arXiv:2606.24470v1 Announce Type: new Abstract: A real-time agent for general computer use - with games as the most demanding case - must act within tens of milliseconds while still planning over seconds. These two regimes sit at opposite ends of the latency-quality tradeoff. A reasoning VLM (Qwen3-VL-8B-Thinking) deliberates effectively but requires ~1.5 s per response - far too slow for a 15 Hz control loop. In contrast, a reactive VLM (MiniCPM-o 4.5) acts in milliseconds but underperforms on planning-heavy tasks. We couple two frozen models of matched scale (9B reactive, 8B reasoning), leav
The increasing computational capabilities and architectural advancements in VLMs are enabling new approaches to real-time agentic systems, particularly for demanding applications like gaming.
This development addresses a fundamental challenge for AI agents: balancing real-time responsiveness with complex, long-term planning, crucial for broader autonomous system deployment.
The proposed 'latent bridge' architecture demonstrates a method for combining fast reactive models with slower deliberative models, improving the performance and applicability of AI agents in dynamic environments.
- · AI Agent developers
- · Gaming industry
- · Autonomous systems sector
More sophisticated and human-like AI agents become feasible in real-time interactive environments.
This capability could accelerate the adoption of AI agents in various sectors requiring immediate response and strategic foresight.
The enhanced performance of real-time AI agents could lead to new forms of human-AI collaboration and automation across white-collar and operational tasks.
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