Executive Summary​

This report presents an innovative paradigm to solve the fundamental governance problems faced by DAO: low participation, decision-making inefficiency, and a lack of accountability. The core thesis is the fusion of Futarchy principles, AI agents, and financial derivatives concepts to transform governance itself into a dynamic financial market. This model goes beyond simple binary prediction markets to build an ecosystem where sophisticated synthetic options, linked to governance proposals and KPI, are issued and traded.

Key findings are as follows. First, a governance-linked options market transforms a DAO's native token from a simple voting right into an essential collateral and medium of exchange for market participation: a 'casino chip.' This exponentially increases the token's trading volume and liquidity, driving an economic flywheel that creates genuine transactional demand beyond speculative interest.

Second, AI agents are a critical element for the expansion and popularization of these complex governance markets. AI performs roles such as information gathering, strategy automation, liquidity provision, and providing a customized UX, lowering the entry barrier for non-expert participants while optimizing micro-decisions at a scale beyond human capacity, all based on market principles.

Third, a reward structure that is scalar and option-like, where compensation varies continuously with the degree of KPI achievement rather than being 'winner-take-all,' allows for the design of more nuanced and performance-oriented incentives. This imposes real 'skin-in-the-game' for both proposers and participants, holding them financially accountable for poor decisions and clearly rewarding success, thereby preventing governance corruption and fostering a culture of responsibility.

Finally, this model offers practical solutions to new problems that are difficult to solve with existing governance systems, such as imbuing memecoins with purpose or managing the orderly process of a CTO(Community Takeover). By providing an in-depth analysis of this architecture's specific design, technical foundations, economic effects, and potential risks and mitigation strategies, this report offers a blueprint for DAO to evolve into information-based organizations that allocate capital and influence based on verifiable performance.

Executive Summary​

Despite the exponential growth of data, building high-quality, tailored datasets essential for training AI models remains a challenge, demanding significant cost and effort. While first-generation AI copilot tools represented an initial step towards addressing these issues, they have demonstrated limitations in accuracy, transparency, and cost-efficiency, leading to what is now recognized as 'copilot fatigue' among users. This report addresses these data paradoxes and the shortcomings of existing copilots, proposing a transformative paradigm where AI can autonomously generate meaningful datasets directly from data lakes. 'Probe', currently under Pre-MVP implementation, is designed to actualize this vision, leveraging the proven efficiencies of hierarchical data structuring to revolutionize data preparation and analysis, thereby opening new horizons for AI development.

1. Introduction​

AI development today faces a dual challenge: models are growing ever more complex, yet aligning them with human needs requires extensive oversight and feedback. Fission’s DeSAi framework (Decentralized Science + AI) addresses this by combining advanced model optimization techniques with large-scale human feedback loops (e.g., Reinforcement Learning from Human Feedback, RLHF) and community-driven governance. The central question, however, is how to incentivize and coordinate these decentralized feedback processes effectively.

Fission’s answer is Futarchy—a governance model in which communities do not simply vote on proposals, but instead bet on which proposals will best fulfill a shared success metric. By rewarding accurate predictions and penalizing mistaken ones, Futarchy taps into collective expertise to guide strategic decisions. This research report explores how Futarchy can be applied to optimize AI development and deployment within the DeSAi paradigm. Specifically, it will:

1. Define Futarchy and its Core Mechanics Explain how prediction markets function in an on-chain governance context.
2. Apply Futarchy to AI Optimization Examine how conditional markets can shape AI model improvements through community feedback and economic incentives.
3. Discuss Technical Implementation Detail the use of decentralized oracles, time-weighted average price (TWAP) mechanisms, and other features needed for reliable performance tracking.
4. Outline a Hybrid Rollout Strategy Propose steps for transitioning from off-chain or advisory market trials to fully on-chain Futarchy, ensuring stakeholder trust and participation.
5. Review Key Case Studies Highlight real-world experiments—such as Augur, GnosisDAO, and more recent initiatives—that demonstrate both the potential and challenges of futarchic governance.
6. Identify Risks and Mitigations Analyze common pitfalls—market manipulation, low liquidity, poorly chosen metrics—and discuss safeguard techniques.
7. Present an End-to-End Futarchy Flow Illustrate how proposals move from initial community submission to on-chain market assessment, model implementation, and verified outcomes.
8. Conclude with a Vision for AI Governance Summarize how Futarchy can become a transformative model for AI governance and offer insights on practical adoption.

