Community-based platforms like Stack Overflow (SO) offer a vast and diverse source of software development knowledge, combining natural language data with code snippets. Resources built from SO have been widely used to support downstream tasks in software engineering and natural language processing. However, no existing resource fully reconstructs and connects the complete range of information available on SO, leveraging its structure. We introduce Stack2Graph, a large-scale resource that preserves the forum’s structural relationships in a semantically explicit form by combining a knowledge graph with a vector database. This hybrid design captures the intrinsic links between questions, answers, comments, tags, and cross-references, bridging symbolic and vector-based representations to enable structured and multi-hop retrieval. The goal is to make SO knowledge more efficiently accessible for LLM-based systems and easier to integrate into downstream applications. To evaluate its impact, we integrate Stack2Graph into a zero-shot pipeline for multiple-choice question answering on CodeMMLU. Results show that retrieval augmentation particularly benefits mid-sized general-purpose models, with substantial gains in API- and framework-oriented tasks.

Charts, where information is delivered holistically by visual and textual features, represent a challenge when it comes to downstream tasks such as chart question answering, where both kinds of information contribute to the task. The standard approach is to decouple the task in two steps, first extracting information from the charts, or representing it as a table, text or code, and then a second reasoning step to output the answers. Today, the advancements in visual encoding of Visual Large Language Models (VLLM) have shown their capabilities to solve such complex tasks without using in-between representations of the charts or massive in-domain training. Our new instruction fine-tuned and chain-of-thought model QwenChart showed that even in a complex new benchmark such as SciVQA general models can achieve great performances with low-cost training, matching the capabilities that LLMs have showed in unimodal downstream tasks. An out-of-domain evaluation showed satisfactory results, albeit with an expected drop in performance.

Video question answering (VQA) is a challenging task that requires models to generate answers by using both information from text and video. We present Task-oriented Human Attention Video Question Answering (THAVQA), a new VQA dataset consisting of third- and first- person videos of an instructor using a sewing machine. The sewing task is formalized step-by-step in a script: each step consists of a video annotated with German language open-ended question and answer (QA) pairs and with human visual attention. The paper also includes a first assessment of the performance of a pre-trained Multimodal Large Language Model (MLLM) in generating answers to the questions of our dataset across different experimental settings.Results show that our task-oriented dataset is challenging for pre-trained models. Specifically, the model struggles to answer questions requiring technical knowledge or spatio-temporal reasoning.