YerevaNN

13/08/2026

Can we improve Open Street Maps based on RF / 5G data? We just published a paper in the Pervasive and Mobile Computing journal.

💡Our new study introduces MapRadioFormer (Map-RF), a simple vision transformer model that combines imperfect building maps with radio-frequency measurements from wireless links.

📖 Open-source maps can contain missing, shifted, or oversimplified building footprints. RF signals carry complementary information about the physical environment, including structures that may be absent or inaccurately represented in a map. We trained a model that combines map data with RF signals and could improve mapping accuracy from 40.1% to 65.3% IoU in the synthetic evaluation. We also tried a real-world evaluation. We took real RF measurements from Oslo, took Oslos’ buildings from OpenStreetMaps, artificially corrupted the map to reach 39.9% IoU, and then used our fine-tuned model to improve the map. We reached 64.9% IoU!

🔎 The findings are promising, but real-world deployment will require larger field datasets, direct evaluation with real mapping errors, and further testing across cities and environments.

Read more: https://lnkd.in/d2iPA6XV

Rafayel Mkrtchyan, Armen Manukyan, Theofanis Raptis, Hrant Khachatrian
ԵՊՀ - Yerevan State University | Institute of Informatics and Telematic, National Research Council of Italy

09/06/2026

The results are in! A huge congratulations to Arman Karapetyan for winning the Armenian track of the OpenAI Parameter Golf competition!

Building the best language model under strict constraints, including training in under 10 minutes on 8xH100 GPUs, is an incredible challenge. Arman demonstrated exceptional skill and creativity with his 11L TokenModel architecture, securing the top spot on our local leaderboard with a winning adjusted BPB score of 0.4101. You can view the full final standings here: http://139.59.213.111/armweb/

A special thank you to Eleveight AI for generously funding the competition and for their dedication to empowering our local talent. With their new NVIDIA B300 hardware already on-site in Gagarin, the future of our research capabilities in the region is looking incredibly bright! 👏

A huge thanks to the COPA | Common Operational Picture Analytics, for providing datasets (to be released soon) where all final evaluations were seamlessly hosted and measured. Thank you for providing such a robust platform for this initiative!

Initiatives like this are essential for motivating our students and engineers to take the leap and dive deeper into AI research. This competition wouldn't have been possible without our partners coming together to support the ecosystem.

We also want to extend our gratitude to ԵՊՀ - Yerevan State University for opening up the supercomputer cluster and providing the essential GPU-hours that allowed our participants to experiment and compete. In total, 730 H100-hours were used by the contest participants.

Thank you to all 7 finalists who spent time tuning and submitting a model. We look forward to seeing what you build next!

27/04/2026

On the last day of ICLR workshops we have four posters.

1. At GEMBio Workshop Tatevik Abrahamyan will present how we benchmarked molecular optimization algorithms against three categories molecular design tasks, including a novel one (for this line of research) that tests whether AI models can generate molecules with selective binding to two proteins (or pockets).

PMO-Dock: Benchmarking Docking, Specificity, and Generalization in Molecular Optimization

2. At FM4Science Workshop Filya Geikyan will present our new tokenizer for 3D molecular structures. It is an attempt to make autoregressive models competitive with diffusion or flow-match-based methods for 3D conformer generation.

CoordToken: Accurate Tokenization of 3D Small Organic Molecules

Then we have two more posters at Machine Learning for Remote Sensing workshop:

3. Ani Vanyan will present our new benchmark for cross-satellite generalization of remote sensing foundation models:

GeoCrossBench: Cross-Band Generalization for Remote Sensing

4. Anna Khosrovyan will present how we should evaluate "face recognition" for vessels in satellite imagery.

HOSS-Bench: Open-Set Cross-Modal Vessel Re-Identification Benchmark

See you there!

24/04/2026

We are happy to announce that six of our team members are in Rio de Janeiro this week for ICLR 2026.

Our lab will be presenting seven posters across various workshops, highlighting our latest research in foundation models, molecular optimization, and remote sensing. If you’re attending the conference, we’d love to connect and discuss potential collaborations!

