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Yangbin Yu
Tencent Hunyuan
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Mingyu Yang
Tencent Hunyuan
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Junyou Li
Tencent Hunyuan
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Founder's Pitch
"ProAct enables AI agents to excel in long-horizon planning with enhanced lookahead reasoning and stable decision-making."
Commercial Viability Breakdown
0-10 scaleHigh Potential
2/4 signals
Quick Build
4/4 signals
Series A Potential
4/4 signals
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Why It Matters
ProAct addresses the core challenge of compounding errors in long-horizon planning for AI agents in interactive environments, enabling more accurate and efficient decision-making.
Product Angle
ProAct could be productized as a middleware solution for AI developers, offering a modular system to enhance agents in gaming, autonomous robotics, and virtual simulations.
Disruption
ProAct can replace existing less efficient planning methods in AI by offering robust solutions for agents to reason over long horizons, surpassing current state-of-the-art models.
Product Opportunity
The market for enhanced AI decision-making tools is significant, particularly in sectors like gaming, autonomous vehicles, and smart robotics where long-horizon planning enhances performance. Companies in these sectors would pay for improved AI capabilities to optimize performance and user experience.
Use Case Idea
Integrate ProAct into educational platforms to create intelligent tutors that effectively plan and adjust to student learning paths, enhancing personalized education.
Science
ProAct enhances LLM agents' decision-making using a two-stage process. Stage one, GLAD, distills environment-based search trees into concise reasoning chains for the agent, avoiding the need for complex search during inference. Stage two uses MC-Critic to refine decision accuracy by providing low-variance value estimates via lightweight rollouts, stabilizing policy optimization in RL algorithms.
Method & Eval
ProAct was tested in stochastic and deterministic environments such as 2048 and Sokoban, showing improved planning accuracy. A 4B parameter model trained with ProAct outperformed all open-source baselines and was on par with closed-source state-of-the-art models.
Caveats
Scaling to more complex environments might introduce unforeseen challenges, and the dependency on quality of the initial environment data for training could limit its effectiveness.
Author Intelligence
Yangbin Yu
LEAD Tencent Hunyuan
Mingyu Yang
LEAD Tencent Hunyuan
Junyou Li
Tencent Hunyuan