Papers by Shaoning Zeng
H-MEM: Hierarchical Memory for High-Efficiency Long-Term Reasoning in LLM Agents (2026.eacl-long)
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| 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. |
Preference-Aware Memory Update for Long-Term LLM Agents (2026.findings-acl)
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| 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. |
A Dual-Phase Self-Evolution Framework for Large Language Models (2026.findings-acl)
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| Challenge: | Existing strategies to optimize LLMs through pretraining fail to enhance domain cognition. |
| Approach: | They propose a dual-phase self-evolution framework that integrates user preference adaptation and domain-specific competence to optimize LLMs. |
| Outcome: | The proposed framework outperforms Supervised Fine-Tuning, Preference Optimization, and Memory-Augmented baselines on general NLP benchmarks and long-term dialogue tasks. |
Think Parallax: Solving Multi-Hop Problems via Multi-View Knowledge-Graph-Based Retrieval-Augmented Generation (2026.acl-long)
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| Challenge: | Existing KG-RAG systems collapse all reasoning hops into a single representation, flat embedding space, suppressing this implicit structure and causing noisy or drifted path exploration. |
| Approach: | They propose a symmetric multi-view framework that decouples queries and KGs into aligned, head-specific retrieval spaces. |
| Outcome: | The proposed framework achieves state-of-the-art retrieval and QA performance on WebQSP and CWQ, and significantly reduces hallucination. |