Throughout, we maintain a focus on aligning AI development with community values—“vote on values, but bet on beliefs,” in the spirit of Futarchy’s guiding principle. By leveraging prediction markets and decentralized participation, Fission’s approach seeks to ground each model update and research decision in real data and collective expertise, rather than top-down judgment or speculation. This synergy can lead to a more transparent, accountable, and ultimately more effective path forward for AI.

The Dawn of Generalist Humanoid Robots​

In the rapidly evolving field of artificial intelligence, one of the most exciting frontiers is the development of generalist humanoid robots capable of performing a wide range of tasks in human environments. Recent breakthroughs in hardware design, model architecture, and training methodologies have brought us closer than ever to realizing this vision. This blog post explores the current state of robotics AI agents, focusing on NVIDIA's groundbreaking GR00T N1 model, and examines both the possibilities and challenges that lie ahead.

The Rise of Robot Foundation Models​

Just as foundation models like GPT and CLIP have revolutionized natural language processing and computer vision, similar approaches are now being applied to robotics. These robot foundation models aim to provide a versatile "backbone" of intelligence that can reason about novel situations, handle real-world variability, and quickly adapt to new tasks.

NVIDIA recently introduced GR00T N1, an open foundation model specifically designed for humanoid robots. GR00T (Generalist Robot Transformer) represents a significant step toward creating robots that can operate in complex, unstructured environments and perform a diverse array of tasks through a unified learning framework.

1. Introduction​

Managing unstructured data (text, images, audio, video) for machine learning is labor-intensive, especially when labeling large datasets. A proposed solution is to use hierarchical structuring of labels – for example, having a broad category like “human” with sub-labels like “race”, “gender”, “age group”. The hypothesis is that a taxonomy of labels (categories and subcategories) can streamline annotation workflows and reduce labeling and preprocessing costs. In this report, we investigate existing research and industry practices to see if this hypothesis holds true. We examine how hierarchical taxonomies and multi-level schemas have been applied in data labeling pipelines, whether they improved efficiency or reduced resource use, and how this approach relates to modern data-centric AI techniques (like weak supervision or active learning). We also discuss how hierarchical labeling could integrate with emerging architectures such as the MCP(Model Context Protocol) for agent-based AI, and consider potential benefits for AI safety and fairness (through more consistent, transparent labeling).

2. Background​

Taxonomies and Hierarchical Labels​

A taxonomy in data labeling is a structured classification scheme: it defines categories and (optionally) subcategories in a tree-like hierarchy. In practice, a well-designed taxonomy can enhance labeling efficiency and consistency. By providing clear category definitions and relationships, taxonomy-based labeling gives annotators a structured guideline, reducing ambiguity. According to industry guides, a good taxonomy enhances efficiency and accuracy in data labeling, reduces training time, and improves data comprehension.

In the rapidly evolving landscape of artificial intelligence, a new standard has emerged that's quietly revolutionizing how AI agents interact with tools and capabilities. The Model Context Protocol (MCP) has grown from an overlooked concept to a central pillar in AI development seemingly overnight. But what exactly is driving this sudden interest, and why should developers and AI enthusiasts take notice?

The Unexpected Journey of MCP​

When the Model Context Protocol was first introduced in November 2023, it received little attention from the developer community. Initially described as "the future of LLM Agent Tools," MCP was largely dismissed as just another collection of tools for large language models - nothing more revolutionary than a basic toolbox.

This lack of interest stemmed from two key factors. First, MCP appeared to be merely a collection of tools without significant differentiation from existing solutions. Second, and perhaps more importantly, no major frameworks supported MCP integration at the time, limiting its practical application.

The turning point came in early 2024 when Cursor.AI, one of the leading automated coding tools, announced full MCP integration into their system. This endorsement from a prominent platform sparked widespread interest, transforming MCP from an overlooked concept to one of the hottest topics in AI development within just a few months of its release.

1. Introduction​

1-1. What Fission Does​

Fission is an AI research and development team that specializes in model optimization—making advanced AI models more efficient, scalable, and accessible to a broader range of users. Rather than pouring endless resources into ever-larger models, we focus on streamlining computing requirements and enhancing model performance through smarter, more targeted approaches. Our guiding principle is simple yet profound: the best AI emerges when technical innovation is combined with high-quality, iterative human feedback.

To achieve this, we design workflows and tools that invite user participation throughout the AI lifecycle. From initial data refinement to ongoing feedback loops, we’ve learned that human insight—especially when harnessed at scale—often trumps brute-force computation. By refining AI models through collective user input, we aim to craft systems that are both robust and grounded in real-world needs.

