H-MEM: Hierarchical Memory for High-Efficiency Long-Term Reasoning in LLM Agents (2026.eacl-long)
Copied to clipboard
| Challenge: | Long-term memory is one of the key factors influencing the reasoning capabilities of Large Language Model Agents. |
| Approach: | They propose a hierarchical memory architecture that organizes and updates memory in a multi-level fashion based on the degree of semantic abstraction. |
| Outcome: | The proposed model outperforms baseline methods on five task settings from the LoCoMo dataset. |
Similar Papers
H-Mem: Hybrid Multi-Dimensional Memory Management for Long-Context Conversational Agents (2026.eacl-long)
Copied to clipboard
| Challenge: | Existing frameworks for long-context conversational agents struggle to organize information across dimensions like time and topic, leading to poor retrieval. |
| Approach: | They propose a Hybrid Multi-Dimensional Memory architecture that stores conversational facts in two parallel hierarchical data structures: a temporal tree that organizes information chronologically and a semantic tree that arranges it conceptually. |
| Outcome: | The proposed architecture improves performance on long-context QA datasets by 8.4% compared to current systems. |
HiGMem: A Hierarchical and LLM-Guided Memory System for Long-Term Conversational Agents (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing memory systems rely on vector similarity for retrieval, resulting in bloated evidence sets . existing systems produce little additional recall, but this approach lowers retrieval precision . |
| Approach: | They propose a two-level event-turn memory system that uses event summaries as semantic anchors to predict which related turns are worth reading. |
| Outcome: | The proposed system achieves the best F1 on four of five question categories and improves adversarial F1 from 0.54 to 0.78 over A-Mem while retrieving an order of magnitude fewer turns. |
GAM: Hierarchical Graph-based Agentic Memory for LLM Agents (2026.acl-long)
Copied to clipboard
Zhaofen Wu, Hanrong Zhang, Fulin Lin, Wujiang Xu, Xinran Xu, Yankai Chen, Henry Peng Zou, Shaowen Chen, Weizhi Zhang, Xue Liu, Philip S. Yu, Hongwei Wang
| Challenge: | Current unified stream-based memory systems facilitate context updates but remain vulnerable to interference from transient noise. |
| Approach: | They propose a hierarchical Graph-based Agentic Memory framework that explicitly decouples memory encoding from consolidation to resolve conflict between rapid context perception and stable knowledge retention. |
| Outcome: | The proposed framework outperforms state-of-the-art benchmarks on LoCoMo and LongDialQA. |
Preference-Aware Memory Update for Long-Term LLM Agents (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for integrating long-term memory do not provide dynamic and personalized memory refinement. |
| Approach: | They propose a long-term memory update mechanism that enables dynamic and personalized memory refinement. |
| Outcome: | The proposed mechanism improves the performance of LLM-based agents in five tasks. |
HeLa-Mem: Hebbian Learning and Associative Memory for LLM Agents (2026.acl-long)
Copied to clipboard
| Challenge: | Existing memory systems represent conversation history as unstructured embedding vectors, retrieving information through semantic similarity. |
| Approach: | They propose a bio-inspired memory architecture that models memory as a dynamic graph with Hebbian learning dynamics. |
| Outcome: | The proposed architecture leverages both semantic similarity and learned associations . it can be used to build a bio-inspired memory graph with Hebbian learning dynamics . |
LiCoMemory: Lightweight and Cognitive Agentic Memory for Efficient Long-Term Reasoning (2026.findings-acl)
Copied to clipboard
Zhengjun Huang, Zhoujin Tian, Qintian Guo, Fangyuan Zhang, Yingli Zhou, Di Jiang, Zeying Xie, Xiaofang Zhou
| Challenge: | Large Language Models are constrained by limited context windows and lack of persistent memory . recent efforts address these limitations via external memory architectures . |
| Approach: | They propose an end-to-end agentic memory framework for real-time updating and retrieval that integrates hierarchical and temporal indexing layers. |
| Outcome: | The proposed framework outperforms established benchmarks in temporal reasoning, multi-session consistency, and retrieval efficiency. |
Agentic Memory: Learning Unified Long-Term and Short-Term Memory Management for Large Language Model Agents (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods handle long-term memory (LTM) and short-term (STM) as separate components, relying on heuristics or auxiliary controllers, which limits adaptability and end-to-end optimization. |
| Approach: | They propose a framework that integrates LTM and STM management directly into the agent's policy and propose 'agentic memory' to train such unified behaviors. |
| Outcome: | The proposed framework outperforms strong memory-augmented baselines on five long-horizon benchmarks and achieves higher-quality long-term memory and more efficient context usage. |
Mnemis: Dual-Route Retrieval on Hierarchical Graphs for Long-Term LLM Memory (2026.acl-long)
Copied to clipboard
Zihao Tang, Xin Yu, Ziyu Xiao, Zengxuan Wen, Zelin Li, Jiaxi Zhou, Hualei Wang, Haohua Wang, Haizhen Huang, Weiwei Deng, Feng Sun, Qi Zhang
| Challenge: | Existing methods for retrieving historical messages are based on similarity-based mechanisms. |
| Approach: | They propose a system that integrates System-1 similarity search with a complementary System-2 mechanism, termed Global Selection. |
| Outcome: | The proposed framework achieves state-of-the-art on long-term memory benchmarks and 93.9 on LoCoMo and 91.6 on LongMemEval-S. |
StructMem: Structured Memory for Long-Horizon Behavior in LLMs (2026.acl-short)
Copied to clipboard
| Challenge: | Existing memory systems lack structure and efficiency in capturing relationships between events. |
| Approach: | They propose a structure-enriched hierarchical memory framework that preserves event-level bindings and induces cross-event connections. |
| Outcome: | The proposed framework preserves event-level bindings and induces cross-event connections while reducing token usage, API calls, and runtime compared to prior memory systems. |
HyperMem: Hypergraph Memory for Long-Term Conversations (2026.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to long-term memory management rely on pairwise relations, causing fragmented retrieval. |
| Approach: | They propose a hypergraph-based hierarchical memory architecture that explicitly models high-order associations using hyperedges. |
| Outcome: | Experiments show that HyperMem achieves state-of-the-art performance with 92.73% accuracy for long-term conversations. |