2 citations · 3 across the 13 of their papers we have counts for
14 papers
Rank Reversal in Multilingual LLM Judges: A Label-Free Double-Centering Calibrator
Alhasan Mahmood, Samir Abdaljalil, Hasan Kurban
Multilingual LLM judges produce different evaluator-backbone rankings depending on the prompt language: on an eight-language Agent-as-a-Judge benchmark, the top-ranked backbone alt…
ImageEval 2026: Culturally Grounded Arabic Multimodal Evaluation
Samir Abdaljalil, Hunzalah Hassan Bhatti, Ahlam Bashiti +11
We present an overview of the ImageEval 2026 shared task on culturally grounded Arabic multimodal evaluation. It includes two tasks: (i) AynVQA, covering spoken visual question ans…
ConfTriage: A Calibration-Aware LLM Triage Framework for Pulmonary Nodule Malignancy with Selective Specialist Deferral
Md Rabiul Islam, Samir Abdaljalil, Erchin Serpedin +1
Pulmonary nodule malignancy prediction typically depends on image-trained specialist deep learning (DL) models that require substantial annotated imaging data and task-specific tra…
IsoSci: A Benchmark of Isomorphic Cross-Domain Science Problems for Evaluating Reasoning versus Knowledge Retrieval in LLMs
Samir Abdaljalil, Erchin Serpedin, Hasan Kurban
We introduce ISOSCI, a benchmark of isomorphic cross-domain science problem pairs that separates reasoning ability from domain knowledge retrieval in LLM evaluation. Each pair shar…
Multilingual Prompt Localization for Agent-as-a-Judge: Language and Backbone Sensitivity in Requirement-Level Evaluation
Alhasan Mahmood, Samir Abdaljalil, Hasan Kurban
Evaluation language is typically treated as a fixed English default in agentic code benchmarks, yet we show that changing the judge's language can invert backbone rankings. We loca…
4D Synchronized Fields: Motion-Language Gaussian Splatting for Temporal Scene Understanding
Mohamed Rayan Barhdadi, Samir Abdaljalil, Rasul Khanbayov +2
Current 4D representations decouple geometry, motion, and semantics: reconstruction methods discard interpretable motion structure; language-grounded methods attach semantics after…
Knowing When Not to Answer: Abstention-Aware Scientific Reasoning
Samir Abdaljalil, Erchin Serpedin, Hasan Kurban
Large language models are increasingly used to answer and verify scientific claims, yet existing evaluations typically assume that a model must always produce a definitive answer.…
Halluverse-M^3: A multitask multilingual benchmark for hallucination in LLMs
Samir Abdaljalil, Parichit Sharma, Erchin Serpedin +1
Hallucinations in large language models remain a persistent challenge, particularly in multilingual and generative settings where factual consistency is difficult to maintain. Whil…
Audit-of-Understanding: Posterior-Constrained Inference for Mathematical Reasoning in Language Models
Samir Abdaljalil, Erchin Serpedin, Khalid Qaraqe +1
Large language models (LLMs) often generate reasoning traces that appear coherent but rest on unsupported assumptions, leading to hallucinated conclusions. Prior work mainly addres…
Evaluating Multilingual and Code-Switched Alignment in LLMs via Synthetic Natural Language Inference
Samir Abdaljalil, Erchin Serpedin, Khalid Qaraqe +1
Large language models (LLMs) are increasingly applied in multilingual contexts, yet their capacity for consistent, logically grounded alignment across languages remains underexplor…
Theorem-of-Thought: A Multi-Agent Framework for Abductive, Deductive, and Inductive Reasoning in Language Models
Samir Abdaljalil, Hasan Kurban, Khalid Qaraqe +1
Large language models (LLMs) have shown strong performance across natural language reasoning tasks, yet their reasoning processes remain brittle and difficult to interpret. Prompti…
HalluVerse25: Fine-grained Multilingual Benchmark Dataset for LLM Hallucinations
Samir Abdaljalil, Hasan Kurban, Erchin Serpedin
Large Language Models (LLMs) are increasingly used in various contexts, yet remain prone to generating non-factual content, commonly referred to as "hallucinations". The literature…
SINdex: Semantic INconsistency Index for Hallucination Detection in LLMs
Samir Abdaljalil, Hasan Kurban, Parichit Sharma +2
Large language models (LLMs) are increasingly deployed across diverse domains, yet they are prone to generating factually incorrect outputs - commonly known as "hallucinations." Am…
SAFE: A Sparse Autoencoder-Based Framework for Robust Query Enrichment and Hallucination Mitigation in LLMs
Samir Abdaljalil, Filippo Pallucchini, Andrea Seveso +3
Despite the state-of-the-art performance of Large Language Models (LLMs), these models often suffer from hallucinations, which can undermine their performance in critical applicati…