activity
20242026
collaborators

7 papers

cs.CV2026

Simple Domain Generalization for Strong Pixel-Level Image Tampering Detection in Modern VLMs

Yi Tang, Xinyi Shang, Jiacheng Cui +12

Modern vision-language models (VLMs) have significantly improved image generation and editing capabilities, making pixel-level image tampering detection increasingly important yet…

cs.CL2026

Operation-Guided Progressive Human-to-AI Text Transformation Benchmark for Multi-Granularity AI-Text Detection

Sondos Mahmoud Bsharat, Jiacheng Liu, Xiaohan Zhao +9

As AI writing assistants become increasingly integrated into real-world drafting and revision workflows, many documents are no longer purely human-written or AI-generated, but inst…

cs.CV2026

From Masks to Pixels and Meaning: A New Taxonomy, Benchmark, and Metrics for VLM Image Tampering

Xinyi Shang, Yi Tang, Jiacheng Cui +9

Existing tampering detection benchmarks largely rely on object masks, which severely misalign with the true edit signal: many pixels inside a mask are untouched or only trivially m…

cs.CL2025

Prompting Test-Time Scaling Is A Strong LLM Reasoning Data Augmentation

Sondos Mahmoud Bsharat, Zhiqiang Shen

Large language models (LLMs) have demonstrated impressive reasoning capabilities when provided with chain-of-thought exemplars, but curating large reasoning datasets remains labori…

cs.CL2025

DRAG: Distilling RAG for SLMs from LLMs to Transfer Knowledge and Mitigate Hallucination via Evidence and Graph-based Distillation

Jennifer Chen, Aidar Myrzakhan, Yaxin Luo +3

Retrieval-Augmented Generation (RAG) methods have proven highly effective for tasks requiring factual consistency and robust knowledge retrieval. However, large-scale RAG systems c…

cs.CL2025

Mobile-MMLU: A Mobile Intelligence Language Understanding Benchmark

Sondos Mahmoud Bsharat, Mukul Ranjan, Aidar Myrzakhan +6

Rapid advancements in large language models (LLMs) have increased interest in deploying them on mobile devices for on-device AI applications. Mobile users interact differently with…