Papers by Ziyi Ni
Tree-of-Code: A Self-Growing Tree Framework for End-to-End Code Generation and Execution in Complex Tasks (2025.findings-acl)
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| Challenge: | Effectively and efficiently handling complex realworld problems has become a key focus across industry and academia. |
| Approach: | They propose a tree-of-code framework that generates nodes through self-supervision and combines prompt and model exploration in a GT-free setting. |
| Outcome: | Experiments on two datasets with ten popular zero-shot LLMs show that Tree-of-Code boosts accuracy by nearly 20% over CodeAct with fewer than 1/4 turns. |
SafetyMem: Adaptive Jailbreak Defense via Dual-Component Safety Memory (2026.acl-long)
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| Challenge: | Existing defenses for Large Language Models suffer from a 'memory gap' parameter-modifying methods are computationally expensive and inference-time filters cannot retain or reuse defense knowledge across interactions. |
| Approach: | They propose a framework that secures Large Language Models through a dual-component safety memory system. |
| Outcome: | The proposed framework significantly reduces attack success rates while preserving interpretability and efficiency. |
P-QuASAR: A Unified Probabilistic Framework for Holistic Patent Quality Assessment and Refinement (2026.findings-acl)
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| Challenge: | Existing methods for assessing patent quality rely on modular pipelines or generic detectors, resulting in fragmented decisions and limited integration across quality dimensions. |
| Approach: | They propose a probabilistic framework that represents patent specifications as Quality Graphs. |
| Outcome: | The proposed framework outperforms existing methods on 500 patents against seven baselines. |
TiMem: Temporal-Hierarchical Memory Consolidation for Long-Horizon Conversational Agents (2026.findings-acl)
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Kai Li, Xuanqing Yu, Ziyi Ni, Yi Zeng, Yao Xu, Zheqing Zhang, Xin Li, Jitao Sang, Xiaogang Duan, Xuelei Wang, Chengbao Liu, Jie Tan
| Challenge: | Existing memory frameworks provide limited support for temporally structured information across hierarchical levels, leading to fragmented memories and unstable long-horizon personalization. |
| Approach: | They propose a temporal–hierarchical memory framework that organizes conversations through a Temporal Memory Tree. |
| Outcome: | The proposed framework outperforms baselines while reducing the recalled memory length by 52.20%. |
Mitigating Training Imbalance in LLM Fine-Tuning via Selective Parameter Merging (2024.emnlp-main)
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| Challenge: | Existing studies suggest that the order of training samples can affect model performance, but this is not the case. |
| Approach: | They propose to merge supervised fine-tuning models with different data orders to mitigate this imbalance by parameter merging. |
| Outcome: | The proposed method outperforms the weighted-average method on five datasets. |