23 citations · 33 across the 29 of their papers we have counts for
15 papers · 1 filter
Meta Lattice: Model Space Redesign for Cost-Effective Industry-Scale Ads Recommendations
Liang Luo, Yuxin Chen, Zhengyu Zhang +39
The rapidly evolving landscape of products, surfaces, policies, and regulations poses significant challenges for deploying state-of-the-art recommendation models at industry scale,…
Safe2Harm: Semantic Isomorphism Attacks for Jailbreaking Large Language Models
Fan Yang
Large Language Models (LLMs) have demonstrated exceptional performance across various tasks, but their security vulnerabilities can be exploited by attackers to generate harmful co…
MRD: Multi-resolution Retrieval-Detection Fusion for High-Resolution Image Understanding
Fan Yang, Xingping Dong, Xin Yu +3
Understanding high-resolution (HR) images remains a critical challenge for multimodal large language models (MLLMs). Recent approaches leverage vision-based retrieval-augmented gen…
Catastrophic Forgetting in Kolmogorov-Arnold Networks
Mohammad Marufur Rahman, Guanchu Wang, Kaixiong Zhou +2
Catastrophic forgetting is a longstanding challenge in continual learning, where models lose knowledge from earlier tasks when learning new ones. While various mitigation strategie…
FilmSceneDesigner: Chaining Set Design for Procedural Film Scene Generation
Zhifeng Xie, Keyi Zhang, Yiye Yan +4
Film set design plays a pivotal role in cinematic storytelling and shaping the visual atmosphere. However, the traditional process depends on expert-driven manual modeling, which i…
JT-Safe: Intrinsically Enhancing the Safety and Trustworthiness of LLMs
Junlan Feng, Fanyu Meng, Chong Long +12
The hallucination and credibility concerns of large language models (LLMs) are global challenges that the industry is collectively addressing. Recently, a significant amount of adv…
Agent Data Protocol: Unifying Datasets for Diverse, Effective Fine-tuning of LLM Agents
Yueqi Song, Ketan Ramaneti, Zaid Sheikh +18
Public research results on large-scale supervised finetuning of AI agents remain relatively rare, since the collection of agent training data presents unique challenges. In this wo…
What Matters in RL-Based Methods for Object-Goal Navigation? An Empirical Study and A Unified Framework
Hongze Wang, Boyang Sun, Jiaxu Xing +5
Object-Goal Navigation (ObjectNav) is a key capability for deploying mobile robots in everyday environments such as homes, schools, and workplaces. In this task, an agent must loca…
Logics-Parsing Technical Report
Xiangyang Chen, Shuzhao Li, Xiuwen Zhu +7
Recent advances in Large Vision-Language models (LVLM) have spurred significant progress in document parsing task. Compared to traditional pipeline-based methods, end-to-end paradi…
RCR-Router: Efficient Role-Aware Context Routing for Multi-Agent LLM Systems with Structured Memory
Jun Liu, Zhenglun Kong, Changdi Yang +12
Large language model (LLM) agents are increasingly organized into multi-agent systems for complex reasoning and decision-making tasks. However, scaling such systems can substantial…
The Cost of Thinking: Increased Jailbreak Risk in Large Language Models
Fan Yang
Thinking mode has always been regarded as one of the most valuable modes in LLMs. However, we uncover a surprising and previously overlooked phenomenon: LLMs with thinking mode are…
BlueLM-2.5-3B Technical Report
Baojiao Xiong, Boheng Chen, Chengzhi Wang +58
We present BlueLM-2.5-3B, a compact and unified dense Multimodal Large Language Model (MLLM) designed for efficient edge-device deployment, offering strong general-purpose and reas…
GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning
V Team, Wenyi Hong, Wenmeng Yu +90
We present GLM-4.1V-Thinking, GLM-4.5V, and GLM-4.6V, a family of vision-language models (VLMs) designed to advance general-purpose multimodal understanding and reasoning. In this…
Research on Graph-Retrieval Augmented Generation Based on Historical Text Knowledge Graphs
Yang Fan, Zhang Qi, Xing Wenqian +2
This article addresses domain knowledge gaps in general large language models for historical text analysis in the context of computational humanities and AIGC technology. We propos…
The Amazon Nova Family of Models: Technical Report and Model Card
Amazon AGI, Aaron Langford, Aayush Shah +783
We present Amazon Nova, a new generation of state-of-the-art foundation models that deliver frontier intelligence and industry-leading price performance. Amazon Nova Pro is a highl…