arXiv:2606. 30185v1 Announce Type: new Abstract: Improving vision-language models (VLMs) on visual reasoning typically requires retraining or hand-designed prompts and tools.
Improving vision-language models (VLMs) on visual reasoning typically requires retraining or hand-designed prompts and tools. We present Dynamo, a training-free framework that adapts a frozen VLM without any weight updates.
arXiv:2609.24362v1 Announce Type: new Abstract: Sandboxed computer environments support multi-step reasoning with tools, executable programs, and persistent files, yet their extension from language m...
arXiv:2610.28386v1 Announce Type: cross Abstract: Visual tool use enables vision-language models (VLMs) to acquire task-relevant evidence, but its effectiveness can be limited by predefined visual op...
arXiv:2608.22174v1 Announce Type: new Abstract: Unified multimodal models (UMMs) can perform both understanding and generation, raising a central question: can visual generation improve understanding...
arXiv:2611.07397v1 Announce Type: new Abstract: Solving visual puzzles requires both accurate interpretation of visual evidence and reliable computation under task-specific rules. Embedding visual as...
RoSeViT introduces a role-separated Vision Transformer that decouples high-level instruction inference from visual workspace computation using controller and workspace tokens. This design allows nonlocal workspace interactions to be routed through the controller, reducing complexity compared to dense ViTs. Empirical results show RoSeViT improves ARC-1 accuracy from 54.5 to 56.6 with a backbone replacement and up to 62.6 when integrated into the full system.
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.
Beacon is a new agentic visual reasoning model that improves multimodal large language models (MLLMs) by better deciding when to use tools and how to use them. It introduces two key concepts—Mode Adaptiveness, which ensures tools are invoked only when necessary, and Tool Effect, which measures the net benefit of tool use— and trains the model with supervised fine‑tuning and reinforcement learning that rewards necessity-aware decisions and expands capability through expert hints. Across 13 benchmarks, Beacon outperforms other open‑source models, achieving the highest average score and the largest net tool‑gain on diagnostic tests.
OmniHarness is a framework that enables generalizable visual generation by learning symbolic policies from verified executions. It abstracts shared procedures and applicability conditions, allowing these policies to be instantiated, adapted, and composed for new tasks while keeping model parameters fixed. The system uses intermediate verification for refinement, self-directed inquiry to generate practice tasks, and continuous feedback to expand capabilities, achieving strong results on multiple benchmarks and outperforming baselines on Creative tasks.
Visual tool use has emerged as a fundamental capability for multimodal agents to actively acquire evidence beyond a fixed image encoding. The prevailing recipe learns this capability from teacher-generated trajectories filtered for answer correctness, implicitly assuming that every successful demonstration provides effective supervision.
VETR (Verified Execution Trajectories for Refinement) is a framework that enables visual agents to recursively improve symbolic graphics reasoning by extracting actionable evidence from execution traces. It retains aligned program/render records and uses an execution refiner to compile observed writes and state changes into constrained alternative programs, which are then validated against captured runtime state. Evaluations on BlenderGym and BlenderBench with GPT‑5.6‑luna and Qwen3.8‑27B demonstrate that VETR consistently outperforms existing refinement strategies such as BlenderAlchemy and VIGA.