Human-Computer Interaction

Opening — Why this matters now Large language models have learned how to talk. That part is mostly solved. The harder problem—quietly surfacing beneath the hype—is whether they can stay in character. The explosion of role‑playing agents (RPLAs) is not driven by novelty alone. It reflects a structural shift in how humans want to interact with AI: not as tools, but as persistent entities with memory, motivation, and recognizable behavior. When an AI tutor forgets who it is, or a game NPC contradicts its own values mid‑conversation, immersion collapses instantly. The paper reviewed here treats that collapse as a technical failure, not a UX quirk—and that framing is overdue. fileciteturn0file0 ...

Opening — Why this matters now GUI agents are getting faster, more multimodal, and increasingly competent at clicking the right buttons. Yet in real life, users don’t talk to software like prompt engineers. They omit details, rely on habit, and expect the system to remember. The uncomfortable truth is this: most modern GUI agents are optimized for obedience, not understanding. ...

Opening — Why this matters now AI assistants are no longer quiet utilities humming in the background. They talk back. They empathize. They ask follow-up questions. In short, they behave suspiciously like social actors. This design direction has triggered a familiar anxiety in AI governance: human-like AI leads to misplaced trust. Regulators worry. Ethicists warn. Designers hedge. Yet most of these arguments rest on theory, small samples, or Western-centric assumptions. ...

When we talk about AI agents that can “use a computer like a human,” most of today’s leaders—Claude, GPT-4o, Seed 1.5—are locked in proprietary vaults. This means the critical details that make them competent in high-stakes desktop workflows—training data, error recovery strategies, evaluation methods—are inaccessible to the wider research and business community. OpenCUA aims to change that, not by chasing hype, but by releasing the entire stack: tools, datasets, models, and benchmarks. ...

The GUI Agent Bottleneck: Stuck in Sparse Feedback Training LLM-based GUI agents to complete digital tasks—such as navigating mobile apps or automating workflows—faces a fundamental limitation: reward sparsity. Traditional reward formulations (Outcome Reward Models, or ORMs) provide feedback only at the end of a trajectory. If the task fails, the agent receives zero signal, regardless of how many useful intermediate steps it took. This severely limits credit assignment and slows learning, especially in environments with long action horizons. ...