Echocardiography analysis demands a dual capability: rigorous quantitative keyframe localization for evidence verification and comprehensive qualitative synthesis for diagnostic reporting. However, current Multi-Modal Large Language Models (MLLMs) struggle to meet these clinical requirements due to a misalignment with diagnostic workflows, a scarcity of video instruction data, and the critical challenge of cyclic temporal ambiguity—where the repetitive nature of cardiac cycles renders standard single-frame supervision ill-posed. To bridge this gap, we introduce EchoMLLM, a unified framework designed for real-world echocardiography video understanding. First, we align model capabilities with clinical needs by defining two fine-grained tasks: cycle- and pathology-conditioned keyframe grounding and video report generation. To facilitate this, we curate EchoMM-120k, a large-scale instruction dataset specifically constructed to support temporal localization and professional reporting. Furthermore, to resolve the cyclic ambiguity, we propose a multi-stage training paradigm incorporating a novel cycle-aware Reinforcement Learning (RL) strategy. By prioritizing logical consistency over rigid index matching, our approach moves beyond rote memorization to elicit invariant reasoning. Extensive experiments demonstrate that EchoMLLM reduces temporal grounding errors by up to 76% and improves report generation quality by 65% over its backbone, achieving state-of-the-art performance against both generalist and medical baselines.

The proliferation of Large Language Models (LLMs) has saturated social media platforms with hyper-realistic posts, rendering traditional detection methods that rely on low-level artifacts or unimodal statistics increasingly ineffective. In this work, we identify a fundamental semantic distinction: humans tend to complement visual content with additional context, while LLMs predominantly describe the visual information. To capture this, UMPIRE employs an orthogonal semantic decomposition mechanism that disentangles textual embeddings into redundant and complementary components. An adaptive gating module dynamically weighs these components to reflect diverse communicative styles. To enforce the desired geometric structure, we introduce a latent contrastive redundancy regularization loss that encourages LLM-generated content to exhibit high semantic redundancy, while human-written content emphasizes complementarity. Experimental results demonstrate that UMPIRE significantly outperforms state-of-the-art detection methods across multiple datasets, achieving up to a 5.38% improvement in accuracy.

Rationalization is a framework that aims to build self-explanatory NLP models by extracting a subset of human-intelligible pieces of their inputting texts. It involves a cooperative game where a selector selects the most human-intelligible parts of the input as the rationale, followed by a predictor that makes predictions based on these selected rationales. Existing literature uses the cross-entropy between the model’s predictions and the ground-truth labels to measure the informativeness of the selected rationales, guiding the selector to choose better ones. In this study, we first theoretically analyze the objective of rationalization by decomposing it into two parts: the model-agnostic informativeness of the rationale candidates and the predictor’s degree of fit. We then provide various empirical evidence to support that, under this framework, the selector tends to sample from a limited small region, causing the predictor to overfit these localized areas. This results in a significant mismatch between the cross-entropy objective and the informativeness of the rationale candidates, leading to suboptimal solutions. To address this issue, we propose a simple yet effective method that introduces random vicinal1 perturbations to the selected rationale candidates. This approach broadens the predictor’s assessment to a vicinity around the selected rationale candidate. Compared to recent competitive methods, our method significantly improves rationale quality (by up to 6.6%) across six widely used classification datasets. The term “vicinal” is borrowed from vicinal risk minimization (Chapelle et al., 2000); “vicinal” means neighboring or adjacent.

The recent advancement of Multimodal Large Language Models (MLLMs) has significantly improved their fine-grained perception of single images and general comprehension across multiple images. However, existing MLLMs still face challenges in achieving precise grounding in complex multi-image scenarios. To address this, we first explore a Chain-of-Thought (CoT) framework that integrates single-image grounding with multi-image comprehension. While partially effective, it remains unstable and struggles to capture abstract visual information due to its non-end-to-end nature. Therefore, we introduce Migician, the first multi-image grounding model capable of performing free-form and accurate grounding across multiple images. To support this, we present the MGrounding-630k dataset, which comprises data for several multi-image grounding tasks derived from existing datasets, along with newly generated free-form grounding instruction-following data. Furthermore, we propose MIG-Bench, a comprehensive benchmark specifically designed for evaluating multi-image grounding capabilities. Experimental results demonstrate that our model achieves significantly superior multi-image grounding capabilities, outperforming the best existing MLLMs by 24.94% and even surpassing much larger 70B models. Our code, model, dataset, and benchmark are fully open-sourced at https://migician-vg.github.io/.

Rationalization is to employ a generator and a predictor to construct a self-explaining NLP model in which the generator selects a subset of human-intelligible pieces of the input text to the following predictor. However, rationalization suffers from two key challenges, i.e., spurious correlation and degeneration, where the predictor overfits the spurious or meaningless pieces solely selected by the not-yet well-trained generator and in turn deteriorates the generator. Although many studies have been proposed to address the two challenges, they are usually designed separately and do not take both of them into account. In this paper, we propose a simple yet effective method named MGR to simultaneously solve the two problems. The key idea of MGR is to employ multiple generators such that the occurrence stability of real pieces is improved and more meaningful pieces are delivered to the predictor. Empirically, we show that MGR improves the F1 score by up to 20.9% as compared to state-of-the-art methods.