Recent advances in Large Vision-Language Models (LVLMs) have enabled general-purpose vision tasks through visual instruction tuning. While existing LVLMs can generate segmentation masks from text prompts for single images, they struggle with segmentation-grounded reasoning across images, especially at finer granularities such as object parts. In this paper, we introduce the new task of part-focused semantic co-segmentation, which involves identifying and segmenting common objects and their constituent common and unique parts across images. To address this task, we present Calico, the first LVLM designed for multi-image part-level reasoning segmentation. Calico features two key components, a novel Correspondence Extraction Module that identifies semantic part-level correspondences, and Correspondence Adaptation Modules that embed this information into the LVLM to facilitate multi-image understanding in a parameter-efficient manner. To support training and evaluation, we curate MixedParts, a large-scale multi-image segmentation dataset containing ∼2.4M samples across ∼44K images spanning diverse object and part categories. Experimental results demonstrate that Calico, with just 0.3% of its parameters finetuned, achieves strong performance on this challenging task.

We introduce PurpCode, the first post-training recipe for training safe code reasoning models towards generating secure code and defending against malicious cyberactivities. PurpCode trains a reasoning model in two stages: (i) Rule Learning, which explicitly teaches the model to reference cybersafety rules to generate vulnerability-free code and to avoid facilitating malicious cyberactivities; and (ii) Reinforcement Learning, which optimizes model safety and preserves model utility through diverse, multi-objective reward mechanisms. To empower the training pipelines with comprehensive cybersafety data, we conduct internal red-teaming to synthesize comprehensive and high-coverage prompts based on real-world tasks for inducing unsafe cyberactivities in the model. Based on PurpCode, we develop a reasoning-based coding model, namely PurpCode-32B, which demonstrates state-of-the-art cybersafety, outperforming various frontier models. Moreover, our alignment method decreases the model overrefusal rates in both general and cybersafety-specific scenarios, while preserving model utility in both code generation and common security knowledge.

The United States (U.S.) military withdrawal from Afghanistan in August 2021 was met with turmoil as the Taliban regained control of most of the country, including Kabul. These events have affected many and were widely discussed on social media, especially in the U.S. In this work, we focus on Twitter discourse regarding these events, especially potential opinion shifts over time and the effect social media posts by established U.S. legislators might have had on online public reception. To this end, we investigate two datasets on the war in Afghanistan, consisting of Twitter posts by self-identified U.S. accounts and conversation threads initiated by U.S. politicians. We find that Twitter users' discussions revolve around the Kabul airport event, President Biden's handling of the situation, and people affected by the U.S. withdrawal. Microframe analysis indicates that discourse centers the humanitarianism underlying these occurrences and politically leans liberal, focusing on care and fairness. Lastly, network analysis shows that Republicans are far more active on Twitter compared to Democrats and there is more positive sentiment than negative in their conversations.