The recent development of Multimodal Large Language Models (MLLMs) has significantly advanced AI’s ability to understand visual modalities. However, existing evaluation benchmarks remain limited to single-turn question answering, overlooking the complexity of multi-turn dialogues in real-world scenarios. To bridge this gap, we introduce MT-Video-Bench, a holistic video understanding benchmark for evaluating MLLMs in multi-turn dialogues. Specifically, our MT-Video-Bench mainly assesses six core competencies that focus on perceptivity and interactivity, encompassing 1,000 meticulously curated multi-turn dialogues from diverse domains. These capabilities are rigorously aligned with real-world applications, such as interactive sports analysis and multi-turn video-based intelligent tutoring. With MT-Video-Bench, we extensively evaluate various state-of-the-art open-source and closed-source MLLMs, revealing their significant performance discrepancies and limitations in handling multi-turn video dialogues. The benchmark will be publicly available to foster future research.

Tabular data is a fundamental component of real-world information systems. However, existing multilingual table benchmarks suffer from geolinguistic imbalance - overrepresenting certain languages and lacking sufficient scale for rigorous cross-lingual analysis. To address these limitations, we introduce M3TQA, which is a comprehensive framework for massively multilingual multitask table question answering, including subsequent datasets M3TQA-BENCH and M3TQA-INSTRUCT, featuring tables expanded to 97 languages from Chinese and English sources. M3TQA-BENCH includes 6,606 professionally annotated question-answering pairs across four tasks designed to evaluate nuanced table reasoning capabilities. Additionally, we synthesized the training set M3TQA-INSTRUCT in 97 languages using Large Language Model (LLM). Experiments on state-of-the-art LLMs reveal critical insights into cross-lingual generalization, demonstrating that synthetically generated, unannotated training data can significantly boost performance, particularly for low-resource languages. M3TQA establishes a new standard for multilingual table understanding, providing both a challenging evaluation platform and a scalable methodology for future research.

Long-video Omni assistants often fail not by inventing content, but by misbinding real evidence: they hear the right utterance and see the right event, yet attach it to the wrong speaker, moment, or modality. These almost-true errors evade standard video QA because local evidence remains valid, so item-level scoring can reward both a supported claim and its near-counterfactual. We introduce a counterfactual event-binding protocol that constructs paired supported/counterfactual claims from the same audio-visual event evidence and evaluates them by strict-pair accuracy. We instantiate it as OmniHalluc-L, to our knowledge the first benchmark for long-video Omni hallucination, with 3600 single-claim QA items from 638 long-form videos averaging 24.16 minutes and covering 256.87 hours. Under this protocol, open-weight Omni models remain weak at pair-level binding: Qwen2.5-Omni-7B reaches 32.06% and Qwen3-Omni-Instruct reaches 41.55%, versus 76.54% for a closed-source reference. To narrow this gap, we propose MPRC (Modality-Perturbation Reliability Calibration), a frozen-backbone calibrator that combines native confidence with response shifts from selected audio-negative probes. MPRC raises these two models to 36.22% and 51.09%, respectively, and improves target-adapted MCQ accuracy on OmniVideoBench and WorldSense. The code is available at https://github.com/ZexDong/omnihallucL-mprc.

Deep Research Agents (DRAs) aim to solve complex, long-horizon research tasks involving planning, retrieval, multimodal understanding, and report generation, yet their evaluation remains challenging due to dynamic web environments and ambiguous task definitions. We propose **DR³-Eval**, a realistic and reproducible benchmark for evaluating deep research agents on multimodal, multi-file report generation. DR³-Eval is constructed from authentic user-provided materials and paired with a per-task static research sandbox corpus that simulates open-web complexity while remaining fully verifiable, containing supportive documents, distractors, and noise. Moreover, we introduce a multi-dimensional evaluation framework measuring Information Recall, Factual Accuracy, Citation Coverage, Instruction Following, and Depth Quality, and validate its alignment with human judgments. Experiments with our developed multi-agent system DR³-Agent based on multiple state-of-the-art language models demonstrate that DR³-Eval is highly challenging and reveals critical failure modes in retrieval robustness and hallucination control.

Omni-modal video captioning is not merely combining visual captioning with audio transcription: a useful caption must describe how visual actions, speech, music, and sound effects co-evolve over time. Existing large multimodal models often fail at this relational step, treating audio and visual streams as loosely coupled observations, relying heavily on automatic speech recognition, and under-specifying non-speech sounds and their links to visual events. We present AVSCap, a framework for audio-visual captioning centered on explicit cross-modal event binding. First, we construct AVSCap-130K, a tri-modal training corpus generated by a decoupled-then-fused pipeline that anchors visual and acoustic evidence independently before composing temporally grounded omni-modal captions. Second, we train AVSCap-7B, a 7B captioner with a two-stage strategy: supervised fine-tuning establishes baseline captioning ability, while a sample-efficient reinforcement learning stage uses hybrid rewards to optimize acoustic completeness and audio-visual synergy. Our scaling analysis shows that reinforcement learning brings larger gains than simply increasing SFT data. Third, we introduce AVSCapBench, a human-curated benchmark that decomposes captions into visual, audio, and synergy events and evaluates them with fine-grained event recall. Experiments on AVSCapBench and external benchmarks show that AVSCap-7B improves non-speech audio coverage and cross-modal binding, delivering the best overall performance among evaluated open-source models.