Interactive research prototypes showcasing cutting-edge AI technologies that reshape our world.
Explore our cutting-edge AI research prototypes that demonstrate novel approaches to complex problems in artificial intelligence.
Our multi-agent system for market research combines specialized AI agents that collaborate to gather, analyze, and synthesize market intelligence with minimal human intervention.
Multi-Agent Systems
Research platform that preserves document layout and formatting while translating content across languages, maintaining semantic integrity and visual structure.
Document Intelligence
Implementation of the Reasoning+Acting framework that combines reasoning traces and task-specific actions in a synergistic loop, enabling more robust planning and decision-making.
Reasoning Systems
Conversational interface for database interaction that translates natural language queries into structured operations, bridging the gap between human intent and data manipulation.
NL2Data Research
A reinforcement learning agent navigating through a grid environment using the Q-learning algorithm
Q(s,a) = Q(s,a) + α[r + γ·max(Q(s',a')) - Q(s,a)]Where:
Explore how multiple AI agents collaborate using swarm intelligence algorithms. Adjust parameters to see how they affect collective behavior patterns.
Our swarm intelligence system demonstrates how multiple autonomous agents can collaborate to solve complex problems. Using principles inspired by natural swarms like bird flocks and ant colonies, each agent follows simple rules that collectively create emergent behavior.
Agents are drawn toward the average position of nearby agents, creating groups
Agents avoid crowding neighboring agents, preventing collisions
Agents steer toward the average heading of neighboring agents
This visualization exemplifies key principles that make multi-agent AI systems powerful in real-world applications:
Unlike traditional AI with centralized decision-making, multi-agent systems distribute intelligence across autonomous entities. Each agent here makes independent decisions based on local information without complete knowledge of the entire system.
Complex global patterns emerge from simple local interactions. These emergent behaviors—like flocking, rotating, or splitting into groups—cannot be predicted by studying individual agents in isolation.
Problems intractable for single agents become solvable through collaboration. Adjusting parameters reveals how different balances of cohesion, separation, and alignment enable agent groups to navigate complex environments effectively.
Multi-agent systems maintain functionality even when individual agents fail or environments change. The collective adapts without requiring reprogramming, demonstrating resilience found in natural systems like ant colonies and human organizations.
These principles drive cutting-edge applications in autonomous vehicle coordination, distributed computing, supply chain optimization, and collaborative robotics. By studying swarm intelligence, we gain insights into designing AI systems that balance autonomy with collaboration to solve increasingly complex challenges.
Visualizing a network of collaborative agents using the Reasoning-Acting (ReAct) framework, where each agent thinks, acts, observes, and communicates with others.
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Agents use reasoning to understand problems, formulate plans, and generate insights. This thinking process is inspired by chain-of-thought prompting in language models.
After reasoning, agents take concrete actions based on their thought process. These actions may involve searching for information, making decisions, or communicating with other agents.
Agents observe the results of their actions and the environment. These observations provide feedback that informs the next reasoning step, creating a continuous loop.
Multiple specialized agents work together, sharing observations and coordinating actions. This collaborative approach enables more complex problem-solving than single-agent systems.