24 citations · 38 across the 24 of their papers we have counts for
12 papers · 1 filter
Rent-Seeking Language Models: Reward Models Set the Prices, Frequent Audits Curb the Rent
Zixiao Wang, Xingbo Du, Duzhen Zhang +1
Reward model overoptimization in RLHF is usually described as reward hacking. We recast it as rent-seeking: the policy, a firm, earns reward from a regulator, the proxy reward mode…
CORAL: Cross-modal Vector Retrieval via Incremental Graph Construction at Scale
Shixin Wan, Guoyu Hu, Yifan Wu +2
Cross-modal vector retrieval is widely used in multimodal systems, such as search engines and vector databases. It typically operates in out-of-distribution (OOD) settings, where q…
QCATS: Query Context-Aware Transformer Slicing for Efficient Predictive Query Processing
Yueying Li, Zhongle Xie, Ke Chen +1
In-database predictive query processing increasingly applies Transformer-based models within relational pipelines. However, existing in-database inference typically exposes only tu…
FastE: Readout-Triggered Token Compression for LLM Embedding Inference
Jinsong Shu, Jinyong Wen, Baokun Wang +4
In this study, we identify depth-dependent prefix redundancy in final-readout LLM embedding models, notably across representative backbones including Qwen3-Embedding and Qwen3-VL-E…
ScaleSense: Cost-Intelligent Scaling Framework via Learned Resource Estimation in Alibaba AnalyticDB
Yifan Wu, Yuhan Li, Zhenhua Wang +6
Cloud-native serverless data warehouses achieve fine-grained elasticity by decoupling storage from compute, yet determining the optimal resource allocation for highly heterogeneous…
Guixu: Valuation-Driven Data Discovery for Autonomous AI Agents with On-Chain Attestation
Yifan Wu, Yuchen Peng, Jiaqi Chai +4
Autonomous agents increasingly rely on external data to complete downstream tasks such as model training and decision support. However, existing data discovery systems remain large…
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…
MM-ShiftKV: Decode-Aware Prefill-Stage KV Selection for Multimodal Large Language Models
Jinsong Shu, Chenyang Wu, Zhongle Xie +2
Key-Value (KV) caching is essential for efficient inference in multimodal large language models (MLLMs), yet its memory footprint grows linearly with context length and becomes a m…
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…
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…