1 citations · 1 across the 16 of their papers we have counts for
18 papers
Towards a Densing Law for User Representation Learning at Billion-Scale Capacity
Bin Dou, Junru Zhang, Zhaoyi Yuan +6
User representation learning in real-world industrial scenarios is commonly scaled by increasing user amount, behavioral sequence length and model size. However, existing methods f…
HARD-KV: Head-Adaptive Regularization for Decoding-time KV Compression
Yuxuan Yang, Feiyang Ren, Bowen Zeng +4
Long-context LLM inference faces a fundamental conflict: head-adaptive compression algorithms (e.g., Top- nucleus sampling) offer superior accuracy by dynamically fluctuating me…
LATTEArena: An Evaluation Framework for LLM-powered Tabular Feature Engineering (Extended Version)
Ankai Hao, Ke Chen, Huan Li +1
Feature engineering remains a cornerstone of tabular data analysis, and Large Language Models (LLMs) have emerged as a promising paradigm for its automation, giving rise to LLM-pow…
MedMemoryBench: Benchmarking Agent Memory in Personalized Healthcare
Yihao Wang, Haoran Xu, Renjie Gu +10
The large-scale deployment of personalized healthcare agents demands memory mechanisms that are exceptionally precise, safe, and capable of long-term clinical tracking. However, ex…
Forcing-KV: Hybrid KV Cache Compression for Efficient Autoregressive Video Diffusion Models
Yicheng Ji, Zhizhou Zhong, Jun Zhang +7
Autoregressive (AR) video diffusion models adopt a streaming generation framework, enabling long-horizon video generation with real-time responsiveness, as exemplified by the Self…
Token Economics for LLM Agents: A Dual-View Study from Computing and Economics
Yuxi Chen, Junming Chen, Chenyu He +9
As LLM agents evolve, tokens have emerged as the core economic primitives of Agentic AI. However, their exponential consumption introduces severe computational, collaborative, and…
Efficient Inference for Large Vision-Language Models: Bottlenecks, Techniques, and Prospects
Jun Zhang, Yicheng Ji, Feiyang Ren +7
Large Vision-Language Models (LVLMs) enable sophisticated reasoning over images and videos, yet their inference is hindered by a systemic efficiency barrier known as visual token d…
See the Forest for the Trees: Loosely Speculative Decoding via Visual-Semantic Guidance for Efficient Inference of Video LLMs
Yicheng Ji, Jun Zhang, Jinpeng Chen +4
Video Large Language Models (Video-LLMs) excel in video understanding but suffer from high inference latency during autoregressive generation. Speculative Decoding (SD) mitigates t…
HybridKV: Hybrid KV Cache Compression for Efficient Multimodal Large Language Model Inference
Bowen Zeng, Feiyang Ren, Jun Zhang +4
Multimodal Large Language Models (MLLMs) have advanced unified reasoning over text, images, and videos, but their inference is hindered by the rapid growth of key-value (KV) caches…
RedParrot: Accelerating NL-to-DSL for Business Analytics via Query Semantic Caching
Tong Wang, Yongqin Xu, Jianfeng Zhang +6
Recently, at Xiaohongshu, the rapid expansion of e-commerce and advertising demands real-time business analytics with high accuracy and low latency. To meet this demand, systems ty…
SpecVLM: Enhancing Speculative Decoding of Video LLMs via Verifier-Guided Token Pruning
Yicheng Ji, Jun Zhang, Heming Xia +4
Video large language models (Vid-LLMs) have shown strong capabilities in understanding video content. However, their reliance on dense video token representations introduces substa…
TableCopilot: A Table Assistant Empowered by Natural Language Conditional Table Discovery
Lingxi Cui, Guanyu Jiang, Huan Li +3
The rise of LLM has enabled natural language-based table assistants, but existing systems assume users already have a well-formed table, neglecting the challenge of table discovery…
HMI: Hierarchical Knowledge Management for Efficient Multi-Tenant Inference in Pretrained Language Models
Jun Zhang, Jue Wang, Huan Li +6
The significant computational demands of pretrained language models (PLMs), which often require dedicated hardware, present a substantial challenge in serving them efficiently, esp…
NLCTables: A Dataset for Marrying Natural Language Conditions with Table Discovery
Lingxi Cui, Huan Li, Ke Chen +2
With the growing abundance of repositories containing tabular data, discovering relevant tables for in-depth analysis remains a challenging task. Existing table discovery methods p…
Train Small, Infer Large: Memory-Efficient LoRA Training for Large Language Models
Jun Zhang, Jue Wang, Huan Li +6
Large Language Models (LLMs) have significantly advanced natural language processing with exceptional task generalization capabilities. Low-Rank Adaption (LoRA) offers a cost-effec…
KcMF: A Knowledge-compliant Framework for Schema and Entity Matching with Fine-tuning-free LLMs
Yongqin Xu, Huan Li, Ke Chen +1
Schema matching (SM) and entity matching (EM) tasks are crucial for data integration. While large language models (LLMs) have shown promising results in these tasks, they suffer fr…
Tabular Data Augmentation for Machine Learning: Progress and Prospects of Embracing Generative AI
Lingxi Cui, Huan Li, Ke Chen +2
Machine learning (ML) on tabular data is ubiquitous, yet obtaining abundant high-quality tabular data for model training remains a significant obstacle. Numerous works have focused…
Revisiting CNNs for Trajectory Similarity Learning
Zhihao Chang, Linzhu Yu, Huan Li +3
Similarity search is a fundamental but expensive operator in querying trajectory data, due to its quadratic complexity of distance computation. To mitigate the computational burden…