TRACE: State-Aware Query Processing over Temporal Evidence Graphs for Conversational Data

2026-06-30

AI Summary

Traditional long-term memory systems struggle to model the temporal evolution of user states and are prone to interference from outdated or contradictory information. This work proposes a Temporal Evidence Graph framework that captures state-aware query processing through a hierarchical structure of events, sessions, and topics, enriched with typed temporal, causal, update, and contradiction relations. The approach integrates vector retrieval with graph-based path reasoning, introduces validity annotations to distinguish historical facts from current states, and designs an update-aware seed node selection mechanism coupled with path-grounded evidence generation. Evaluated on long-conversation question answering benchmarks, the method significantly improves performance in temporal and multi-hop reasoning. Ablation studies confirm the critical contributions of the hierarchical structure, update-aware initialization, and path-grounded evidence formulation.

Abstract

Conversational data is increasingly used as a persistent source of user state for long-running assistants and AI agents. However, querying this data remains challenging because conversations naturally evolve: plans are revised, preferences change, and later messages frequently supersede or contradict earlier information. Existing long-memory pipelines largely treat memories as independent text or vector objects. This approach often retrieves semantically similar but stale evidence, offering limited support for state-aware reasoning. To address this problem, we present TRACE, a query processing framework over temporal evidence graphs for evolving conversational data. TRACE models conversations as a hierarchical graph spanning events, sessions, and topics, enriched with typed temporal, causal, update, and contradiction relations. Crucially, the framework maintains validity annotations so obsolete facts remain accessible for historical queries but are discounted for current-state answers. At query time, TRACE combines vector-based note retrieval with graph-guided evidence search, generating validity-aware support paths and a hybrid context for answer generation. This design separates lexical recall from evidence reconstruction, enabling bounded query-time reasoning over long conversational histories. Experiments on long-conversation query-answering (QA) benchmarks show that TRACE improves temporal and multi-hop reasoning, with ablations highlighting the importance of hierarchy, update-aware seeding, and path-grounded evidence.

Problem

• conversational data
• temporal reasoning
• state-aware querying
• evidence graph
• long-memory systems

Innovation

• temporal evidence graphs
• state-aware query processing
• validity annotation
• hierarchical conversation modeling
• graph-guided reasoning