9 citations · 15 across the 9 of their papers we have counts for
10 papers
ICCU: In-Context Continual Unlearning via Pattern-Induced Refusal Rules
Ruihao Pan, Suhang Wang
Machine unlearning aims to remove the influence of specific data from trained language models. In real-world deployments, unlearning requests often arrive sequentially, which chall…
Slipstream: Trajectory-Grounded Compaction Validation for Long-Horizon Agents
Zhuofu Chen, Rui Pan, Yinwei Dai +1
To cope with the large contexts that long-horizon LLM agents produce, modern frameworks increasingly rely on compaction -- invoking an LLM to rewrite the accumulated trajectory int…
A Comprehensive Evaluation of LLM Unlearning Robustness under Multi-Turn Interaction
Ruihao Pan, Suhang Wang
Machine unlearning aims to remove the influence of specific training data from pre-trained models without retraining from scratch, and is increasingly important for large language…
SpecReason: Fast and Accurate Inference-Time Compute via Speculative Reasoning
Rui Pan, Yinwei Dai, Zhihao Zhang +3
Recent advances in inference-time compute have significantly improved performance on complex tasks by generating long chains of thought (CoTs) using Large Reasoning Models (LRMs).…
METIS: Fast Quality-Aware RAG Systems with Configuration Adaptation
Siddhant Ray, Rui Pan, Zhuohan Gu +5
RAG (Retrieval Augmented Generation) allows LLMs (large language models) to generate better responses with external knowledge, but using more external knowledge often improves gene…
Marconi: Prefix Caching for the Era of Hybrid LLMs
Rui Pan, Zhuang Wang, Zhen Jia +5
Hybrid models that combine the language modeling capabilities of Attention layers with the efficiency of Recurrent layers (e.g., State Space Models) have gained traction in practic…
Optimizing Mixture-of-Experts Inference Time Combining Model Deployment and Communication Scheduling
Jialong Li, Shreyansh Tripathi, Lakshay Rastogi +3
As machine learning models scale in size and complexity, their computational requirements become a significant barrier. Mixture-of-Experts (MoE) models alleviate this issue by sele…
Mowgli: Passively Learned Rate Control for Real-Time Video
Neil Agarwal, Rui Pan, Francis Y. Yan +1
Rate control algorithms are at the heart of video conferencing platforms, determining target bitrates that match dynamic network characteristics for high quality. Recent data-drive…
Apparate: Rethinking Early Exits to Tame Latency-Throughput Tensions in ML Serving
Yinwei Dai, Rui Pan, Anand Iyer +2
Machine learning (ML) inference platforms are tasked with balancing two competing goals: ensuring high throughput given many requests, and delivering low-latency responses to suppo…
Shockwave: Fair and Efficient Cluster Scheduling for Dynamic Adaptation in Machine Learning
Pengfei Zheng, Rui Pan, Tarannum Khan +2
Dynamic adaptation has become an essential technique in accelerating distributed machine learning (ML) training. Recent studies have shown that dynamically adjusting model structur…