arXiv:2607. 15845v1 Announce Type: new Abstract: Workflow generation in visual creation systems such as ComfyUI demands not only syntactic accuracy but also expert-level reasoning over modular compositions.
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arXiv:2607. 15845v2 Announce Type: replace Abstract: Workflow generation in visual creation systems such as ComfyUI demands not only syntactic accuracy but also expert-level reasoning over modular compositions.
arXiv:2508. 17298v3 Announce Type: replace-cross Abstract: Compositional visual reasoning has emerged as a key research frontier in multimodal AI, aiming to endow machines with the human-like ability to decompose visual scenes, ground intermediate concepts, and perform multi-step logical inference.
The paper introduces UniSandbox, a decoupled evaluation framework with controlled synthetic datasets, to study whether understanding informs generation in Unified Multimodal Models. Results show a notable understanding‑generation gap, especially in reasoning generation and knowledge transfer. Explicit Chain‑of‑Thought (CoT) in the understanding module bridges this gap, and self‑training can internalize CoT for implicit reasoning during generation; query‑based architectures also exhibit latent CoT‑like properties that aid knowledge transfer.
Recent advances in multimodal generative models have enabled instruction-based image generation to move beyond semantic manipulation to knowledge-driven visual reasoning. However, these methods focus on explicit commonsense reasoning, shallow causal understanding, and direct knowledge recall, failing at knowledge-intensive generation.
arXiv:2511. 17731v2 Announce Type: replace-cross Abstract: Chain-of-Thought (CoT) prompting has proven remarkably effective for eliciting complex reasoning in large language models (LLMs).
The paper introduces a multi-stage rule‑chaining framework for the Abstraction and Reasoning Corpus (ARC), aiming to model cognitive generalization by inferring abstract rules from few examples. It combines three solvers—a deterministic rule discovery module, a pattern‑composition engine, and a structural abstraction layer—executed sequentially in a fallback hierarchy that reuses earlier reasoning traces to improve interpretability and generalization. The system achieved over 95% accuracy on ARC tasks, demonstrating strong performance across deterministic, compositional, and abstract categories.