Papers by Shiyu Hu
EvoSpark: Endogenous Interactive Agent Societies for Unified Long-Horizon Narrative Evolution (2026.acl-long)
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| Challenge: | Existing systems suffer from social memory stacking and narrative-spatial dissonance . long-horizon narratives suffer from conflicting relational states without resolution . |
| Approach: | They propose a framework to sustain logically coherent long-horizon narratives within endogenous interactive agent societies. |
| Outcome: | Experiments show that the framework outperforms baselines across paradigms. |
Look Less, Reason More: Rollout-Guided Adaptive Pixel-Space Reasoning (2026.acl-long)
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| Challenge: | Recent work has shown promise by incorporating pixel-level visual information into the reasoning process, enabling VLMs to access high-resolution visual details during their thought process. |
| Approach: | They propose a framework that dynamically determines necessary pixel-level operations based on the input query. |
| Outcome: | The proposed model achieves 73.4% accuracy on HR-Bench 4K while maintaining a tool usage ratio of only 20.1%, improving accuracy and reducing tool usage by 66.5% compared to the previous methods. |
From Domains to Instances: Dual-Granularity Data Synthesis for LLM Unlearning (2026.findings-acl)
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Xiaoyu Xu, Minxin Du, Zitong LI, Zi Liang, Zhibiao Guo, Zhang Shiyu, Peizhao Hu, Qingqing Ye, Haibo Hu
| Challenge: | Currently, the evaluation of unlearning is limited due to the lack of granularity in the model. |
| Approach: | They propose a framework for synthesizing high-quality forget sets that exploits the target model per se to elicit data that matches its internal knowledge distribution through seed-guided and adversarial prompting. |
| Outcome: | The proposed framework achieves a superior balance of relevance, diversity, and efficiency across benchmarks. |
Can LLM Watermarks Robustly Prevent Unauthorized Knowledge Distillation? (2025.acl-long)
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| Challenge: | Large Language Model (LLM) watermarking is radioactive and enables the detection of watermarks inherited by student models when trained on the outputs of watermarked teacher models. |
| Approach: | They propose two types of watermark removal attacks that allow student models to perform untraceable knowledge distillation while avoiding watermark inheritance. |
| Outcome: | The proposed attacks eliminate inherited watermarks while maintaining knowledge transfer efficiency and low computational overhead. |
LLMArena: Assessing Capabilities of Large Language Models in Dynamic Multi-Agent Environments (2024.acl-long)
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| Challenge: | Existing benchmarks for evaluating large language models use static datasets, leading to data leakage or overlooking the complexities of multi-agent interactions. |
| Approach: | They propose a framework that evaluates the diverse capabilities of LLM agents in multi-agent dynamic environments. |
| Outcome: | The proposed framework assesses the diverse capabilities of LLM agents in multi-agent dynamic environments. |