Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.26860

OpenVLHarness: Real World Visual Agentic Tool-Use with Multimodal Memory

Yu Zhou·Cheng-Fu Yang·Rui Sun·Di Wu·Sibo Peng·Mingyang Zhang·Zhen Yang·Ruiyi Zhang·Nanyun Peng·Zhe Gan·Kai-Wei Chang
View original source ↗Read source document ↗Export metadata ↓

Abstract

Multimodal large language models (MLLMs) have made rapid progress on visual reasoning, yet many tasks require evidence that is difficult to obtain through internal reasoning alone: our controlled study on 22 benchmarks shows that simply increasing the inference budget of the base model yields only modest gains. Such tasks demand grounded perception, external knowledge, and numerical measurements obtained through heterogeneous tools, whose outputs must be made interpretable to the model and retained across reasoning steps. We introduce OpenVLHarness, a multimodal reasoning harness organized around a capability layer and persistent multimodal memory. The capability layer combines task-level tool interfaces with evidence rendering, providing consistent access to heterogeneous backends and converting their outputs into model-readable observations. Persistent multimodal memory retains the corresponding images, structured data, and web references as addressable artifacts throughout each query. A coding interface executes generated programs over stored data, enabling computations that combine results from multiple tools. Together, these mechanisms support an iterative process of acquiring, inspecting, and composing multimodal evidence. Evaluated on 22 benchmarks with eight open and proprietary backbones, OpenVLHarness improves average scores by 9.7-12.6 points over matched no-tool baselines, including +11.2 with Qwen3-VL-8B, +9.7 with Kimi K3, and +9.9 with GPT-6 Astra. With GPT-6 Luna at medium reasoning effort, OpenVLHarness outperforms the base model, Codex, and Visual Sketchpad at maximum effort by 7.7-8.5 points while incurring at most 37% of their model-token cost.

Explore related subjects

cs.CLcs.AIcs.CVcs.LGcs.MM

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yu Zhou, Cheng-Fu Yang, Rui Sun, Di Wu, Sibo Peng, Mingyang Zhang, Zhen Yang, Ruiyi Zhang, Nanyun Peng, Zhe Gan, Kai-Wei Chang. 2026-10-19. OpenVLHarness: Real World Visual Agentic Tool-Use with Multimodal Memory. https://arxiv.org/abs/2610.26860

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

MedHal: a Synthetic Dataset for Medical Hallucination Detection

Hallucination, the generation of non factual content by AI systems, poses serious risks in medical contexts, where errors can directly affect patient outcomes. We present MedHal, a large-scale dataset specifically designed to assess capabilities and train models on the task of hallucination detection in medical texts. Current hallucination detection methods face significant limitations when applied to specialized domains like medicine, where they can have disastrous consequences. MedHal addresses this issue by incorporating diverse medical text sources and tasks covering both intrinsic and extrinsic hallucinations, and by providing a substantial volume of data samples suitable for training medical hallucination detection models. We demonstrate MedHal's utility by training and evaluating a baseline medical hallucination detection model, showing improvements over general-purpose hallucination detection approaches. This resource enables more efficient evaluation and training of medical text generation systems while reducing reliance on costly expert review, potentially accelerating the development of medical AI research.

cs.CL↗
arXiv2026

Ask, Answer, and Detect: Role-Playing LLMs for Personality Detection with Question-Conditioned Mixture-of-Experts

Understanding human personality is important for personalized recommendation and mental health assessment. Existing personality detection methods typically encode social media posts into user representations and predict personality labels. Despite advances in large language models (LLMs), these methods face limited supervision from scarce labels and an unclear mapping between user language and psychological constructs. We propose ROME, a framework that incorporates psychological knowledge into personality detection. Inspired by standardized self-assessment tests, ROME uses LLM role-playing to simulate users' responses to validated psychometric questionnaires. These question-level answers provide interpretable, questionnaire-grounded evidence and intermediate supervision. A question-conditioned Mixture-of-Experts module jointly routes post and question representations to predict questionnaire answers under explicit supervision. The predicted answers are aggregated into an interpretable answer vector and fused with user representations for final personality prediction in a multi-task learning framework. Experiments on two real-world datasets show that ROME outperforms state-of-the-art baselines, with relative improvements of 13.60% on Kaggle and 19.72% on Pandora. Code is available at https://github.com/JasmineLyf/ROME.

cs.CL↗
arXiv2026

Clarify or Answer: Reinforcement Learning for Agentic VQA with Context Under-specification

Real-world visual question answering (VQA) may be context-dependent: an image-question pair could be under-specified, such that the correct answer depends on external information that is not observable in the image. In such cases, directly answering can lead to confident but incorrect predictions. We propose Clarify-or-Answer (CoA), an ask-or-answer agent that separately models the decision to ask or answer, and what to ask if needed. CoA first determines whether clarification is necessary; if so, it generates one or more clarification questions to iteratively enrich the context until ambiguity is resolved. To validate this framework, we introduce CONTEXTCLARIFY, a dataset of ambiguous VQA questions along with a contrast set of non-ambiguous questions. We further introduce GRPO-CR(Clarification Reasoning), a reinforcement learning approach that optimizes clarification question generation with multiple reward signals encouraging well-formed, focused, non-trivial questions that resolve ambiguity. Across three VLMs and three datasets, CoA achieves consistent improvements at both the module and system levels, with a single clarification turn providing substantial gains and recursive multi-turn execution offering modest additional improvements in select settings.

cs.CL↗