7 citations · 13 across the 15 of their papers we have counts for
16 papers
VideoVIBE: A Video-Grounded Diagnostic Benchmark for One-Shot Interactive Website Generation
Jiajun Xu, Yanghao Zhou, Jingyun Liao +6
Natural-language-driven "vibe coding" enables the one-shot generation of visually rich and interactive web applications, yet reliable assessment of their quality has not kept pace.…
Diverse Thinking Schemata Elicit Better Reasoning in Large Language Models
Xinyue Liang, Yizhe Yang, Yu Bai +3
Large reasoning models (LRMs) have attracted increasing attention for their ability to solve complex mathematical problems by generating extended reasoning chains. In this work, we…
TrustMargin: Training-Free Arbitration between Parametric Memory and Retrieved Evidence in Large Language Models
Jingyan Xu, Hong Shi, Yi Shan +4
Large language models answer knowledge-intensive questions using both parametric memory and retrieved evidence, but neither source is uniformly reliable. Retrieval can fill knowled…
MTAVG-Bench 2.0: Diagnosing Failure Modes of Cinematic Expressiveness in Multi-Talker Audio-Video Generation
Haitian Li, Yanghao Zhou, Heyan Huang +15
In recent years, Multi-Talker Audio-Video Generation (MTAVG) models have shown promising performance on fundamental metrics such as lip-sync and audio-visual alignment. However, th…
ExoActor: Exocentric Video Generation as Generalizable Interactive Humanoid Control
Yanghao Zhou, Jingyu Ma, Yibo Peng +3
Humanoid control systems have made significant progress in recent years, yet modeling fluent interaction-rich behavior between a robot, its surrounding environment, and task-releva…
EgoActor: Grounding Task Planning into Spatial-aware Egocentric Actions for Humanoid Robots via Visual-Language Models
Yu Bai, MingMing Yu, Chaojie Li +3
Deploying humanoid robots in real-world settings is fundamentally challenging, as it demands tight integration of perception, locomotion, and manipulation under partial-information…
EduBench: A Comprehensive Benchmarking Dataset for Evaluating Large Language Models in Diverse Educational Scenarios
Bin Xu, Yu Bai, Huashan Sun +10
As large language models continue to advance, their application in educational contexts remains underexplored and under-optimized. In this paper, we address this gap by introducing…
SoLoPO: Unlocking Long-Context Capabilities in LLMs via Short-to-Long Preference Optimization
Huashan Sun, Shengyi Liao, Yansen Han +8
Despite advances in pretraining with extended context sizes, large language models (LLMs) still face challenges in effectively utilizing real-world long-context information, primar…
Being-0: A Humanoid Robotic Agent with Vision-Language Models and Modular Skills
Haoqi Yuan, Yu Bai, Yuhui Fu +6
Building autonomous robotic agents capable of achieving human-level performance in real-world embodied tasks is an ultimate goal in humanoid robot research. Recent advances have ma…
How Far Can In-Context Alignment Go? Exploring the State of In-Context Alignment
Heyan Huang, Yinghao Li, Huashan Sun +2
Recent studies have demonstrated that In-Context Learning (ICL), through the use of specific demonstrations, can align Large Language Models (LLMs) with human preferences known as…
CItruS: Chunked Instruction-aware State Eviction for Long Sequence Modeling
Yu Bai, Xiyuan Zou, Heyan Huang +4
Long sequence modeling has gained broad interest as large language models (LLMs) continue to advance. Recent research has identified that a large portion of hidden states within th…
Identifying and Analyzing Performance-Critical Tokens in Large Language Models
Yu Bai, Heyan Huang, Cesare Spinoso-Di Piano +4
In-context learning (ICL) has emerged as an effective solution for few-shot learning with large language models (LLMs). However, how LLMs leverage demonstrations to specify a task…
PSP: Pre-trained Soft Prompts for Few-Shot Abstractive Summarization
Xiaochen Liu, Yang Gao, Yu Bai +4
Few-shot abstractive summarization has become a challenging task in natural language generation. To support it, we designed a novel soft prompts architecture coupled with a prompt…
DePA: Improving Non-autoregressive Machine Translation with Dependency-Aware Decoder
Jiaao Zhan, Qian Chen, Boxing Chen +3
Non-autoregressive machine translation (NAT) models have lower translation quality than autoregressive translation (AT) models because NAT decoders do not depend on previous target…
Unifying Cross-lingual Summarization and Machine Translation with Compression Rate
Yu Bai, Heyan Huang, Kai Fan +5
Cross-Lingual Summarization (CLS) is a task that extracts important information from a source document and summarizes it into a summary in another language. It is a challenging tas…
Cross-Lingual Abstractive Summarization with Limited Parallel Resources
Yu Bai, Yang Gao, Heyan Huang
Parallel cross-lingual summarization data is scarce, requiring models to better use the limited available cross-lingual resources. Existing methods to do so often adopt sequence-to…