6 papers
MemBoost: A Memory-Boosted Framework for Cost-Aware LLM Inference
Joris Köster, Zixuan Liu, Siavash Khajavi +1
Large Language Models (LLMs) deliver strong performance but incur high inference cost in real-world services, especially under workloads with repeated or near-duplicate queries acr…
Digital Twin and Agentic AI for Wild Fire Disaster Management: Intelligent Virtual Situation Room
Mohammad Morsali, Siavash H. Khajavi
According to the United Nations, wildfire frequency and intensity are projected to increase by approximately 14% by 2030 and 30% by 2050 due to global warming, posing critical thre…
Targeting Misalignment: A Conflict-Aware Framework for Reward-Model-based LLM Alignment
Zixuan Liu, Siavash H. Khajavi, Guangkai Jiang +1
Reward-model-based fine-tuning is a central paradigm in aligning Large Language Models with human preferences. However, such approaches critically rely on the assumption that proxy…
DetectiumFire: A Comprehensive Multi-modal Dataset Bridging Vision and Language for Fire Understanding
Zixuan Liu, Siavash H. Khajavi, Guangkai Jiang
Recent advances in multi-modal models have demonstrated strong performance in tasks such as image generation and reasoning. However, applying these models to the fire domain remain…
RGB-Th-Bench: A Dense benchmark for Visual-Thermal Understanding of Vision Language Models
Mehdi Moshtaghi, Siavash H. Khajavi, Joni Pajarinen
We introduce RGB-Th-Bench, the first benchmark designed to evaluate the ability of Vision-Language Models (VLMs) to comprehend RGB-Thermal image pairs. While VLMs have demonstrated…
Synthetic imagery for fuzzy object detection: A comparative study
Siavash H. Khajavi, Mehdi Moshtaghi, Dikai Yu +3
The fuzzy object detection is a challenging field of research in computer vision (CV). Distinguishing between fuzzy and non-fuzzy object detection in CV is important. Fuzzy objects…