arXiv:2607. 12097v1 Announce Type: new Abstract: Video games are a dynamic medium experienced over time.
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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.
arXiv:2609.25652v1 Announce Type: new Abstract: Recent game world models support realistic visual simulation and interactive gameplay based on player inputs. However, they typically learn environment...
arXiv:2608. 04037v1 Announce Type: cross Abstract: Designing narrative-grounded interactive experiences remains labor-intensive because interactive content must align with the underlying world implied by the narrative.
arXiv:2407. 09013v2 Announce Type: replace Abstract: The attempt to utilize machine learning in PCG has been made in the past.
arXiv:2607. 11594v1 Announce Type: new Abstract: Multi-scene navigation (clearing an objective in one bounded space and then crossing a portal into the next) is a defining feature of contemporary 3D games, but authoring it is laborious: every portal must have consistent endpoints on both sides, each interior must remain navigable once it is furnished, and the resulting connectivity must be kept consistent across many files.
OpenBlock is an open, adaptive tile‑matching platform that uses a dual‑track content‑generation system: a deterministic rule‑based generator and an optional learned generator, both verified to ensure every piece set is fully placeable. A self‑play reinforcement‑learning agent diagnoses that long‑bar pieces become largely unplaceable at high board fill, indicating board‑state degeneration rather than difficulty drives late‑game failure. In live play, the learned track improves day‑1 retention by 1.8 percentage points and session duration by 7% compared to the rule track alone.