arXiv AI

Do Recipes Have Personas? Characterizing and Generating Creator Style in Attributed Procedural Graphs

The paper introduces ViralRecipesTrans, a dataset of execution flow graphs from culinary videos linked to specific creators, and proposes a graph learning framework to discover procedural personas. It shows that discrete topological metrics better capture a creator’s workflow than lexical classifiers, and presents a two‑stage generative model that predicts a creator’s exact execution graph for new dishes. The study finds that few‑shot LLMs excel at semantic assignment but lack macro‑planning, while the structured model offers superior topological control, and an ensemble approach combines both strengths for personalized workflow generation.

Hugging Face Trending Papers
Jul 15

ThinkBLOX: 3D Indoor Scene Generation with Progressive Reasoning

While traditional graphics methods often synthesize 3D indoor scenes autoregressively or hierarchically, recent vision-language model (VLM)-based generators predominantly adopt a one-shot paradigm where the full layout is planned at once. This one-shot approach often requires global re-optimization or complete reconstruction during interactive editing (e.

arXiv AI
Jul 20

Human-Aligned Procedural Level Generation Reinforcement Learning via Text-Level-Sketch Shared Representation

arXiv:2508. 09860v2 Announce Type: replace Abstract: Human-aligned AI is a critical component of co-creativity, as it enables models to accurately interpret human intent and generate controllable outputs that align with design goals in collaborative content creation.

By In-Chang Baek, Seoyoung Lee, Sung-Hyun Kim, Geumhwan Hwang, KyungJoong Kim
arXiv AI
3d ago

The Garden of Forking Paths: Threading Narrative Archetype as a Semantic Signal Through Gameplay Planning

The paper introduces Forking Garden, a branching game generation system that threads narrative archetype as a persistent semantic signal throughout the generation pipeline. Narrative progression is modeled with soft Rise/Fall states, guiding the creation of plot nodes, structural constraints, encounter composition, objectives, rewards, and difficulty adaptation. Experiments across ten storylines show that the system produces distinct archetypal trajectories, improves entity diversity, and that Rise/Fall distinctions remain meaningful during play, aiding both narrative understanding and creator interpretation.

By Yunge Wen, Chenliang Huang, Hangyu Zhou, Zhuo Zeng, Yuxuan Weng, Timothy Merino, Julian Togelius, Max Kreminski, Sam Earle
arXiv AI
Jun 12

CreativeBench: Benchmarking and Enhancing Machine Creativity via Self-Evolving Challenges

arXiv:2603. 11863v2 Announce Type: replace Abstract: The saturation of high-quality pre-training data has shifted research focus toward evolutionary systems capable of continuously generating novel artifacts, leading to the success of AlphaEvolve.

By Zi-Han Wang, Lam Nguyen, Zhengyang Zhao, Mengyue Yang, Chengwei Qin, Yujiu Yang, Linyi Yang
arXiv Computation and Language
Aug 24

Do Large Language Models Play Six Degrees of Separation? Measuring Topological Compression in Long-Context Manifolds

The paper shows that large language models (LLMs) naturally organize their hidden state manifolds into small‑world networks, enabling efficient multi‑hop reasoning. By converting similarity matrices into unweighted graphs, the authors trace connectivity between distant semantic anchors and find a sharp topological phase transition: deep reasoning layers compress conceptual distances into paths bounded by six semantic hops, while early syntactic layers remain fragmented. The framework is applied to zero‑shot hallucination detection in Retrieval‑Augmented Generation, revealing that factual generations preserve a ~3‑hop structure, whereas hallucinations collapse the topology.

By Md. Faiyaz Abdullah Sayeedi