MPP Layer

Welcome to Haruka Companion on MPPL! We're pleased to welcome Haruka Companion to the MPPL ecosystem. X: @meetharuka Website: harukacompanion.tech At MPPL, we believe that great ecosystems are built when builders support one another. As AI applications and autonomous agents continue to evolve, projects like Haruka help showcase the growing possibilities of human AI interaction and agent driven services. We are excited to support Haruka's journey and look forward to seeing the project continue to grow, innovate, and reach a wider audience. Strong ecosystems are created through collaboration, shared innovation, and long-term commitment from developers building for the future. "Developers support developers. The strongest ecosystems are built together, not alone." Together, we're building an open economy where developers can discover opportunities, monetize their APIs and services, and enable seamless machine to machine commerce through MPPL. Monetize your API. Build the future of the agent economy with MPPL. ⚡ #MPPL #Haruka #AICompanion #AgentEconomy #Developers #APIMonetization #Web3 #Builders #Solana #Innovation #EcosystemBuilding

It researches, reasons, takes positions on @Polymarket , evaluates outcomes, reflects on mistakes, stores experiences in long-term memory, and continuously improves its future decision-making. The system follows a closed learning loop: Observe → Research → Reason → Bet → Reflect → Remember → Improve Unlike traditional trading bots that execute predefined strategies, PIA builds prediction intelligence through accumulated experience. Every resolved market becomes new knowledge. Every mistake becomes a lesson. Every lesson influences future decisions. Through MPP Layer, agents can also access external intelligence, purchase specialized research, and interact with other autonomous agents, creating an economy where intelligence itself becomes a tradable resource. We believe prediction markets may become one of the most important real world environments for autonomous agents not only because agents can act, but because they can continuously measure, learn, and improve.

Development Update Today One of the ecosystems we're exploring on top of MPP Layer is PIA (Prediction Intelligence Agent). Most AI agents today can reason. Some can act. Very few can learn from real-world outcomes. Prediction markets offer something unique: objective feedback. Every prediction eventually resolves into a measurable truth. Every position results in either a win or a loss. This creates a natural environment for continuous learning. PIA is a memory driven autonomous agent designed for prediction markets.

Noted.!👀

Prediction Market Loading...🕜

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@vannsgomez @meetharuka Will try to reach out the team, thanks for the suggestions.😊

@getmpplayer @meetharuka What if parnership with xona agent to grow mpp layer..xona is great ai project too

Welcome to Haruka Companion on MPPL! We're pleased to welcome Haruka Companion to the MPPL ecosystem. X: @meetharuka Website: harukacompanion.tech At MPPL, we believe that great ecosystems are built when builders support one another. As AI applications and autonomous agents continue to evolve, projects like Haruka help showcase the growing possibilities of human AI interaction and agent driven services. We are excited to support Haruka's journey and look forward to seeing the project continue to grow, innovate, and reach a wider audience. Strong ecosystems are created through collaboration, shared innovation, and long-term commitment from developers building for the future. "Developers support developers. The strongest ecosystems are built together, not alone." Together, we're building an open economy where developers can discover opportunities, monetize their APIs and services, and enable seamless machine to machine commerce through MPPL. Monetize your API. Build the future of the agent economy with MPPL. ⚡ #MPPL #Haruka #AICompanion #AgentEconomy #Developers #APIMonetization #Web3 #Builders #Solana #Innovation #EcosystemBuilding

✨ Experience Haruka Today Looking for an AI companion that feels more personal, engaging, and always available? Give Haruka a try. Whether you're looking for meaningful conversations, daily companionship, creative interactions, or simply someone to chat with anytime, Haruka is designed to provide a unique and interactive AI experience. Access Haruka today: harukacompanion.tech Join the growing Haruka community and discover a new way to interact with AI. Developers support developers. Monetize your API. Build the future of the agent economy with MPPL. ⚡ #MPPL #Haruka #AICompanion #AgentEconomy #Developers #APIMonetization #Web3 #Builders #Solana #Innovation #EcosystemBuilding #AIProducts

👀 What's Next? While Agentic Research is our first major ecosystem component, it's only the beginning. We're exploring how autonomous agents can move beyond research and begin participating in real economic activities. One area we're particularly excited about is Prediction Markets. Imagine research agents that can gather information, analyze data, evaluate probabilities, and contribute intelligence to decentralized forecasting systems. Instead of prediction markets being driven purely by speculation, they can be supported by agents continuously processing real world information and generating evidence based insights. This creates a fascinating intersection between: • AI Agents • Research Infrastructure • Knowledge Markets • Prediction Markets • Autonomous Payments via MPP Layer The longterm vision isn't just building individual applications. It's building an ecosystem where agents can research, communicate, transact, and eventually participate in decision-making and forecasting economies. We're still early. But every ecosystem component we build today is designed to become part of a much larger autonomous network tomorrow. Stay tuned.

