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COTA - A new type of game intelligent agent launched by Hyperparameter Technology

COTA is a new type of game intelligent agent launched by Hyperparameter Technology. Driven by a Large Language Model (LLM), it possesses cognitive, operational, tactical, and auxiliary capabilities. COTA breaks through traditional reinforcement learning and supervised learning models, achieving its goals through innovative architecture...

What is COTA?

COTA, a novel game AI agent developed by Hyperparameter Technology, is driven by a Large Language Model (LLM) and possesses cognitive, operational, tactical, and auxiliary capabilities. Breaking away from traditional reinforcement learning and supervised learning models, COTA achieves millisecond-level response times through innovative architecture, reaching the level of high-scoring real players. COTA performs exceptionally well in FPS game tests, approaching human-like performance in both solo combat and team coordination. COTA's biggest highlight is its use of thought chain technology, making the AI decision-making process transparent and explainable, allowing players to clearly understand the AI's behavioral logic. COTA elevates the level of game AI, bringing new possibilities to future game development and experiences.

Main functions of COTA

  • Advanced tactical decision makingCOTA can formulate macro-level tactics, such as analyzing maps, judging enemy intentions, and formulating strategic policies (such as "all-out rush" or "tactical retreat").
  • Precise operation executionAt the micro level, COTA can perform complex operations, such as sudden stop and gun pull, cover battle, throwing smoke grenades, planting and defusing the bomb, etc., to complete tactical cooperation in multiplayer combat.
  • Explainability of thoughtThrough Chain of Thought (CoT) technology, COTA makes the decision-making process transparent, allowing players to view the AI's thought process in real time and understand the reasons behind each action.
  • Real-time response capabilityCOTA's response time reaches the level of hundreds of milliseconds (as fast as 100ms), meeting the needs of real-time game scenarios.

COTA's technical principles

  • Model selectionCOTA is based on the Qwen3-VL-8B-Thinking model, which has 8B parameters, balancing performance and efficiency, and is suitable for real-time game scenarios.
  • Dual-system layered architectureCOTA employs an innovative "dual-system layered architecture," simulating the collaborative working mode of the human brain's "fast and slow systems." The upper-level "Commander" is responsible for macro-tactical reasoning and outputs strategic deployment; the lower-level "Operator" translates strategic instructions into specific operations, executing micro-tactics. This effectively decouples the decision-making chain and improves overall performance.
  • Training methodsThe training process of COTA consists of three stages: First, supervised fine-tuning (SFT) is performed using a high-quality game CoT dataset to complete the cold start; then, group relative policy optimization (GRPO) is introduced to enhance the model's decision robustness in complex situations through large-scale self-play; finally, direct preference optimization (DPO) is used to align with data from high-level human players, improving the readability of the thought process and the anthropomorphism of the operation.
  • Mind Chain TechnologyCOTA utilizes Chain of Thought (CoT) technology to transform AI's decision-making process from a "black box" to a "white box." In the CoT panel, users can clearly see the real-time scrolling thought process, understanding the reasons behind each AI action. This transparent decision-making process enhances AI's explainability, providing game developers and players with a more intuitive way to understand and interact with it.

COTA project address

  • COTA reservation application addresshttps://www.chaocanshu.cn/product/cota_apply

Application scenarios of COTA

  • Game developmentCOTA can be used as a development tool for highly realistic NPCs. Its "white-box" thinking chain function helps developers intuitively review AI decision-making logic and optimize the debugging process.
  • Game experience optimizationCOTA can become a "high-IQ teammate" for players through natural language interaction, providing tactical guidance and collaborative operations, enhancing game immersion and interactivity, and improving the player experience.
  • esports trainingCOTA can provide esports players with a high-level competitive environment, assist in tactical training, and its transparent decision-making process can serve as a teaching tool.
  • Education and teachingCOTA's transparent decision-making mechanism is a high-quality tool for AI teaching and research, helping students understand the principles of complex models.
  • Technology migrationCOTA's technical architecture and training methods have strong transferability and can be applied to complex decision-making fields such as intelligent transportation, industrial automation, and medical assistance.
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