Full project description
The Problem Modern AI systems rely on large prompts to improve accuracy. In practice, most of this context is redundant or irrelevant. Developers compensate by sending entire datasets, documentation, or conversation history to the model. This increases costs, reduces performance, and often degrades answer quality due to noise. Furthermore, AI amnesia is a real emerging phenomenon that will affect most of enterprise level LLM infrastructure, we aim to eliminate it. As context windows scale from tens of thousands to millions of tokens, this inefficiency becomes a core limitation. The Solution Carbon is a context optimization subnet that transforms large inputs into high-value signal before they reach the model. Instead of sending raw context, Carbon filters, ranks, and restructures information to preserve what matters while removing irrelevant data. Raw context → Carbon → optimized context → LLM This results in significant token reduction while maintaining or improving answer quality. How it works Carbon leverages the Bittensor architecture: • Miners receive a context and a query and produce a compressed representation • Validators evaluate outputs based on: • accuracy retention • compression ratio • latency Rewards are allocated to miners that maximize useful information per token. This creates a continuous optimization process where compression strategies improve over time. Positioning Quasar focuses on optimizing attention for large context. Carbon addresses the same problem from a different angle by optimizing the input itself. Rather than changing how models process data, Carbon improves what they receive. Use cases Carbon improves performance in: • retrieval-augmented systems where too much data is retrieved • AI agents with long-term memory • systems using large knowledge bases • applications relying on large document or archive access Strategic value Carbon is not a standalone feature. It is an infrastructure layer that improves the efficiency of any AI system using large context. As context sizes grow, optimizing signal-to-noise ratio becomes essential for scalable AI.
Why it works on Bittensor
Carbon is uniquely suited for Bittensor because it transforms context optimization into a competitive, continuously improving process. Miners compete to produce better compression strategies, while validators objectively measure accuracy, efficiency, and latency. We aim to be broadly accessible, allowing participation without requiring high-end training infrastructure, which supports decentralization. Carbon also enables subnet-to-subnet optimization. Outputs from one subnet can be compressed before being consumed by another, improving performance across the ecosystem. Large-scale storage systems rely on cold and archival data that becomes expensive to process with LLMs. Carbon enables efficient access to this data by reducing tokens without compromising quality of context.