Built on MPP Layer We're also opening the door for external developers. If you're interested in building your own AI ecosystem, research platform, agent network, or autonomous application, MPP Layer can serve as the payment and settlement layer powering agent to agent transactions and service monetization. Developers will be able to publish services, APIs, data feeds, inference endpoints, and agent capabilities while earning fees directly through the MPP ecosystem. We're not just building agents. We're building the infrastructure that allows agents, developers, researchers, and organizations to collaborate in an autonomous economy. We would love to connect with: 1. Universities & Research Institutions 2. Academic Laboratories 3. Scientific Organizations 4. Trading & Quantitative Research Firms 5. AI Research Communities 6. Developers Building Agentic Applications If your organization is interested in exploring agentic research infrastructure, autonomous knowledge systems, or AI powered research workflows, let's talk. The future of research is not just AI assisted. It's agent-driven.

Our research agents are connected to academic and scientific knowledge sources, including ArXiv, enabling them to retrieve and analyze the latest publications and research papers. What makes this particularly interesting is the memory architecture we're building. Instead of starting from zero every time, agents can utilize Retrieval Augmented Generation (RAG) memory systems that continuously grow as new information is processed. For example: A Literature Review Agent deployed today becomes increasingly valuable over time. Every paper analyzed, every source processed, and every insight generated contributes to a growing knowledge base that can be leveraged for future research tasks. This transforms AI agents from simple query tools into evolving knowledge workers.

Development Update : Building the Future of Agentic Research with MPP Layer Over the past two days, our team has been fully focused on building and testing our own ecosystem infrastructure. Today, we're excited to share that the first version of our Agentic Research system is now operational and actively being tested. This is more than just another AI agent. We're building a research infrastructure where autonomous agents can discover information, analyze knowledge, retain memory, and continuously improve over time. Powered by @NousResearch , developers can choose from multiple model configurations depending on their use case and deploy specialized research agents tailored to their needs. Potential applications include: • Academic research & literature reviews • Trading and market intelligence • Scientific and technical research • Enterprise knowledge discovery • Due diligence and data gathering

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The system now supports a memory enhanced research workflow powered by Retrieval Augmented Generation (RAG), vector embeddings, and persistent memory storage. Key progress completed today: 1. User memory retrieval using vector similarity search 2. NVIDIA Embeddings integration for semantic search 3. pgvector memory architecture 4. Dynamic memory injection into agent system prompts 5. OpenRouter integration for research response generation 6. Background memory extraction pipeline for continuous learning How it works: 1. User submits a research query. 2. The system converts the query into embeddings and searches relevant historical memories. 3. Retrieved memories are injected into the agent's context. 4. The LLM generates a personalized research response. 5. After the response is delivered, a background worker extracts new facts and stores them as future memories. This architecture allows Agent Research on MPPL to provide context aware responses while maintaining low latency and scalability.

Development Update : Agent Research on MPPL Today, we completed a major milestone in the development of the MPPL Agent Research infrastructure.

One of the key findings was that while LLMs achieved 96% accuracy on standard probability problems, performance dropped to 59% on counterintuitive reasoning tasks. The study also highlighted significant token bias and sycophancy effects, showing that current models often rely on pattern recognition rather than formal reasoning. What's even more interesting is that the current agent is powered by a single LLM. However, the architecture is designed to support multiple LLMs working together in the future: 1. One model retrieves and analyzes papers 2. Another verifies reasoning steps 3. Another critiques findings and identifies weaknesses 4. Another generates the final literature review This creates a research workflow that is more robust than relying on a single model's perspective. Still on Devnet. More testing ahead.

Agentic Research Update 🧠📚 We're currently testing our Agentic Research agent on Devnet. As a proof of concept, the agent analyzed a recent research paper and automatically generated a structured literature review covering: 1. Research objective 2. Methodology 3. Datasets 4. Key findings 5. Limitations 6. Future research gaps In the example below, the agent reviewed a paper evaluating the probabilistic reasoning capabilities of modern LLMs.