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20232026
most citedPCGRLLM: Large Language Model-Driven Reward Design for Procedural Content Generation Reinforcement Learning

1 citations · 2 across the 11 of their papers we have counts for

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cs.RO2026

RobotUse: Allocating Computation, Context, and Decisions

Junhoo Lee, Injun Baek, Seungyeon Kim +4

Robot agents must connect their intended actions to observed outcomes while retaining the context needed to revise their choices over repeated attempts. Existing interfaces often l…

cs.CL2026

LandingAgent: A Reference-Annotated Dataset and Agentic Generation Framework for Landing Pages

Injun Baek, HyeongSeok Lee, Yearim Kim +2

Landing pages are goal-oriented web interfaces that must communicate a target-specific value proposition while organizing information flow, visual hierarchy, and calls to action (C…

cs.AI2026

When Saying No Makes Better Videos: Designing Dual Gatekeeping for Pedagogically Grounded AI Content Creation

Yearim Kim, Injun Baek, Nojun Kwak

To prevent the adoption of aesthetically polished but pedagogically flawed AI content, we study a video authoring pipeline featuring two layers of structured refusal. The first lay…

cs.AI2026

Multiverse: Language-Conditioned Multi-Game Level Blending via Shared Representation

In-Chang Baek, Jiyun Jung, Geum-Hwan Hwang +2

Text-to-level generation aims to translate natural language descriptions into structured game levels, enabling intuitive control over procedural content generation. While prior tex…

cs.CV2026

PedaCo-Gen: Scaffolding Pedagogical Agency in Human-AI Collaborative Video Authoring

Injun Baek, Yearim Kim, Nojun Kwak

While advancements in Text-to-Video (T2V) generative AI offer a promising path toward democratizing content creation, current models are often optimized for visual fidelity rather…