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AI Agents & Chatbots Checked Jul 13, 2026
Google Titans+MIRAS
A neural memory architecture that utilizes surprise-based weight updates to maintain context across virtually infinite sequence lengths.
Best for: Large-scale codebase refactoring and autonomous agents requiring persistent state across million-token contexts.
What you can do with it
- Maintaining global state in multi-repository code migration projects
- Executing long-horizon planning for autonomous agentic workflows
- High-fidelity retrieval from multi-gigabyte document corpora without external vector indexing
Good to know
- Handles massive 2M+ token contexts
- Persistent long-term memory hierarchy
- Reduces need for complex RAG pipelines
Limitations
- High computational resource requirements
- Complex implementation for small teams
- Proprietary architecture limitations
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