How an AI system notices which user goals, constraints, files, decisions, and tool results still matter after the conversation has moved on.
Clairos Research
AI research for extended context awareness.
Clairos studies how practical AI systems keep the right context alive across long sessions: what should be remembered, what should be retrieved, what should be ignored, and how tools stay coherent as work unfolds.
The work is applied and behavior-focused. We care less about spectacle and more about whether a model can stay grounded, useful, and honest when the session gets long.
How memory can support continuity without becoming a junk drawer: compact, revisable, attributable, and careful about what should not be stored.
Research lanes
Where the work is pointed.
The research program centers on extended context awareness: the discipline of keeping useful context available without flooding the model, the user, or the toolchain.
How an AI system notices which user goals, constraints, files, decisions, and tool results still matter after the conversation has moved on.
How memory can support continuity without becoming a junk drawer: compact, revisable, attributable, and careful about what should not be stored.
How retrieval should bring in the right evidence at the right time, cite its source, and make uncertainty visible instead of filling gaps with confident noise.
How models carry intent across tools, files, admin surfaces, builds, deployments, and long-running operations without losing the thread.
How to test behavior across realistic multi-step work: context drift, repeated corrections, stale assumptions, hidden dependencies, and recovery after mistakes.
Applied direction
The goal is clearer model behavior, not bigger claims.
Clairos research is designed to feed real products and operations. The output may be a paper, an evaluation note, a system prompt pattern, a product decision, or a safer admin workflow.
A useful system should remember enough to help, but not so much that it becomes invasive, noisy, or impossible to audit.
Important context should be legible: where it came from, why it was used, and what assumptions it created.
When a model moves between APIs, files, browsers, and admin actions, the user should not have to repeatedly reassemble the plan.
Related paths
Use the right surface for the next step.
Open the blog for longer notes, product pages for current app truth, or contact when a research question needs a direct answer.
Papers
Published papers and PDFs.
Formal papers will appear here when they are published from Clairos HQ. Draft and archived papers stay private until they are intentionally released.
This research hub is ready for papers, but nothing has been published publicly yet. When a PDF is marked published in admin, it will appear here automatically.
Next
Turn research into clearer AI behavior.
Open the blog for longer notes, or get in touch if you want to talk about extended context awareness, applied evaluation, or practical model behavior.