The paper introduces a controlled evaluation framework for interactive visual grounding in large vision-language models (LVLMs), examining how varying amounts of initial target information and dialogue affect performance. Experiments across four visual contexts and interaction protocols show that current LVLMs lag behind human baselines, especially when no initial description is given and information must be gathered through questions. The study also finds that LVLMs are poorly calibrated, often overestimating confidence, and that interactive grounding remains a significant challenge requiring visual matching, information seeking, and synthesis.
arXiv:2609.22588v1 Announce Type: new Abstract: Vision-language models (VLMs) perform strongly on visual question answering benchmarks, yet often make decisions that contradict visual evidence they h...
arXiv:2610.19723v1 Announce Type: new Abstract: Interpreting an ambiguous utterance in real-world interactions often requires context beyond a single utterance. When the utterance refers to objects i...
MagiC is a benchmark for grounded multimodal cognition that evaluates vision‑language models on answer accuracy, step‑by‑step reasoning quality, grounding fidelity, and self‑correction. It contains about 5,500 weakly supervised QA examples generated from strong model outputs and 900 human‑curated examples with fine‑grained annotations, including rationales and bounding box groundings. The benchmark tests 15 models ranging from 7B to 70B parameters, introduces new metrics such as MagiScore and StepSense, and includes diagnostic settings to probe robustness against adversarial visual cues and introspective error correction.
The paper introduces Grounding IDs, latent identifiers that emerge when external visual cues—such as symbols or grid lines—structure both visual and textual modalities in large vision-language models. These IDs improve the alignment between visual components and their textual descriptions, enhancing cross‑modal grounding, reducing hallucinations, and boosting overall performance on multimodal tasks. The study demonstrates that partition‑inducing cues lead to more accurate connections and better attention between related components.
arXiv:2606. 31719v1 Announce Type: cross Abstract: In collaborative dialogue, shared perception does not guarantee shared interpretation.
arXiv:2511. 16107v3 Announce Type: replace-cross Abstract: Visual in-context learning (VICL) solves visual tasks by conditioning on a few input-output demonstrations without any model training.
arXiv:2610.08401v1 Announce Type: cross Abstract: While recent vision-language models (VLMs) have shown outstanding performance across diverse applications, they tend to under-use visual information...
arXiv:2607. 11436v1 Announce Type: new Abstract: Vision-language models increasingly succeed on multimodal reasoning benchmarks, yet their visual evidence often becomes unstable once it enters the language stack, weakening evidence-grounded reasoning.
The paper introduces VGEBench, a new benchmark for evaluating Vision‑Language Models (VLMs) on generalizable, visually grounded exploration of household devices. Unlike existing datasets that rely on static images or annotated trajectories, VGEBench employs a logic‑driven state machine to simulate multi‑turn interaction loops, requiring agents to actively perceive, act, and refine their actions to achieve goals. Experiments show that current VLMs struggle to translate semantic knowledge into physical execution and to maintain long‑horizon state tracking.
EPIC-Bench is a perception‑centric benchmark for fine‑grained embodied visual grounding in vision‑language models. It contains 6.6k human‑annotated tuples of Image, Text, and Mask across the three core stages of embodied interaction—Target Localization, Navigation, and Manipulation. The benchmark offers 23 fine‑grained tasks, multi‑dimensional settings, and evaluates over 80 leading VLMs to provide diagnostic insights into their vision‑language grounding capabilities.
Knowledge-Intensive Visual Question Answering (KI-VQA) benchmarks evaluate Vision-Language Models (VLMs) as multimodal knowledge assistants by requiring external information beyond a provided image to answer questions. KI-VQA involves multiple sub-problems -referring expression understanding, visual grounding, object recognition, knowledge retrieval, and reasoning-yet existing benchmarks typically report only end-task accuracy, obscuring where failures arise.
arXiv:2608.22429v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) capable of thinking with images often rely on external tools for fine-grained perception. However, this relian...