This work presents MMOOC, a large-scale benchmark for evaluating refusal and robust answering abilities of MLLMs, and introduces an LLM-as-a-Judge metric to assess the correctness of model reasoning.
Abstract
Multimodal Large Language Models (MLLMs) have achieved strong performance on a wide range of vision-language tasks, but often fail under imperfect or shifted contexts. A reliable MLLM should refuse truly out-of-context (OOC) questions with subject-level context shifts while still answering shifted in-context (Shifted IC) questions with non-subject context shifts. Existing benchmarks mainly target OOC or visually unanswerable questions, but overlook answerable Shifted IC cases and cover limited OOC shifts. To fill this gap, we present MMOOC, a large-scale benchmark for evaluating refusal and robust answering abilities of MLLMs. MMOOC contains over 41K image-question pairs, including answerable Shifted IC cases and unanswerable OOC cases, spanning three question formats, eight shift types and six visual scenarios, with data quality ensured through MLLM-based filtering and human verification. We evaluate model responses using Accuracy and Refusal Rate, and further introduce an LLM-as-a-Judge metric to assess the correctness of model reasoning. Experiments on diverse MLLMs show that current models still struggle to balance answer-ability and refusal under shifted contexts. We further analyze key failure patterns and show that post-training can improve robustness. MMOOC will be made publicly available.
MGAL is the first multilingual, granularity- and position-aware long-context benchmark, constructed from United Nations reports spanning 8K to 128K tokens across the six official UN languages, and finds that LLMs perform well at word-level tasks but struggle with coarser-grained ones.
Chunhan Li, Chenglin Xu, Zongyang Zhang et al.· 0 citations
Multi-modal Large Language Models (MLLMs) have made substantial advances on benchmarks, yet their real-world effectiveness remains uncertain. This gap stems from the fundamental misalignment between benchmarks in controlled, static settings and the dynamic, interactive, and contextualized nature of real-world applications. To bridge this gap, we propose CEDI (Contextualized Evaluations of MLLMs through Dynamic, multi-round Interactions), a framework that recasts evaluation as a three-party interaction between an evaluatee model, an automated examiner, and a grader. The examiner conducts multi-turn, semi-structured conversation guided by a graph-based representation of the task. By navigating state-space transitions, CEDI deploys diverse strategies, from clarification requests to adversarial probes, to elicit performance evidence. We apply CEDI to visual hallucinations. Empirical results across multiple models, diverse settings, datasets, and domains show that contextualized, interactive evaluations reveal not only significantly more hallucinations than conventional static evaluation but also ones that more closely resemble those arising in practical use cases. We further show that hallucinations often accumulate over long contexts, through self-reinforcing dialogue history, and models are particularly vulnerable to questions requiring premise rejection or refusal. Together, these findings highlight CEDI as a step toward realistic, systematic, and ecologically valid assessments of MLLMs'capabilities. Code is available at github.com/williamium3000/cedi.
Yijiang Li, Huiqi Zou, Bingyang Wang et al.· 0 citations
AutoDavis is introduced, a first-of-its-kind automatic and dynamic evaluation protocol that enables on-demand benchmarking of LVLMs across specific capability dimensions and shows effectiveness and reliability, offering a new paradigm for dynamic benchmarking of multimodal intelligence.
Han Bao, Yue Huang, Yanbo Wang et al.· Proceedings of the 32nd ACM...· 0 citations
Long-video question answering (QA) forces multimodal large language models (MLLMs) to work within a tight frame budget, so the choice of frames largely decides whether a question can be answered at all. The standard recipe scores every frame against the question with a pretrained image–text matching (ITM) model and keeps the top scorers. A fundamental mismatch underlies this recipe: ITM models are trained on short, concrete visual descriptions, while QA questions are interrogative and often involve abstract terms. Scored against the question alone, the ITM yields a near-random signal whenever the question is not a direct image–text match, such as one asking for the temporal order of scenes. In our LongVideoBench diagnostic analysis, the score collapses even on benchmark-provided answer-relevant frames, with more than half falling into a near-zero region —not because the encoder is faulty, but because it behaves exactly as it was trained to. We argue that this format mismatch should be absorbed at the two ends of the pipeline while the encoder itself stays frozen. On the input side, a type-conditioned routed pipeline reformulates each question into a single ITM-aligned description by selectively applying grounding, decomposition, and constrained synthesis. The ITM therefore receives exactly one description per frame, preserving the per-frame matching cost of a standard single-query baseline. On the output side, because the score distribution remains polarized and answer frames are scattered in time, we replace top- $K$ selection with a parameter-free Rosin threshold followed by a temporal maximal-marginal-relevance (MMR) step that uses frame positions alone. Across three benchmarks (LongVideoBench, Video-MME, MLVU) and and four backbones (Qwen2-VL, Qwen2.5-VL, LLaVA-OneVision, LLaVA-Video), the resulting training-free pipeline, RECAST, consistently outperforms recent frame-selection baselines without modifying the ITM encoder.
S. Han, Thang Vu, Junyeong Kim· IEEE Access· 0 citations
BEAR-Bench (Bilingual Enterprise and Academic Reasoning), a self-contained, complex English-and-Russian benchmark comprising 1000 human-annotated questions based on text-rich business and scientific documents, is introduced, and existing hallucination detection methods are compared.
L. Chubarova, A. Kuleshova, D. P. Volkov et al.· 0 citations
Test-time scaling (TTS) reliably improves reasoning in large language models, but whether it transfers to small open vision-language models remains unclear. We examine this on EXAMS-V, a multilingual visual multiple-choice benchmark, comparing self-consistency, describe-then-reason with PRM-guided beam search, and two post-hoc selectors across Qwen2.5-VL-7B-Instruct and Qwen3.5-4B. What matters is the conditions under which TTS runs, not the search or verification machinery. The largest factor is parseability: an early prompt format left many chains reasoning correctly yet never committing to an answer letter, which a standard answer cue and a guided repair step largely remove. A larger decoding budget removes the rest: raising the per-chain token limit from 1k to 2k recovers 3.7 pp, whereas sampling more chains (8 to 16) adds only 0.15 pp. Once chains have room to finish, elaborate methods contribute little: PRM-guided beam search trails plain self-consistency by 0.39 pp at over eight times the cost, and neither a training-free generative critic nor a trained multimodal PRM beats majority vote across both policies. The largest gain comes instead from the policy model itself (+11.4 pp). Our best configuration reaches 84.1% on the held-out ImageCLEF 2026 test split, ranking first on the Visual MCQ leaderboard.