Chronos: Temporal-Aware Conversational Agents with Structured Event Retrieval for Long-Term Memory
Sahil Sen, Elias Lumer, Anmol Gulati, Vamse Kumar Subbiah

TL;DR
Chronos introduces a temporal-aware memory system for conversational AI that effectively retrieves and reasons over long-term, time-sensitive dialogue histories, significantly improving accuracy in multi-turn, long-term interactions.
Contribution
The paper presents Chronos, a novel structured event retrieval framework that enhances long-term memory reasoning in LLM-based conversational agents, outperforming previous systems.
Findings
Achieves up to 95.60% accuracy on LongMemEvalS benchmark.
Event calendar component contributes 58.9% to performance gains.
Outperforms prior approaches with various LLMs, including open-source models.
Abstract
Recent advances in Large Language Models (LLMs) have enabled conversational AI agents to engage in extended multi-turn interactions spanning weeks or months. However, existing memory systems struggle to reason over temporally grounded facts and preferences that evolve across months of interaction and lack effective retrieval strategies for multi-hop, time-sensitive queries over long dialogue histories. We introduce Chronos, a novel temporal-aware memory framework that decomposes raw dialogue into subject-verb-object event tuples with resolved datetime ranges and entity aliases, indexing them in a structured event calendar alongside a turn calendar that preserves full conversational context. At query time, Chronos applies dynamic prompting to generate tailored retrieval guidance for each question, directing the agent on what to retrieve, how to filter across time ranges, and how to…
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Taxonomy
TopicsTopic Modeling · Multimodal Machine Learning Applications · Speech and dialogue systems
