AgentVQA: A Unified Benchmark for Agentic Visual Understanding

Vision-language models (VLMs) increasingly power consumer-facing AI search, yet evaluating them on the diversity of everyday visual questions remains challenging. Existing benchmarks often target predefined capabilities, such as multi-hop retrieval or long-form synthesis, whereas users ask photo-grounded questions spanning a long tail of everyday scenarios. Despite advances in VLMs, users on Xiaohongshu, a mainstream Chinese image-sharing platform, continue to turn to other people for help with everyday visual questions. Motivated by this behaviour, we curate NoteVQA from these questions, yielding 252 items across 12 topical categories and 7 user intents. Each item includes a concise reference distilled from expert community responses and a human-audited interleaved reference answer that combines textual explanations with supporting visual evidence. We evaluate both short-answer correctness and interleaved-answer quality. To support the latter, we introduce AgenticInterleave, a single-agent ReAct framework for retrieval-supported answer generation, together with IVR-12, a 12-dimensional rubric for assessing the content, presentation, and image quality of interleaved references and model outputs. Across 9 frontier VLMs, the highest short-answer accuracy is 52.8%, while adding agentic search to Qwen3.5-397B-A17B improves accuracy by only 2.0%. For interleaved answers, the same model running AgenticInterleave scores 3.52 under IVR-12, compared with 4.65 for the human-audited references, with the largest gap in content quality. These results highlight the challenges that everyday visual questions pose for current VLMs in both answer accuracy and the quality of visually grounded explanations.

Vision-language models (VLMs) have shown promising spatial reasoning capabilities from static visual inputs, where the evidence needed to answer a question is available in the provided views. However, in cluttered environments, answer-relevant evidence may be occluded rather than absent: an object may lie beneath a pile, be covered by another object, or have identifying information facing away from the camera. Answering such questions requires physical interaction to reveal the hidden evidence. We formalize this setting as Manipulation-Grounded Visual Question Answering (MG-VQA), where an agent answers a question about an initially cluttered scene by using manipulation as an intermediate evidence-gathering operation. We introduce MG-VQA-Bench, comprising 600 human-verified questions across four spatial reasoning tasks, and evaluate it in a cluttered tabletop interactive environment. Eight VLMs are evaluated under three levels of environment access: Direct (image only), Perception (pointing, segmentation, and scene graphs), and Manipulation (perception, grasping, and pushing). Across all models, Direct and Perception achieve average success rates of 36.2% and 37.6%, respectively, only slightly above an image-blind chance baseline of 32.7%. In contrast, Manipulation raises average success to 56.8% (40.8-83.0% across models). Stronger tool-calling VLMs, GPT6Astra (83.0%) and Gemini3.8Flash (69.3%), search persistently and re-ground after unsuccessful interactions, while weaker models often answer without gathering sufficient evidence or stop prematurely after failed actions. Our results highlight the need for VLM agents that use manipulation for persistent, physically grounded evidence gathering and recovery. Code and benchmark is available here: https://github.com/vineet2104/MG-VQA

Agentic vision-language models increasingly act through extended interactions, but most evaluations still focus on single-image, single-turn correctness. We introduce AMIGO (Agentic Multi-Image Grounding Oracle Benchmark), a long-horizon benchmark for hidden-target identification over galleries of visually similar images. In AMIGO, the oracle privately selects a target image, and the model must recover it by asking a sequence of attribute-focused Yes/No questions under a strict protocol that returns Yes/No/Unsure feedback and penalizes invalid actions with Skip. This setting stresses (i) question selection under uncertainty, (ii) consistent constraint tracking across turns, and (iii) fine-grained discrimination as evidence accumulates. We instantiate AMIGO with the Guess My Preferred Dress task and evaluate open-source VLMs with metrics covering identification success, evidence verification, efficiency, protocol compliance, robustness to controlled feedback perturbations, and trajectory-level diagnostics. The benchmarking results show that final-answer accuracy alone overstates evidence-grounded performance: models can guess correctly without verification-passing evidence, waste turns through invalid questions, or fail to preserve the upload protocol. Strong AMIGO performance depends on the combination of visual discrimination, informative question selection, constraint tracking, efficient stopping, sustained protocol following, and recovery from controlled feedback noise; model scale alone does not guarantee reliable long-horizon interactive grounding.