arXiv:2609.14473v1 Announce Type: new Abstract: Personal AI assistants hold the potential to evolve from digital interfaces into embodied companions capable of guiding users through complex physical...
The paper introduces RISEBench and its expanded version RISEBench++, a benchmark for evaluating reasoning-informed visual editing (RISE). RISEBench++ categorizes tasks into six reasoning dimensions—Temporal, Causal, Spatial, Logical, Counterfactual, and Hybrid—decomposed into 12 subcategories and 65 fine-grained task types, and includes 1,000 human‑annotated test cases in English and Chinese. The authors also present RISE‑Agent, a training‑free framework that integrates reasoning‑driven planning, tool‑augmented execution, and verifier‑guided refinement, outperforming most existing approaches on diverse RISE tasks.
The paper introduces SVGLM, a framework that integrates scalable vector graphics (SVG) primitives into vision‑language models to enable image generation within reasoning tasks. By treating SVG both as image descriptions and text instructions, SVGLM offers a compact and interpretable method for connecting text and image reasoning. The authors provide a curated SVG‑based image editing dataset and demonstrate strong SVG generation and image‑aware reasoning performance on a mathematical benchmark.
The paper introduces RIG-BENCH, a benchmark for evaluating reasoning-driven image generation (RIG) in four cognitively demanding domains—Concept-based, Transformation-based, Pattern & Structure, and Scenario-based—using 2000 curated samples. It highlights a reasoning-generation gap in current unified generative models (UGMs) and world simulators, noting that these models often produce locally plausible but globally illogical outputs. RIG-BENCH aims to serve as a rigorous stress test and diagnostic framework to guide the development of next-generation, logically grounded UGMs and simulators.
arXiv:2607. 16409v1 Announce Type: cross Abstract: Unified Multimodal Large Language Models (MLLMs) offer a promising paradigm for unifying visual understanding and generation, yet they still struggle to follow complex spatial instructions and logical constraints in controllable image generation.
PrismGPT is a Vision‑Language Model that generates structured, region‑aware photo‑editing plans from a single image, without relying on commercial black‑box tools. It learns to diagnose aesthetic issues globally and locally while predicting precise editing parameters, using proxy‑guided learning with operation decomposition and region‑aware aesthetic ranking to bootstrap the model. A competence‑based dynamic scheduler shifts training focus from proxy tasks to the main editing task as skills improve, and all reasoning traces for fine‑tuning are self‑synthesized by the model itself. Experiments on MIT‑Adobe FiveK and a new professionally retouched benchmark, SPIRE, show PrismGPT achieves state‑of‑the‑art results using only about 6% of the training data required by previous methods.
arXiv:2608. 15006v1 Announce Type: cross Abstract: Although visual reasoning is crucial for solving complex geometry tasks, existing vision-language models rely heavily on text-only reasoning.
arXiv:2609.12606v2 Announce Type: replace Abstract: While multimodal reasoning has advanced rapidly, solving complex geometry problems critically hinges on active visual assistance, such as construct...
The paper introduces Mosaic, a multi-image visual harness that lets large language‑vision models (MLLMs) construct visual intermediates using ten composable image operations. It evaluates five re‑representation settings on existing multi‑image benchmarks and a new grounding‑focused benchmark, MosaicBench, finding that visual re‑representation benefits tasks requiring precise visual evidence more than those dominated by high‑level semantics. MosaicAgent‑8B is trained via reinforcement learning to compose these operations without demonstration trajectories, demonstrating diverse problem‑solving patterns.
Recent years have witnessed remarkable progress in image generation and editing, particularly regarding instruction following and visual fidelity. However, when handling ambiguous intentions, logical reasoning, and Out-of-Distribution (OOD) knowledge, existing image models often yield sub-optimal results due to a lack of deep reasoning capabilities and real-time external information.
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...
VBVR-Pro is a closed‑loop testbed that enables native visual reasoning through generation, offering 300 procedurally generated tasks that scale training and allow strong transfer to external benchmarks. It supplies verifiable reward scorers based on deterministic, task‑specific rules, outperforming VLM‑as‑a‑judge approaches and providing reliable signals for reinforcement learning. The suite also facilitates controlled modality studies, revealing that video generation excels at persistent spatiotemporal tracking while interleaved generation offers a compute‑efficient alternative, and highlights the importance of vision‑native trajectories for reasoning.