2 citations · 5 across the 18 of their papers we have counts for
19 papers
Do Agent Benchmarks Measure Capability? Protocol Validity in the Age of Agentic AI
Jiaqi Shao, Hanck Chen, Wei Zhang +2
Agent benchmarks increasingly evaluate repository editing, web research, terminal use, and long-horizon interaction. Their scores support capability claims only when the evaluation…
Stochastic Reset Pathfinding: Path-Level Regret for Cascading Bandits over Graph Paths
Guni Sharon, Wei Zhang
We introduce Stochastic Reset Pathfinding (SRP), an episodic learning problem on a known directed graph with unknown stationary edge success probabilities. In each episode, the age…
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…
PhoStream: Benchmarking Real-World Streaming for Omnimodal Assistants in Mobile Scenarios
Xudong Lu, Huankang Guan, Yang Bo +10
Multimodal Large Language Models excel at offline audio-visual understanding, but their ability to serve as mobile assistants in continuous real-world streams remains underexplored…
JavisGPT: A Unified Multi-modal LLM for Sounding-Video Comprehension and Generation
Kai Liu, Jungang Li, Yuchong Sun +13
This paper presents JavisGPT, the first unified multimodal large language model (MLLM) for joint audio-video (JAV) comprehension and generation. JavisGPT has a concise encoder-LLM-…
FoldAct: Efficient and Stable Context Folding for Long-Horizon Search Agents
Jiaqi Shao, Yufeng Miao, Wei Zhang +1
Long-horizon reinforcement learning (RL) for large language models faces critical scalability challenges from unbounded context growth, leading to context folding methods that comp…
InSight-o3: Empowering Multimodal Foundation Models with Generalized Visual Search
Kaican Li, Lewei Yao, Jiannan Wu +7
The ability for AI agents to "think with images" requires a sophisticated blend of reasoning and perception. However, current open multimodal agents still largely fall short on the…
MEPIC: Memory Efficient Position Independent Caching for LLM Serving
Qian Wang, Zahra Yousefijamarani, Morgan Lindsay Heisler +8
Modern LLM applications such as deep-research assistants, coding agents, and Retrieval-Augmented Generation (RAG) systems, repeatedly process long prompt histories containing share…
SERL: Self-Examining Reinforcement Learning on Open-Domain
Weixuan Ou, Yanzhao Zheng, Shuoshuo Sun +7
Reinforcement Learning (RL) has been shown to improve the capabilities of large language models (LLMs). However, applying RL to open-domain tasks faces two key challenges: (1) the…
MIRAD - A comprehensive real-world robust anomaly detection dataset for Mass Individualization
Pulin Li, Guocheng Wu, Li Yin +3
Social manufacturing leverages community collaboration and scattered resources to realize mass individualization in modern industry. However, this paradigm shift also introduces su…
BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining
Jie Hao, Rui Yu, Wei Zhang +3
Effective data selection is essential for pretraining large language models (LLMs), enhancing efficiency and improving generalization to downstream tasks. However, existing approac…
SlimPack: Fine-Grained Asymmetric Packing for Balanced and Efficient Variable-Length LLM Training
Yuliang Liu, Guohao Wu, Shenglong Zhang +4
The efficient distributed training of Large Language Models (LLMs) is severely hampered by the extreme variance in context lengths. This data heterogeneity, amplified by convention…
VDEGaussian: Video Diffusion Enhanced 4D Gaussian Splatting for Dynamic Urban Scenes Modeling
Yuru Xiao, Zihan Lin, Chao Lu +7
Dynamic urban scene modeling is a rapidly evolving area with broad applications. While current approaches leveraging neural radiance fields or Gaussian Splatting have achieved fine…
MMReason: An Open-Ended Multi-Modal Multi-Step Reasoning Benchmark for MLLMs Toward AGI
Huanjin Yao, Jiaxing Huang, Yawen Qiu +9
Reasoning plays a crucial role in advancing Multimodal Large Language Models (MLLMs) toward Artificial General Intelligence. However, existing MLLM benchmarks often fall short in p…
Temporal-IRL: Modeling Port Congestion and Berth Scheduling with Inverse Reinforcement Learning
Guo Li, Zixiang Xu, Wei Zhang +5
Predicting port congestion is crucial for maintaining reliable global supply chains. Accurate forecasts enableimprovedshipment planning, reducedelaysand costs, and optimizeinventor…
Exploring Rewriting Approaches for Different Conversational Tasks
Md Mehrab Tanjim, Ryan A. Rossi, Mike Rimer +9
Conversational assistants often require a question rewriting algorithm that leverages a subset of past interactions to provide a more meaningful (accurate) answer to the user's que…
Predicting Human Brain States with Transformer
Yifei Sun, Mariano Cabezas, Jiah Lee +4
The human brain is a complex and highly dynamic system, and our current knowledge of its functional mechanism is still very limited. Fortunately, with functional magnetic resonance…
Fortifying Ethical Boundaries in AI: Advanced Strategies for Enhancing Security in Large Language Models
Yunhong He, Jianling Qiu, Wei Zhang +1
Recent advancements in large language models (LLMs) have significantly enhanced capabilities in natural language processing and artificial intelligence. These models, including GPT…
Native Language Identification with Large Language Models
Wei Zhang, Alexandre Salle
We present the first experiments on Native Language Identification (NLI) using LLMs such as GPT-4. NLI is the task of predicting a writer's first language by analyzing their writin…