MIRAGE: Mobile Agents with Implicit Reasoning and Generative World Models

MIRAGE is a new AI framework that enables mobile agents to reason internally using compressed latent representations instead of generating verbose reasoning chains. By aligning hidden states with future interface screenshots, the system achieves comparable performance to explicit chain-of-thought approaches while reducing token generation by 3-5x, offering significant efficiency gains for AI-powered mobile automation.

MIRAGE represents a meaningful shift in how AI systems approach reasoning efficiency for mobile task automation. Rather than externalizing thought processes through decoded text chains—a common pattern in recent language model agents—the framework compresses reasoning into continuous latent vectors learned from training traces. This architectural choice directly addresses practical deployment constraints: reduced token generation lowers computational costs, decreases inference latency, and minimizes supervision overhead during training.

The research builds on growing recognition that explicit reasoning chains, while interpretable and effective, carry significant efficiency penalties. MIRAGE's dual objective—learning compressed reasoning while predicting future interface states—creates a coupled representation where hidden computation simultaneously serves as both thought abstraction and environmental dynamics model. This is conceptually similar to how human visual processing integrates prediction with action planning.

Benchmark results demonstrate material improvements: on AndroidWorld, MIRAGE matches supervised fine-tuning baselines while using 75% fewer decoded tokens on AndroidControl and improving instruction-tuned baselines by 10.2 points. These gains matter primarily for developers building mobile AI agents where token budget directly impacts operational costs and responsiveness. The 3-5x reduction in required tokens translates to faster interaction loops and reduced infrastructure costs for production deployments.

Looking forward, this work signals intensifying focus on inference efficiency within agentic AI systems. As mobile agents become production-critical for enterprise applications, frameworks that compress reasoning while maintaining performance become increasingly valuable. The generative world-model component also suggests emerging trends toward tighter integration between reasoning and environmental understanding.

• →MIRAGE reduces token generation 3-5x versus explicit chain-of-thought while maintaining comparable performance on mobile automation tasks.
• →The framework learns compressed reasoning representations, eliminating the need to decode verbose rationales at inference time.
• →Generative world-model alignment encourages agents to anticipate future interface states, improving action grounding and planning.
• →Results show 10.2 point improvements over instruction-tuned baselines on AndroidControl with 75% fewer tokens generated.
• →Efficiency gains directly reduce computational costs and latency for production mobile agent deployments.