Advanced chart question answering requires both precise perception of small visual elements and multi-step reasoning across several subplots. While existing MLLMs are strong at understanding single plots, they often struggle with multi-step reasoning across multiple subplots. We propose HierVA, a hierarchical visual agent framework for chart reasoning that iteratively constructs and updates a working context in a joint image–text space. A high-level manager generates plans and maintains a compact context containing only key information, while specialized sub-agents perform reasoning, gather evidence, and return results. In particular, the agent maintains separate visual and textual contexts, using a zoom-in tool to restrict the visual context. Experiments on the chart reasoning benchmarks demonstrate consistent improvements over strong multimodal baselines, and ablation studies verify that hierarchical architecture, limited visual context, and distilled context contribute complementary gains.

The recent surge of interest in unified Multimodal Large Language Models (MLLMs) has catalyzed rapid progress toward general-purpose generation and understanding across different modalities. Despite the remarkable advancements, the field lacks a systematic and cohesive framework that connects these developments, revisits the motivations, and situates current trends within a broader landscape. In this survey, we present a comprehensive and in-depth review of unified MLLMs, offering both a methodology taxonomy and unique perspectives on the field. We begin by outlining the foundational concepts and prerequisites for understanding unified MLLMs. We then delve into designs from different aspects, including model architectures, loss functions, alignment techniques, and different representation strategies. Furthermore, we discuss persistent challenges and identify promising directions for future research. By bridging scattered progress and providing a consolidated view, this survey aims to foster a deeper and systematical understanding of unified MLLMs and inspire future innovations in building truly general multimodal intelligence.

A reliable resume-job matching system helps a company recommend suitable candidates from a pool of resumes and helps a job seeker find relevant jobs from a list of job posts. However, since job seekers apply only to a few jobs, interaction labels in resume-job datasets are sparse. We introduce ConFit v2, an improvement over ConFit to tackle this sparsity problem. We propose two techniques to enhance the encoder’s contrastive training process: augmenting job data with hypothetical reference resume generated by a large language model; and creating high-quality hard negatives from unlabeled resume/job pairs using a novel hard-negative mining strategy. This method also simplifies the representation space of the encoder. We evaluate ConFit v2 on two real-world datasets and demonstrate that it outperforms ConFit and prior methods (including BM25 and OpenAI text-embedding-003), achieving an average absolute improvement of 13.8% in recall and 17.5% in nDCG across job-ranking and resume-ranking tasks.