Jakhongir Saydaliev

I research large language and vision models as an MSc student at EPFL. I’m fortunate to have worked at the NLP, DHLAB, LINX labs and also at SwissAI and Logitech. I have done my Bachelor’s at Politecnico di Torino.

Research Interests

I research building inclusive, multimodal reasoning AI systems that work for everyone. Below are some areas I’ve been working on:

Inclusivity in NLP: I want to bridge the gaps in multilingual NLP & ensure AI benefits linguistically diverse and underrepresented communities
- ConLID: Contrastive language identification for low-resource languages
- Apertus: The first large-scale language model developed in Switzerland
Multimodal Reasoning: Models need to reason across modalities, not just text, to handle real-world scenarios
- Multimodal reasoning: Bounding boxes based multimodal reasoning
- Tool-augmented visual reasoning: Multi-turn VLMs training with RL
- GUI agents: Building autonomous agents for mouse/keyboard operations (ongoing)
Efficient Reasoning: As we scale to multimodal scenarios, we need computationally efficient reasoning to make deployment practical
- Investigating the “overthinking” phenomenon in LLMs (ongoing)

News

Sep 2025	Joined Logitech as an ML Research Intern to work on Computer Use Agents
Jun 2025	Joined SwissAI to work on reasoning for vision language models through reinforcement learning
May 2025	Won the 2nd place in a hackathon on efficient LLM training [code]

Selected Publications

ConLID: Supervised Contrastive Learning for Low-Resource Language Identification

Negar Foroutan^*, Jakhongir Saydaliev^*, Ye Eun Kim, and 1 more author

2025

Under review at EACL 2025; Highest score on the WMDQS Shared Task #2 at COLM 2025

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Language identification (LID) is a critical step in curating multilingual LLM pretraining corpora from web crawls. While many studies on LID model training focus on collecting diverse training data to improve performance, lowresource languages – often limited to single domain data, such as the Bible – continue to perform poorly. To resolve these class imbalance and bias issues, we propose a novel supervised contrastive learning (SCL) approach to learn domain-invariant representations for low-resource languages. Through an extensive analysis, we show that our approach improves LID performance on out-of-domain data for low-resource languages by 3.2%, demonstrating its effectiveness in enhancing LID models.
LLM Agents for Interactive Exploration of Historical Cadastre Data: Framework and Application to Venice

Tristan Karch^*, Jakhongir Saydaliev^*, Isabella Di Lenardo, and 1 more author

Computational Humanities Research, 2025

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Cadastral data reveal key information about the historical organization of cities but are often non-standardized due to diverse formats and human annotations, complicating large-scale analysis. We explore as a case study Venice’s urban history during the critical period from 1740 to 1808, capturing the transition following the fall of the ancient Republic and the Ancien Régime. This era’s complex cadastral data, marked by its volume and lack of uniform structure, presents unique challenges that our approach adeptly navigates, enabling us to generate spatial queries that bridge past and present urban landscapes. We present a text-to-programs framework that leverages Large Language Models (LLMs) to process natural language queries as executable code for analyzing historical cadastral records. Our methodology implements two complementary techniques: a SQL agent for handling structured queries about specific cadastral information, and a coding agent for complex analytical operations requiring custom data manipulation. We propose a taxonomy that classifies historical research questions based on their complexity and analytical requirements, mapping them to the most appropriate technical approach. This framework is supported by an investigation into the execution consistency of the system, alongside a qualitative analysis of the answers it produces. By ensuring interpretability and minimizing hallucination through verifiable program outputs, we demonstrate the system’s effectiveness in reconstructing past population information, property features, and spatiotemporal comparisons in Venice.

Other contributions

Apertus: Democratizing Open and Compliant LLMs for Global Language Environments

Alejandro Hernández-Cano, Alexander Hägele, Allen Hao Huang, and 98 more authors

2025

Contributed through my ConLID project

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We present Apertus, a fully open suite of large language models (LLMs) designed to address two systemic shortcomings in today’s open model ecosystem: data compliance and multilingual representation. Unlike many prior models that release weights without reproducible data pipelines or regard for content-owner rights, Apertus models are pretrained exclusively on openly available data, retroactively respecting this http URL exclusions and filtering for non-permissive, toxic, and personally identifiable content. To mitigate risks of memorization, we adopt the Goldfish objective during pretraining, strongly suppressing verbatim recall of data while retaining downstream task performance. The Apertus models also expand multilingual coverage, training on 15T tokens from over 1800 languages, with 40% of pretraining data allocated to non-English content. Released at 8B and 70B scales, Apertus approaches state-of-the-art results among fully open models on multilingual benchmarks, rivalling or surpassing open-weight counterparts. Beyond model weights, we release all scientific artifacts from our development cycle with a permissive license, including data preparation scripts, checkpoints, evaluation suites, and training code, enabling transparent audit and extension.