A Competitive Reasoning AI Subnet on Bittensor
This work focuses on integrating reasoning agents into robotic systems to enhance intelligent behavior in both simulated and physical environments. By combining symbolic and data-driven reasoning, robots can make context-aware decisions, adapt to dynamic conditions, and perform complex tasks with greater autonomy.
This challenge explores how reasoning agents can enhance intelligent behavior in robots across both simulated and real-world environments. By combining symbolic and data-driven reasoning, robots gain the ability to make context-aware decisions, adapt to dynamic conditions, and carry out complex tasks with greater autonomy.
Learn more ->This benchmark evaluates the reasoning skills of large language models through structured tasks and competitions. By integrating symbolic reasoning, simulation, and data-driven methods, the benchmark provides measurable insights into how well LLMs can perform logical, strategic, and context-sensitive problem-solving.
We have some datasets which is used to train llm models for better reasoning capabilities for miners and users.
A curated dataset from thousands of Codenames matches between human players and LLMs.
Benchmarks capturing logical reasoning tasks to evaluate model problem-solving ability.
Dataset of reasoning steps used by AI agents to control simulated robots in virtual environments.
Logs of API queries and model responses for utility-based Alpha Token transactions.
Dataset of conversations and reasoning steps between multiple AI agents working together.
Records from human vs AI competitions in reasoning games like Codenames and beyond.
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