15 citations · 18 across the 15 of their papers we have counts for
15 papers
Understanding Dynamic Compute Allocation in Recurrent Transformers
Ibraheem Muhammad Moosa, Suhas Lohit, Ye Wang +2
Token-level adaptive computation seeks to reduce inference cost by allocating more computation to harder tokens and less to easier ones. However, prior work is primarily evaluated…
ERNIE 5.0 Technical Report
Haifeng Wang, Hua Wu, Tian Wu +432
In this report, we introduce ERNIE 5.0, a natively autoregressive foundation model desinged for unified multimodal understanding and generation across text, image, video, and audio…
PretrainRL: Alleviating Factuality Hallucination of Large Language Models at the Beginning
Langming Liu, Kangtao Lv, Haibin Chen +8
Large language models (LLMs), despite their powerful capabilities, suffer from factual hallucinations where they generate verifiable falsehoods. We identify a root of this issue: t…
Comparative Study of Large Language Models on Chinese Film Script Continuation: An Empirical Analysis Based on GPT-5.2 and Qwen-Max
Yuxuan Cao, Zida Yang, Ye Wang
As large language models (LLMs) are increasingly applied to creative writing, their performance on culturally specific narrative tasks warrants systematic investigation. This study…
Fine-tuning Small Language Models as Efficient Enterprise Search Relevance Labelers
Yue Kang, Zhuoyi Huang, Benji Schussheim +19
In enterprise search, building high-quality datasets at scale remains a central challenge due to the difficulty of acquiring labeled data. To resolve this challenge, we propose an…
PhraseVAE and PhraseLDM: Latent Diffusion for Full-Song Multitrack Symbolic Music Generation
Longshen Ou, Ye Wang
This technical report presents a new paradigm for full-song symbolic music generation. Existing symbolic models operate on note-attribute tokens and suffer from extremely long sequ…
Janus: Disaggregating Attention and Experts for Scalable MoE Inference
Zhexiang Zhang, Ye Wang, Yumiao Zhao +9
Serving large Mixture-of-Experts (MoE) models is challenging because of their large memory footprints, heterogeneous resource demands, and highly dynamic inference workloads. Most…
TS-PEFT: Unveiling Token-Level Redundancy in Parameter-Efficient Fine-Tuning
Dabiao Ma, Ziming Dai, Zhimin Xin +3
Current Parameter-Efficient Fine-Tuning (PEFT) methods typically operate under an implicit assumption: Once a target module is selected, every token passing through it contributes…
LouisKV: Efficient KV Cache Retrieval for Long Input-Output Sequences
Wenbo Wu, Qingyi Si, Xiurui Pan +2
While Key-Value (KV) cache succeeds in reducing redundant computations in auto-regressive models, it introduces significant memory overhead, limiting its practical deployment in lo…
OWL: Overcoming Window Length-Dependence in Speculative Decoding for Long-Context Inputs
Jaeseong Lee, seung-won hwang, Aurick Qiao +3
Speculative decoding promises faster inference for large language models (LLMs), yet existing methods fail to generalize to real-world settings. Benchmarks typically assume short c…
DeepPHY: Benchmarking Agentic VLMs on Physical Reasoning
Xinrun Xu, Pi Bu, Ye Wang +7
Although Vision Language Models (VLMs) exhibit strong perceptual abilities and impressive visual reasoning, they struggle with attention to detail and precise action planning in co…
Arctic Inference with Shift Parallelism: Fast and Efficient Open Source Inference System for Enterprise AI
Samyam Rajbhandari, Mert Hidayetoglu, Aurick Qiao +5
Inference is now the dominant AI workload, yet existing systems force trade-offs between latency, throughput, and cost. Arctic Inference, an open-source vLLM plugin from Snowflake…
Chain-of-Thought Driven Adversarial Scenario Extrapolation for Robust Language Models
Md Rafi Ur Rashid, Vishnu Asutosh Dasu, Ye Wang +2
Large Language Models (LLMs) exhibit impressive capabilities, but remain susceptible to a growing spectrum of safety risks, including jailbreaks, toxic content, hallucinations, and…
EAGER-LLM: Enhancing Large Language Models as Recommenders through Exogenous Behavior-Semantic Integration
Minjie Hong, Yan Xia, Zehan Wang +8
Large language models (LLMs) are increasingly leveraged as foundational backbones in the development of advanced recommender systems, offering enhanced capabilities through their e…
Structured Multi-Track Accompaniment Arrangement via Style Prior Modelling
Jingwei Zhao, Gus Xia, Ziyu Wang +1
In the realm of music AI, arranging rich and structured multi-track accompaniments from a simple lead sheet presents significant challenges. Such challenges include maintaining tra…