Cross-Conversation Continuity

Cross-Conversation Continuity enables AI systems to maintain context awareness across multiple sessions without requiring users to re-explain background information. Traditional chat interfaces treat conversations as isolated sessions, losing context when conversations end. Mavaia's Cross-Conversation Continuity implements three mechanisms: Conversation Summarization (distilling key information into persistent summaries), Digest Systems (structured capture of decisions, commitments, and open questions), and Memory Integration (semantic retrieval of relevant past context when new sessions begin). The system operates transparently, surfacing past context when relevant rather than overwhelming users with all historical information.

Cross-conversation continuity implements four-stage processing. First, Session Boundary Detection identifies conversation endpoints using time gaps (>30 minutes), explicit closure commands, or workspace switches. Second, Conversation Summarization analyzes completed sessions to extract key information: main topics discussed, decisions made, action items identified, unresolved questions. Summary generation uses local model with conversation transcript as context, producing 100-300 word distillations. Third, Structured Digest Creation captures commitments ('I'll research X'), preferences ('I prefer Y format'), and context ('working on Z project'). Fourth, Semantic Retrieval queries summarized conversations when new sessions begin, surfacing relevant past context based on topic similarity to recent messages.

Continuity evaluation across 1,500 multi-session conversations showed 78% successful context transfer validated by users not needing to re-explain background. Summarization quality: 84% of generated summaries captured key information, average compression ratio 12:1 (conversation to summary length). Digest extraction: 73% accuracy for commitment detection, 81% for preference identification. Semantic retrieval: 78% of surfaced past conversations judged relevant to current session. Context transfer latency: <200ms to retrieve and inject relevant summaries when starting new sessions. User experience metrics: 41% reduction in context re-explanation messages, 23% faster task continuation across sessions.

Cross-Conversation Continuity demonstrates that conversational context can persist across sessions through summarization and semantic retrieval. The 78% context transfer success rate proves the approach is effective for most continuation scenarios, though not perfect. The 12:1 compression ratio validates that conversations contain significant redundancy that summarization effectively eliminates. The 73-81% extraction accuracy for structured information (commitments, preferences) shows moderate reliability - good enough to surface relevant context but requiring user validation for critical information. The 41% reduction in re-explanation and 23% faster continuation quantify tangible productivity benefits. The <200ms retrieval latency ensures continuity doesn't create noticeable delays when starting sessions.

Current limitations include: (1) Session boundary detection may incorrectly segment long work periods with brief pauses, (2) Summarization quality varies by conversation complexity - multi-topic sessions challenge 300-word limit, (3) Structured digest extraction misses implicit commitments or preferences not explicitly stated, (4) Semantic retrieval limited to past conversation context without external knowledge base integration, (5) The system doesn't explicitly model conversation dependency chains (session B builds on session A building on session C), (6) Summary compression loses nuanced details that may become relevant later, (7) Cross-workspace continuity not yet implemented, preventing context transfer across project boundaries.

Cross-Conversation Continuity provides persistent memory across chat sessions through summarization and semantic retrieval. The 78% context transfer success and 41% reduction in re-explanation validate that conversational memory can extend beyond individual sessions. The framework enables C-LLMs to maintain long-term working relationships with users rather than treating each conversation as isolated interaction. Future work will focus on improved session boundary detection, hierarchical summarization for complex multi-topic conversations, conversation dependency modeling, cross-workspace continuity, and external knowledge integration for richer context beyond past conversations.