CAMEL: Collaborative Agentic Memory via Evidence Linking across Speakers and Time

Agents in collaborative settings must answer questions using evidence distributed across speakers and time. Participants use different terms for shared concepts, revise decisions, and contribute complementary information across threads. These challenges motivate evidence assembly: retrieving and organizing related messages while preserving the context needed to interpret them. We introduce CAMEL, a collaborative memory framework combining an offline typed graph with a query-time assembly pipeline. The graph retains attributed messages and records reply, decision, and term relations without generative LLM calls at ingestion. At query time, CAMEL seeds candidates through query decomposition and hybrid retrieval, links related evidence through graph expansion and bidirectional contextual term alignment, grounds messages in speaker and temporal context, and selects an evidence window with chronological ordering where appropriate. Across EverMemBench, GroupMemBench, and SocialMemBench, CAMEL improves answer accuracy over the strongest of ten baselines by $12.5$, $11.4$, and $11.7$ percentage points. Analyses of components, answer models, and evidence budgets support jointly considering evidence coverage, attribution, and temporal context across collaborative settings.