📅 Sunday, April 26
1. PMO-Dock: Benchmarking Docking, Specificity, and Generalization in Molecular Optimization | GEM Workshop
2. Beyond Data Size: Impact of Dataset Diversity and Density in Self-Distillation | DATA-FM Workshop
3. Bridging the Sim-to-Real Gap: RF Localization with Large-Scale Synthetic Pretraining | DATA-FM Workshop
4. Aerial Vision-Language Navigation: Enhancing Navigation with Map Grounding | ES-Reasoning Workshop

📅 Monday, April 27
5. CoordToken: Accurate Tokenization of 3D Small Organic Molecules | FM4Science Workshop
6. GeoCrossBench: Cross-Band Generalization for Remote Sensing | ML4RS Workshop
7. HOSS-Bench: Open-Set Cross-Modal Vessel Re-Identification Benchmark | ML4RS Workshop

Anna Khosrovyan, Ani Vanyan, Narek Nurijanyan, Armen Manukyan, Filya Geikyan, Tatevik Abrahamyan

23/04/2026

🔊 We are happy to announce the third edition of the Armenia LLM Summer School!
📅 We are returning to Yerevan from August 3–7, 2026, and we’re excited to be hosted by our partner, the AI9 Startup Campus.
What’s on the Agenda?
▫️Day 1: Pre-training, Scaling Laws & MoE (with Andre Martins, IST / Unbabel)
▫️Day 2: SFT & Reinforcement Learning (Speaker TBC)
▫️Day 3: Test-time Scaling & Compute-optimality (with Ivan Mashkov, NVIDIA)
▫️Day 4: Agents, Reasoning & Workflows (Speaker TBC)
▫️Day 5: Advanced Topics: VLA, Robotics & World Models (Speaker TBC)

We cap off the week on August 8th with an intensive 24-hour Hackathon! This will be organized by AI9, with Armenia LLM Summer School organizing committee serving as the content partner. The Hackathon will have separate application form and will be open to both the school attendees and other applicants.

Join us in person in Yerevan or via our online track for theoretical sessions.
✅ Apply now: armllm.github.io/2026

Special thanks to the AI9 Startup Campus for being a partner for this year's school as well!

25/03/2026

ԵՊՀ - Yerevan State University, YerevaNN and Eleveight AI invite AI engineers, students and researchers from Armenia to participate in OpenAI Parameter Golf!

The goal is to train the best language model that fits in 16MB and runs in under 10 minutes on 8×H100 GPUs.

Yerevan State University will provide compute on its cluster for participants from Armenia, sponsored by Eleveight AI. Eleveight AI will also award a 500,000 AMD prize for the best submission from Armenia.

Because of how Parameter Golf is designed, it is not trivial to determine the best submission among participants from Armenia. We believe we found a fair approach, please see https://github.com/YerevaNN/Parameter-Golf-Armenia/ for details.

To apply for 20 GPU-hours on the YSU cluster for your Parameter Golf submission, use this form: https://forms.gle/RGGjcAhCsBGY4NjY9

We wish you every success in making it onto the OpenAI leaderboard!

24/03/2026

Lots of news coming out of the lab over the next days. Stay tuned!

09/03/2026

We are happy to co-organize the 4th edition of the AI Conf Armenia along with the ԵՊՀ - Yerevan State University, Union of Advanced Technology Enterprises-UATE and Krisp.

Stay tuned for more details on agenda, speakers, participants, and more!

🤖🇦🇲 AI Conf Armenia-ն՝ արհեստական բանականությանը նվիրված հայկական խոշորագույն համաժողովը, վերադառնում է։

Գալիք AI Conf-ը՝ ոչ թե պարզապես 4-րդ, ոչ թե քառապատկված,
այլ 4-րդ աստիճանում` ճիշտ համահունչ ոլորտի էքսպոնենցիալ զարգացմանը։ 📈

📅 Ապրիլի 18-ին հավաքվում ենք Մայր ԲՈւՀ-ի հարկի տակ՝ վերաիմաստավորելու ԱԲ ոլորտի աճը և նախանշելու ապագայի ուրվագիծը։

🎯 Առանցքում՝ GPU-ներով մասշտաբավարվելու և երկիրը մասշտաբավորելու հեռանկարը։

🤝 Կազմակերպիչներ՝

ԵՊՀ - Yerevan State University, Krisp, YerevaNN, Union of Advanced Technology Enterprises-UATE

02/01/2026

We finished 2025 with a fresh publication on AI applications in the RF domain.

A mobile device can be localized in an urban environment by measuring signal strength from various base stations in the city. The most straightforward approach is to collect fingerprints on the streets and then, for a given device, compute a similar fingerprint and match it to the nearest neighbor.

In this paper, we ask whether this approach can be extended to streets with no prior fingerprints. Deep learning methods can fuse urban maps, radio signals, and base station locations to predict the device location. However, the error remains astonishingly high, given the scarcity of real-world datasets.

We used NVIDIA Sionna to simulate a large number of RF signals in an environment modeled after the city of Rome and showed that pretraining on synthetic data can reduce the localization error by 50%.

Rafayel Mkrtchyan Armen Manukyan Ararat Saribekyan Theofanis Raptis Hrant Khachatrian

We wish you a productive new year!

Link to the paper: https://www.sciencedirect.com/science/article/pii/S1566253525011662

Link to the code: https://github.com/YerevaNN/RF-Loc-Sim2Real