← All IntelClip / AI AgentsThe gap list for local game agents
From Local Agentic Theory For Mobile Games — Shafik Quoraishee & Joanne Song, The New York Times · ≈16:43
A concrete, prioritized research agenda from practitioners: sub-16ms planning, predictive models of layout changes, per-person long-term memory, a portable game-state language, and honest benchmarks proving the agent helps.
What’s in it
- A concrete, prioritized research agenda from practitioners: sub-16ms planning, predictive models of layout changes, per-person long-term memory, a portable game-state language, and honest benchmarks proving the agent helps.
Clip transcript
partners. But there's still groundwork to be laid. Looking ahead for local agents to truly understand games, there's still a lot we need to build. First, they have to be faster. We need a plan we need plans and decisions within a 16-ms frame to prevent stuttering for games. We need models to predict the game so that we can see exactly what a layout change will do before they actually make it. And the real magic though will be the long-term memory learning one specific person's unique habits and needs over time. We need a shared game state language so one agent can work across multiple games instead of being rebuilt from scratch for different releases releases. And then finally, we need better chips and honest testing. Faster chips paired with real benchmarks to prove that the agent makes things better. And the future of AI doesn't have to be one giant centralized brain. It can be billions of small local brains, each running on a personal device, each shaped entirely by the individual it serves. Thank you.
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