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3 months ago
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I am very pleased to introduce ContextSphere, a new query language model architecture and implementation.
The primary design goal and ultimate success of ContextSphere is that it is designed to support infinite context. While most large language models are constrained by fixed context windows—typically ranging from a few thousand to a few hundred thousand tokens—ContextSphere introduces a paradigm shift: it is able to handle unbounded quantities of text without segmentation or truncation. This enables seamless operation across entire books, extensive code repositories, long-term conversations, and large-scale enterprise documents.
Modelling Methodology
ContextSphere is not reliant on chunked retrieval or summarization techniques. Instead, it employs an unique “zero-attention” mechanism that preserves equal levels of coherence and relevance across arbitrarily long sequences of tokens. Users can reference earlier material—whether it appears ten pages back or at the beginning of a multi-document corpus—with the confidence that the model has lost no awareness of prior context. This guarantees that there is no diminishing of quality on tasks that require deep integration across temporally or structurally distant information.
The model is designed as query-first: It takes two inputs, a query string and a corpus. (In the model implementation included on this page, the corpus is always plaintext, but that is only a limitation of this representative example.) The query and the corpus together form the input via two separate streams which are treated with equivalent attention within the model.
Further details on ContextSphere 8B can be found in the white paper No Attention Is All You Need, soon to be published on ArXiv and, later, probably at NeurIPS.
Benefits of the ContextSphere Architecture
The primary benefits of this approach include:
- Query-First, Answer-First—The fuzzy dual-state nature of the input-output mechanism means the summarisation syntax is consistent, easy to follow, and machine-parseable.
- Scalablity—The infinite-context streaming mechanism scales to any length of corpus without additional drain on resident memory or model size, resulting in (at minimum) linear-factor memory residency over existing SOTA models.
- Speed—while the model on this page has been rate-limited to preserve availability, we believe that this model will outperform SOTA on any dual-state query tasks in terms of speed.
This architecture unlocks new capabilities across a wide range of applications, from legal and technical analysis to long-horizon planning and customer support automation.
Unbounded access to the ContextSphere 8B model is now available in private beta, and we invite organizations working with complex or large-scale language data to explore its potential with us. Enterprise plans start from $8500 per seat per month with discounts for bulk. Please direct all business communications to [email protected]. Please note that by contacting us at this address you agree for future iterations of ContextSphere to be trained on the content of such communications and that your requests for engagement will first be appraised by the current iteration of ContextSphere.
ContextSphere 8B is not affiliated with Mattel, Inc.
Playground
You can test the first public version of ContextSphere 8B below: just provide a corpus of text and a Boolean query to perform on that text. The input will then be sent to the server and the model’s Fuzzy Dual-State Response will be provided.