1-2. The Core Narrative​

As we delved deeper into model optimization, we discovered a critical tipping point: no matter how elegant or efficient our algorithms became, they still depended on authentic human feedback to truly excel. While data labeling had been the traditional approach, we realized that continuous, context-rich evaluation—from domain experts, enthusiasts, and everyday users—was the key to fine-tuning AI in a more dynamic, impactful way.

However, gathering and managing this feedback via centralized methods proved costly and time-consuming. It was also difficult to maintain transparency and fairness at scale. That challenge led us to explore more decentralized structures—platforms where users could offer feedback and be rewarded in ways that felt natural, equitable, and community-driven.

That’s where DeSci (Decentralized Science) entered the conversation. DeSci promised an open, collaborative environment for knowledge-sharing and verification, yet it quickly became clear that advanced AI was needed to handle large data streams and real-time inputs. Combining DeSci’s ethos with AI-driven workflows gave rise to DeSAi—a synergy merging decentralized collaboration with iterative optimization.

An LLM-based agent is an AI system that leverages a Large Language Model (LLM) as its core computational engine to perform complex tasks autonomously. These agents are capable of understanding and generating human-like language, reasoning through problems, planning actions, and interacting with external tools or environments to achieve specific objectives.

Key Components of LLM-Based Agents:

1. Core LLM: The foundational large language model trained on extensive text data, enabling the agent to comprehend and produce human-like language.
2. Prompting Mechanism: Carefully crafted prompts that define the agent's identity, instructions, and context, guiding its responses and actions.
3. Memory Modules:
   • Short-Term Memory: Maintains context within ongoing interactions, ensuring coherent and contextually relevant responses.
   • Long-Term Memory: Stores information from past interactions, allowing the agent to recall and utilize previous knowledge in future tasks.
4. Knowledge Integration: Incorporates domain-specific knowledge, commonsense understanding, and procedural information to enhance decision-making and task execution.
5. Tool Integration: Interfaces with external tools, APIs, or services to perform specialized tasks beyond language processing, such as data retrieval, computations, or accessing real-time information.

The rapid evolution of artificial intelligence (AI) hinges significantly on the quality of data used for training models. However, centralized data labeling—where a few entities control the annotation and validation processes—poses significant risks to the development and deployment of robust AI systems. As AI becomes increasingly multi-modal, combining textual and visual modalities, the limitations of centralized labeling become more evident, threatening the very progress of the field.

The Limitations of Large Language Models (LLMs)​

Recent research has highlighted critical shortcomings in large language models (LLMs) when interpreting complex datasets, such as images of clocks with hour and minute hands. And it even has difficulty to understand ambiguity in general text contexts. While advanced models like CLIP and BLIP have demonstrated promise in aligning textual descriptions with visual inputs and top edge GPT based models are shown impressive understanding both texts and images, they struggle with tasks requiring precise spatial reasoning and understanding general context of overall languages. This is not merely a limitation of the models themselves but also a reflection of the constraints imposed by the quality and structure of labeled data.

Retrieval-Augmented Generation (RAG): A Critical Tool for Managing and Selecting Datasets in Decentralized Data Labeling​

In the evolving landscape of artificial intelligence (AI), data is the lifeblood of innovation. As the field transitions toward decentralized data labeling frameworks to address biases and enhance diversity, the challenge of managing and selecting datasets becomes paramount. Retrieval-Augmented Generation (RAG) emerges as a transformative solution, combining retrieval mechanisms with generative models to optimize data utilization and improve the quality of AI systems. This blog explores the concept of RAG, its role in decentralized labeling, and why it is essential for dataset management.

Understanding Retrieval-Augmented Generation (RAG)​

RAG is a hybrid approach that integrates information retrieval with generative AI models. Unlike standalone generative models, which rely entirely on pre-trained data, RAG systems retrieve relevant external data to enhance the generation process. This method combines the strengths of retrieval systems, such as search engines, with the creative potential of generative models, enabling:

1. Context-Aware Generation: By pulling in relevant external knowledge, RAG can generate responses or insights that are more accurate and grounded in factual data.
2. Dynamic Adaptation: The ability to retrieve real-time or updated information ensures that models remain relevant and effective.
3. Enhanced Diversity: By sourcing data from decentralized and diverse repositories, RAG systems mitigate biases inherent in single-source datasets.