4 citations · 10 across the 14 of their papers we have counts for
14 papers
ClawForge: Generating Executable Interactive Benchmarks for Command-Line Agents
Yuxiang Lai, Peng Xia, Haonian Ji +8
Interactive agent benchmarks face a tension between scalable construction and realistic workflow evaluation. Hand-authored tasks are expensive to extend and revise, while static pr…
AudioMosaic: Contrastive Masked Audio Representation Learning
Hanxun Huang, Qizhou Wang, Xingjun Ma +3
Audio self-supervised learning (SSL) aims to learn general-purpose representations from large-scale unlabeled audio data. While recent advances have been driven mainly by generativ…
EvolveMem:Self-Evolving Memory Architecture via AutoResearch for LLM Agents
Jiaqi Liu, Xinyu Ye, Peng Xia +4
Long-term memory is essential for LLM agents that operate across multiple sessions, yet existing memory systems treat retrieval infrastructure as fixed: stored content evolves whil…
CAST: Modeling Visual State Transitions for Consistent Video Retrieval
Yanqing Liu, Yingcheng Liu, Fanghong Dong +3
As video content creation shifts toward long-form narratives, composing short clips into coherent storylines becomes increasingly important. However, prevailing retrieval formulati…
In-Context Reinforcement Learning for Tool Use in Large Language Models
Yaoqi Ye, Yiran Zhao, Keyu Duan +4
While large language models (LLMs) exhibit strong reasoning abilities, their performance on complex tasks is often constrained by the limitations of their internal knowledge. A com…
SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning
Peng Xia, Jianwen Chen, Hanyang Wang +10
Large Language Model (LLM) agents have shown stunning results in complex tasks, yet they often operate in isolation, failing to learn from past experiences. Existing memory-based m…
ViT-5: Vision Transformers for The Mid-2020s
Feng Wang, Sucheng Ren, Tiezheng Zhang +4
This work presents a systematic investigation into modernizing Vision Transformer backbones by leveraging architectural advancements from the past five years. While preserving the…
Reliable and Responsible Foundation Models: A Comprehensive Survey
Xinyu Yang, Junlin Han, Rishi Bommasani +49
Foundation models, including Large Language Models (LLMs), Multimodal Large Language Models (MLLMs), Image Generative Models (i.e, Text-to-Image Models and Image-Editing Models), a…
Spiral RoPE: Rotate Your Rotary Positional Embeddings in the 2D Plane
Haoyu Liu, Sucheng Ren, Tingyu Zhu +5
Rotary Position Embedding (RoPE) is the de facto positional encoding in large language models due to its ability to encode relative positions and support length extrapolation. When…
A Definition of AGI
Dan Hendrycks, Dawn Song, Christian Szegedy +30
The lack of a concrete definition for Artificial General Intelligence (AGI) obscures the gap between today's specialized AI and human-level cognition. This paper introduces a quant…
Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety
Xingjun Ma, Yifeng Gao, Yixu Wang +45
The rapid advancement of large models, driven by their exceptional abilities in learning and generalization through large-scale pre-training, has reshaped the landscape of Artifici…
VideoLLaMB: Long Streaming Video Understanding with Recurrent Memory Bridges
Yuxuan Wang, Yiqi Song, Cihang Xie +2
Recent advancements in large-scale video-language models have shown significant potential for real-time planning and detailed interactions. However, their high computational demand…
VideoHallucer: Evaluating Intrinsic and Extrinsic Hallucinations in Large Video-Language Models
Yuxuan Wang, Yueqian Wang, Dongyan Zhao +2
Recent advancements in Multimodal Large Language Models (MLLMs) have extended their capabilities to video understanding. Yet, these models are often plagued by "hallucinations", wh…
Scaling (Down) CLIP: A Comprehensive Analysis of Data, Architecture, and Training Strategies
Zichao Li, Cihang Xie, Ekin Dogus Cubuk
This paper investigates the performance of the Contrastive Language-Image Pre-training (CLIP) when scaled down to limited computation budgets. We explore CLIP along three dimension…