OpenMind - Subnet Design Proposal

1. Introduction: The Vision for Persistent, Lossless AI Memory

OpenMind is a Bittensor subnet engineered to deliver an always-available, persistent memory layer specifically for AI agents, chatbots, virtual assistants, long-running intelligent systems, and any framework. Our core vision is to fundamentally eliminate the "amnesia" that plagues modern AI systems — where fixed context windows force aggressive, lossy summarisation of histories, leading to inconsistent behavior, repeated user explanations, escalating token costs, hallucinations from memory gaps, and an inability to build genuine long-term coherence or relationships. We believe the future of agentic AI lies not in ever-larger prompts or ephemeral local caches, but in a shared, external, uncompressed "open mind" that every MCP-compatible agent can access instantly and reliably.

To realize this, OpenMind has designed a sophisticated incentive mechanism that commoditises high-fidelity, intelligent memory services. Miners compete to provide the most accurate, fast, durable, and contextually intelligent storage and retrieval — handling encrypted shards of full histories (conversations, documents, transcripts, tool outputs, knowledge graphs), hybrid semantic vector + relational graph search, tiered durability (basic replication vs. premium Reed-Solomon erasure coding), and seamless MCP endpoint exposure. Validators rigorously evaluate performance across relevance, fidelity, speed, durability proofs, graph reasoning depth, and downstream agent improvement, assigning weights that drive evolutionary pressure toward ever-better memory utility.

This proposal details the OpenMind subnet's architecture, including its incentive structure, miner and validator roles, mechanisms for genuine proof of intelligence, and the strong market rationale that positions it as foundational DeAI infrastructure. We will show how OpenMind creates a self-sustaining, decentralized ecosystem that empowers any MCP-integrated agent to remember everything — forever — unlocking coherent, cost-efficient, human-like intelligence at scale.

2. Emission and Reward Logic: A Benchmark-Beating System

OpenMind operates on a decisive benchmark-beating reward model, inspired by high-stakes competitive dynamics but adapted to reward sustained excellence in memory utility rather than a strict single-winner snapshot.

The core principle is straightforward: the miner (or group of miners) whose performance consistently achieves and maintains the highest aggregate utility score — measured across real and synthetic MCP queries, retrieval relevance, fidelity to original data, reconstruction accuracy (in premium mode), latency, durability proofs, graph reasoning depth, and measurable downstream agent improvement — captures the largest share of the subnet's TAO emissions for the validation cycle (tempo).

Whenever a new all-time high benchmark is set (e.g., a miner delivers superior hybrid retrieval + RS reconstruction results on a standardised set of challenges, or sustains top scores across multiple epochs), that miner's weighted contribution surges, drawing the majority (or in peak cases, nearly all) of the miner-side emissions until surpassed. This creates relentless pressure to innovate while allowing multiple high-performers to share rewards proportionally based on how closely they approach or exceed the current frontier.

This approach ensures emissions always flow to the cutting edge of memory intelligence: the best context retrieval, the most faithful reconstructions, the deepest relational insights, and the lowest hallucination rates in agent loops. Unlike flat proportional allocation, it amplifies rewards for breakthroughs (e.g., a novel indexing method that halves latency while boosting precision@K) while still distributing meaningfully to strong contenders — preventing winner-takes-all from becoming winner-takes-nothing stagnation.

Incentive Alignment for Miners and Validators

The key to OpenMind's success lies in radical transparency and outcome-oriented evaluation.

• For Miners: Miners are driven to maximize genuine utility rather than superficial metrics. Their earnings scale dramatically when they deliver high-relevance retrievals, faithful graph-based reasoning, low-latency responses, successful storage proofs, accurate Reed-Solomon reconstruction in premium mode, and verifiable improvements in downstream MCP agent performance (e.g., coherence/consistency gains in follow-up queries). High-spec hardware for erasure coding, large-scale indexing, and fast graph traversal receives priority task assignment and higher effective multipliers, creating a clear path for specialization and investment. The benchmark-beating dynamic encourages continuous R&D: a breakthrough in semantic + graph hybrid search becomes the new baseline, raising the bar for all and turning competition into collective advancement.
• For Validators: Validators are aligned to produce honest, accurate, diligent scoring. Their dividends scale with how closely their submitted weights match the final Yuma consensus outcome. By rigorously testing miners on real MCP queries, synthetic benchmarks, possession challenges, poisoned/adversarial inputs, and reconstruction tasks, validators maintain the subnet's integrity. Strong alignment attracts delegation; poor performance (outliers, missed repairs) risks stake penalties and delegation flight. The transparent, reproducible evaluation criteria (open-source validator logic) eliminate black-box gaming and ensure credibility.

Mechanisms to Discourage Low-Quality or Adversarial Behavior

Multiple overlapping safeguards protect against gaming, laziness, collusion, and malice:

• Yuma Consensus automatically downweights or zeros outliers (malicious validator manipulation or low-effort miners).
• Commit-reveal protocols for miner responses prevent front-running, copying, or fake pre-computed results.
• Reputation decay exponentially reduces task assignment probability for persistent low performance.
• Continuous storage/possession challenges (zk-proofs or hash verification) prove shard retention; repeated failures trigger severe score reduction or ejection.