LycheeMemory V2: Efficient Long-Term Memory for LLM Agents via Semantic Segment-Level Consolidation

2026-08-13

AI Summary

This work addresses the challenges of high memory construction costs and loss of fine-grained context in long-horizon interactions with large language model (LLM) agents. The authors propose an efficient long-term memory framework based on semantic segment-level integration: multi-turn dialogues are partitioned into segments via semantic boundary detection, then batch-encoded into context-independent, typed memory records. A lightweight structured index enables query-planned retrieval without additional overhead. By operating at the semantic segment level rather than integrating turn-by-turn, this approach substantially reduces LLM invocation frequency while preserving event-level semantics and temporal coherence. Experiments show the framework achieves 89.22% and 92.20% accuracy on LoCoMo and LongMemEval-S benchmarks, respectively, with 86.0% and 75.9% fewer tokens consumed for memory construction compared to A-Mem, all without increasing query costs.

Abstract

Long-horizon LLM agents must preserve information from past interactions to support future tasks. Existing memory systems typically rely on eager consolidation, invoking LLMs after each interaction to extract, summarize, or update memories. This design makes memory construction increasingly costly as conversations grow. Coarse summarization can reduce construction cost but risks discarding fine-grained contextual evidence, whereas larger retrieval contexts or multi-hop LLM reasoning shift the overhead to query time. We present LycheeMemory V2, an efficient long-term memory framework that replaces turn-level consolidation with semantic segment-level consolidation. Instead of consolidating every interaction, LycheeMemory batches multiple exchanges into segments and encodes each finalized segment into context-independent typed memory records. Segment-level batching lowers LLM encoding frequency, while semantic boundary detection helps preserve coherent event-level and temporal evidence compared with fixed-window batching. The resulting records are organized with lightweight structured indexes for query-planned evidence retrieval. Experiments using GPT-4.1-Mini show that LycheeMemory achieves state-of-the-art performance, reaching 89.22% on LoCoMo and 92.20% on LongMemEval-S. Compared with A-Mem, it reduces construction tokens by 86.0% on LoCoMo and 75.9% on LongMemEval-S without increasing query-time token usage. More broadly, our results suggest that the accuracy--cost trade-off of long-term agent memory depends not only on what information is retained, but also on the granularity at which it is consolidated.

Problem

• long-term memory
• LLM agents
• memory consolidation
• semantic segmentation
• efficiency

Innovation

• semantic segment-level consolidation
• long-term memory
• LLM agents
• memory efficiency
• structured indexing