activity
20242026
collaborators

10 papers

cs.CV2026

TimeSpot: Benchmarking Geo-Temporal Understanding in Vision-Language Models in Real-World Settings

Azmine Toushik Wasi, Shahriyar Zaman Ridoy, Koushik Ahamed Tonmoy +5

Geo-temporal understanding, the ability to infer location, time, and contextual properties from visual input alone, underpins applications such as disaster management, traffic plan…

cs.CL2026

Fanar 2.0: Arabic Generative AI Stack

FANAR TEAM, Ummar Abbas, Mohammad Shahmeer Ahmad +34

We present Fanar 2.0, the second generation of Qatar's Arabic-centric Generative AI platform. Sovereignty is a first-class design principle: every component, from data pipelines to…

cs.CL2026

Stop Taking Tokenizers for Granted: They Are Core Design Decisions in Large Language Models

Sawsan Alqahtani, Mir Tafseer Nayeem, Md Tahmid Rahman Laskar +2

Tokenization underlies every large language model, yet it remains an under-theorized and inconsistently designed component. Common subword approaches such as Byte Pair Encoding (BP…

cs.CL2026

MathMist: A Parallel Multilingual Benchmark Dataset for Mathematical Problem Solving and Reasoning

Mahbub E Sobhani, Md. Faiyaz Abdullah Sayeedi, Tasnim Mohiuddin +2

Mathematical reasoning remains one of the most challenging domains for large language models (LLMs), requiring not only linguistic understanding but also structured logical deducti…

cs.CL2025

Ready to Translate, Not to Represent? Bias and Performance Gaps in Multilingual LLMs Across Language Families and Domains

Md. Faiyaz Abdullah Sayeedi, Md. Mahbub Alam, Subhey Sadi Rahman +5

The rise of Large Language Models (LLMs) has redefined Machine Translation (MT), enabling context-aware and fluent translations across hundreds of languages and textual domains. De…

cs.CL2025

Beyond Fertility: Analyzing STRR as a Metric for Multilingual Tokenization Evaluation

Mir Tafseer Nayeem, Sawsan Alqahtani, Md Tahmid Rahman Laskar +2

Tokenization is a crucial but under-evaluated step in large language models (LLMs). The standard metric, fertility (the average number of tokens per word), captures compression eff…