2 citations · 4 across the 15 of their papers we have counts for
19 papers
Certified Interface Aliases: Exact Collisions in Vision-Language Preprocessing, and When They Exist
Mert Onur Cakiroglu, Elham Buxton, Mehmet Dalkilic +1
Vision-language verifiers and routers must distinguish errors repairable by more reasoning from those caused by visual evidence never reaching the language model. This distinction…
Oracle Gaps in Reliability Coverage: Sampling Noise or Policy Specialization?
Mert Onur Cakiroglu, Mehmet Dalkilic, Hasan Kurban
Policies trained from the same base model can appear to solve different problems. An oracle that chooses the best policy for each problem may therefore appear much stronger than an…
When Does Retrieval Help Time-Series Forecasting?
Mert Onur Cakiroglu, Elham Buxton, Mehmet Dalkilic +1
Retrieval plug-ins supply a deep forecaster with information its lookback window cannot carry. Published evaluations report consistent gains, and each credits its own mechanism. We…
Detect Early, Escalate Rarely: Anytime Detection of AI-Generated Video from the Compressed Bitstream
Mert Onur Cakiroglu, Mehmet Dalkilic, Hasan Kurban
Detectors for AI-generated video are evaluated offline. A clip is decoded to pixels and scored once, increasingly by a large vision-language model. Detection, however, is deployed…
The Spectrum Is Not Enough: When Context Helps Time-Series Forecasting
Mert Onur Cakiroglu, Mehmet Dalkilic, Hasan Kurban
A growing family of indices scores how predictable a series is from its spectrum. Practitioners increasingly read these scores as answering a different question: whether \emph{addi…
Auditing Generalization in AI-Generated Video Detection: A Six-Control Protocol and the VidAudit Toolkit
Mert Onur Cakiroglu, Zhihe Lu, Mehmet Dalkilic +1
AI-generated video detection benchmarks such as GenVidBench and AIGVDBench are the de facto leaderboards, yet most evaluation protocols leave uncontrolled confounds that can inflat…
Topical Phase Transitions in Artificial Intelligence Research: Large-Scale Evidence and an Early-Warning Signature for Emerging Topics
Rasul Khanbayov, Hasan Kurban
Do research topics in artificial intelligence grow gradually, or do they advance through abrupt, detectable jumps? Analyzing 80,814 accepted main-track papers from five premier AI…
LGQ: Learnable Geometric Quantization for Image Tokenization
Idil Bilge Altun, Mert Onur Cakiroglu, Elham Buxton +2
Recent collapse-free quantizers such as FSQ achieve stable training by replacing the learnable codebook with an engineered geometry: a fixed scalar grid whose structure is dictated…
Temporal Realism Evaluation of Generated Videos Using Compressed-Domain Motion Vectors
Mert Onur Cakiroglu, Idil Bilge Altun, Zhihe Lu +2
Temporal realism remains a central weakness of current generative video models, as most evaluation metrics prioritize spatial appearance and offer limited sensitivity to motion. We…
Geometric-k-means: A Bound Free Approach to Fast and Eco-Friendly k-means
Parichit Sharma, Marcin Stanislaw, Hasan Kurban +2
This paper introduces Geometric-k-means (or Gk-means for short), a novel approach that significantly enhances the efficiency and energy economy of the widely utilized k-means algor…
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…
Beyond Atomic Geometry Representations in Materials Science: A Human-in-the-Loop Multimodal Framework
Can Polat, Erchin Serpedin, Mustafa Kurban +1
Most materials science datasets are limited to atomic geometries (e.g., XYZ files), restricting their utility for multimodal learning and comprehensive data-centric analysis. These…
xChemAgents: Agentic AI for Explainable Quantum Chemistry
Can Polat, Mehmet Tuncel, Mustafa Kurban +2
Recent progress in multimodal graph neural networks has demonstrated that augmenting atomic XYZ geometries with textual chemical descriptors can enhance predictive accuracy across…
Stress-Testing Multimodal Foundation Models for Crystallographic Reasoning
Can Polat, Hasan Kurban, Erchin Serpedin +1
Evaluating foundation models for crystallographic reasoning requires benchmarks that isolate generalization behavior while enforcing physical constraints. This work introduces a mu…
Understanding the Capabilities of Molecular Graph Neural Networks in Materials Science Through Multimodal Learning and Physical Context Encoding
Can Polat, Hasan Kurban, Erchin Serpedin +1
Molecular graph neural networks (GNNs) often focus exclusively on XYZ-based geometric representations and thus overlook valuable chemical context available in public databases like…
Multivariate de Bruijn Graphs: A Symbolic Graph Framework for Time Series Forecasting
Mert Onur Cakiroglu, Idil Bilge Altun, Mehmet Dalkilic +2
Time series forecasting remains a challenging task for foundation models due to temporal heterogeneity, high dimensionality, and the lack of inherent symbolic structure. In this wo…
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…