This paper investigates how to effectively utilize Vision-Language Models (VLMs) for image forgery detection and localization (IFDL). It introduces IFDL-VLM, a novel two-stage pipeline that decouples the core IFDL task from VLM-based explanation generation, leveraging localization masks to significantly enhance VLM interpretability and achieving state-of-the-art results across multiple benchmarks.

This paper investigates the effectiveness of Explainable Artificial Intelligence (XAI) methods in making Vision-Language Models (VLMs), specifically CLIP, interpretable within art-historical contexts. It evaluates seven XAI methods through zero-shot localization experiments and human interpretability studies, concluding that their effectiveness depends on the conceptual stability and representational availability of the examined categories.

This paper introduces Chameleon, an adaptive adversarial framework that exploits image downscaling vulnerabilities in Vision-Language Models (VLMs) to inject hidden malicious visual prompts. By employing an iterative, feedback-driven optimization mechanism, Chameleon can craft imperceptible perturbations that hijack VLM execution and compromise agentic decision-making systems.

This paper introduces MemLoRA, a novel memory system that enables efficient, on-device deployment of memory-augmented language models by equipping small models with specialized LoRA adapters. It also presents MemLoRA-V, extending these capabilities to multimodal contexts with native visual understanding.

This paper introduces Stage-Aware Reinforcement (StARe), a novel module that decomposes long-horizon robotic manipulation tasks into semantically meaningful stages, providing dense, interpretable reinforcement signals. Integrated into the Imitation → Preference → Interaction (IPI) fine-tuning pipeline, StARe significantly improves the performance and robustness of Vision-Language-Action (VLA) models on complex manipulation tasks.

This paper introduces SpaceTools, a vision-language model trained with Double Interactive Reinforcement Learning (DIRL) to achieve precise spatial reasoning and real-world robot manipulation by effectively coordinating multiple external tools. It demonstrates state-of-the-art performance on various spatial understanding benchmarks.

AdaptVision introduces an efficient VLM paradigm that autonomously determines the minimum number of visual tokens required for each sample by employing a coarse-to-fine visual acquisition strategy, leading to superior performance with significantly reduced computational overhead.

DeepSeek-OCR explores the innovative approach of compressing long text contexts into visual tokens to overcome computational challenges in Large Language Models. This vision-language model, featuring a novel DeepEncoder and DeepSeek3B-MoE decoder, demonstrates impressive OCR precision with significant compression ratios and state-of-the-art performance on document parsing benchmarks.