closed datasets or say little about how the data was curated. NVIDIA researchers address this with OpenCodeReasoning, a large open supervised fine-tuning corpus for…
  4. Nemotron 3 Nano: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning (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.
  5. Scaling Test-Time Compute to Achieve IOI Gold Medal with Open-Weight Models (2026 · 1 citation)
    OpenAI and Google DeepMind report gold-medal results at olympiad-level programming contests, but they have disclosed little about how they did it. Open-weight models have lagged behind. NVIDIA researchers present GenCluster, a test-time compute pipeline that aims for the same level openly and reproducibly.

 Co-authors here

12 of 15 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 311 times (of the 149,439 papers whose bibliographies we read); 68 citing papers with a page here →.

Updated

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