# Problem Statement

The proliferation of Tiny LLMs, downloaded and run on millions of edge devices, has created a fundamental challenge: hyper-personalization and continuous fine-tuning of these small models is computationally infeasible for most users. Each Tiny LLM faces knowledge gaps, context fragmentation, and high costs associated with maintaining up-to-date, relevant knowledge bases. The current AI ecosystem lacks scalable mechanisms to empower Tiny LLMs with fresh, verified knowledge without constant dependence on cloud-based inference.

EchoLM analysis indicates that 60% of user queries exhibit semantically similar counterparts in historical data.

# Key Challenges

• Static and outdated models: Once deployed, Tiny LLMs quickly become stale, as they cannot natively update their knowledge base or integrate new information without retraining.
• Hyper-personalization of millions of Tiny LLM instances is unscalable
• Hallucinated or unverifiable SLM answers.
• Costly and centralized fine-tuning: Existing methods for updating models, such as LoRA or QLoRA fine-tuning, require cloud GPUs, expert intervention, and significant time and money.
• Dependency on Big LLM APIs: Users and apps frequently fall back on Big LLM APIs (GPT, Claude, Gemini) to access fresh or complex knowledge, incurring high costs and introducing privacy, latency, and control issues.
• Absence of a shared, reusable knowledge memory: Tiny LLMs operate in isolation, lacking access to a federated, verified, and continuously growing knowledge layer they can rely on.
• No incentive model for community-driven knowledge distillation.