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arXiv:2607. 00491v1 Announce Type: cross Abstract: Benchmarks for vision-language models (VLMs) mostly test observational spatial reasoning: models describe relations already visible in the input.
GroundSight at GroundLM 2026 Shared Tasks: GoldenViewVQA introduces a new VQA benchmark that requires models to answer driving‑scene questions while also identifying the camera view that supports the answer. The authors present CoVeR‑VQA, a training‑free, multi‑stage verification and correction framework that starts with GPT‑5.6 zero‑shot predictions and applies successive view‑specific, prior‑guided, and cross‑split group‑level verifications using Gemini‑3.6‑Flash, Claude‑Opus‑5, and semantic question grouping. On the official test set, CoVeR‑VQA achieves 84.75 % joint accuracy, surpassing the zero‑shot baseline by 13.56 percentage points, and the final run reaches 88.14 % joint accuracy after evaluator‑informed post‑hoc corrections.
The paper introduces the Answer-Evidence Support Protocol (AESP) to evaluate whether the grounding evidence in remote sensing visual question answering actually supports the model’s answer. It applies task‑specific rules for existence, counting, and comparison questions and is tested on the newly constructed G‑RSVQA benchmark, which contains 15,027 samples with detailed object‑level annotations. Experiments on seven grounded vision‑language models show that up to 50.82% of correct answers are not supported by the provided grounding evidence.
arXiv:2608. 08021v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) should answer from concrete image evidence rather than language priors, dataset shortcuts, or irrelevant visual context.
StateSight is a new benchmark designed to isolate and evaluate the ability of vision‑language models to reconstruct latent spatial structure from a single image. It consists of three procedurally generated task families—cube‑net opposite‑face reasoning, occluded cube‑tower counting, and 4‑neighbor connected‑component counting—each with 300 deterministic prompts and exact‑match scoring. The benchmark also includes a companion dataset, StateSight‑Steps, with 900 image‑text examples and 3,600 intermediate visual states to aid analysis of reconstruction errors.
The paper introduces three new vision‑centric evaluation benchmarks—temporal frame retrieval, video future prediction, and causal memory distortion—to assess visual question answering in large video models. Unlike traditional benchmarks that rely on text-based multiple choice questions, these tasks require models to reason directly from visual inputs. The authors find that current state‑of‑the‑art models struggle with visual queries, highlighting a gap in visual understanding that future research should address.