Papers by Haowei Fu
HERMES: KV Cache as Hierarchical Memory for Efficient Streaming Video Understanding (2026.acl-long)
Copied to clipboard
| Challenge: | Existing models struggle to maintain stable understanding performance and low GPU memory overhead. |
| Approach: | They propose a training-free architecture for real-time and accurate understanding of video streams . HERMES reuses a compact KV cache, enabling efficient streaming understanding . |
| Outcome: | The proposed architecture achieves 10 faster TTFT compared to prior SOTA. |
Ensemble Privacy Defense for Knowledge-Intensive LLMs against Membership Inference Attacks (2026.findings-eacl)
Copied to clipboard
| Challenge: | Large language models (LLMs) are the foundation of modern natural language processing, powering applications across diverse domains. |
| Approach: | They propose a model-agnostic defense framework which aggregates and evaluates the outputs of a knowledge-injected LLM, a base LLM and a dedicated judge model to enhance resistance against membership inference attacks. |
| Outcome: | The proposed framework reduces MIA success by up to 27.8% for SFT and 526.3% for RAG compared to inference-time baseline while maintaining answer quality. |
Risk-Controlled Event-Driven Cascading Updates for Knowledge Graph Consistency Restoration (2026.findings-acl)
Copied to clipboard
| Challenge: | Knowledge Graphs (KGs) typically treat updates as independent facts . factual, localized updates can contradict and invalidate previously correct knowledge . |
| Approach: | They propose a model-agnostic framework for cascading KG update identification that leverages conformal prediction to provide reliable uncertainty guarantees over the cascade as a whole. |
| Outcome: | The proposed framework provides reliable uncertainty guarantees over the cascade as a whole . it integrates large language models to enrich event representations with world knowledge. |
CoreCodeBench: Decoupling Code Intelligence via Fine-Grained Repository-Level Tasks (2026.acl-long)
Copied to clipboard
Lingyue Fu, Hao Guan, Bolun Zhang, Haowei Yuan, Yaoming Zhu, Lin Qiu, ZongYu Wang, Xuezhi Cao, Xunliang Cai, Weiwen Liu, Weinan Zhang, Yong Yu
| Challenge: | Existing large language models for software engineering rely on coarse-grained pass rates obscuring specific cognitive bottlenecks. |
| Approach: | They propose a repository-level benchmark that dissects coding capabilities through atomized tasks. |
| Outcome: | The proposed framework achieves a 78.55% validity yield, surpassing the 31.7% retention rate of SWE-bench-Verified. |