16 citations · 37 across the 18 of their papers we have counts for
19 papers
SLDBench v2: Active Scaling Law Discovery
Lingbei Meng, Rui Huang, Zhirong Shen +5
Scaling laws predict large-model performance from small experiments, but finding a reliable law itself requires costly experiments. SLDBench tests whether language-model agents can…
SceneActBench: Can Agents Act on the 3D Scenes They See?
Yifei Zhao, Xiangxin Zhou, Wenhao Yang +11
Vision-language model (VLM) agents increasingly use tools to act on 3D scenes rather than only describe them. Existing 3D benchmarks score textual responses or single-object operat…
Can Current Agents Close the Discovery-to-Application Gap? A Case Study in Minecraft
Zhou Ziheng, Huacong Tang, Jinyuan Zhang +9
Discovering causal regularities and applying them to build functional systems--the discovery-to-application loop--is a hallmark of general intelligence, yet evaluating this capacit…
UniCode: Augmenting Evaluation for Code Reasoning
Xinyue Zheng, Haowei Lin, Shaofei Cai +3
Current coding benchmarks often inflate Large Language Model (LLM) capabilities due to static paradigms and data contamination, enabling models to exploit statistical shortcuts rat…
Unified Cross-Scale 3D Generation and Understanding via Autoregressive Modeling
Shuqi Lu, Haowei Lin, Lin Yao +6
3D structure modeling is essential across scales, enabling applications from fluid simulation and 3D reconstruction to protein folding and molecular docking. Yet, despite shared 3D…
GROOT-2: Weakly Supervised Multi-Modal Instruction Following Agents
Shaofei Cai, Bowei Zhang, Zihao Wang +4
Developing agents that can follow multimodal instructions remains a fundamental challenge in robotics and AI. Although large-scale pre-training on unlabeled datasets (no language i…
Preference Goal Tuning: Post-Training as Latent Control for Frozen Policies
Guangyu Zhao, Kewei Lian, Haoxuan Ru +8
Goal-conditioned policies enable decision-making models to execute diverse behaviors based on specified goals, yet their downstream performance is often highly sensitive to the cho…
TFG: Unified Training-Free Guidance for Diffusion Models
Haotian Ye, Haowei Lin, Jiaqi Han +6
Given an unconditional diffusion model and a predictor for a target property of interest (e.g., a classifier), the goal of training-free guidance is to generate samples with desira…
OmniJARVIS: Unified Vision-Language-Action Tokenization Enables Open-World Instruction Following Agents
Zihao Wang, Shaofei Cai, Zhancun Mu +7
This paper presents OmniJARVIS, a novel Vision-Language-Action (VLA) model for open-world instruction-following agents in Minecraft. Compared to prior works that either emit textua…
CLoG: Benchmarking Continual Learning of Image Generation Models
Haotian Zhang, Junting Zhou, Haowei Lin +6
Continual Learning (CL) poses a significant challenge in Artificial Intelligence, aiming to mirror the human ability to incrementally acquire knowledge and skills. While extensive…
RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon Generation
Zihao Wang, Anji Liu, Haowei Lin +3
We explore how iterative revising a chain of thoughts with the help of information retrieval significantly improves large language models' reasoning and generation ability in long-…
Selecting Large Language Model to Fine-tune via Rectified Scaling Law
Haowei Lin, Baizhou Huang, Haotian Ye +7
The ever-growing ecosystem of LLMs has posed a challenge in selecting the most appropriate pre-trained model to fine-tune amidst a sea of options. Given constrained resources, fine…
JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Models
Zihao Wang, Shaofei Cai, Anji Liu +9
Achieving human-like planning and control with multimodal observations in an open world is a key milestone for more functional generalist agents. Existing approaches can handle cer…
FLatS: Principled Out-of-Distribution Detection with Feature-Based Likelihood Ratio Score
Haowei Lin, Yuntian Gu
Detecting out-of-distribution (OOD) instances is crucial for NLP models in practical applications. Although numerous OOD detection methods exist, most of them are empirical. Backed…
MCU: An Evaluation Framework for Open-Ended Game Agents
Xinyue Zheng, Haowei Lin, Kaichen He +3
Developing AI agents capable of interacting with open-world environments to solve diverse tasks is a compelling challenge. However, evaluating such open-ended agents remains diffic…
Class Incremental Learning via Likelihood Ratio Based Task Prediction
Haowei Lin, Yijia Shao, Weinan Qian +3
Class incremental learning (CIL) is a challenging setting of continual learning, which learns a series of tasks sequentially. Each task consists of a set of unique classes. The key…
Continual Pre-training of Language Models
Zixuan Ke, Yijia Shao, Haowei Lin +3
Language models (LMs) have been instrumental for the rapid advance of natural language processing. This paper studies continual pre-training of LMs, in particular, continual domain…
Adapting a Language Model While Preserving its General Knowledge
Zixuan Ke, Yijia Shao, Haowei Lin +3
Domain-adaptive pre-training (or DA-training for short), also known as post-training, aims to train a pre-trained general-purpose language model (LM) using an unlabeled corpus of a…
Continual Training of Language Models for Few-Shot Learning
Zixuan Ke, Haowei Lin, Yijia Shao +3
Recent work on applying large language models (LMs) achieves impressive performance in many NLP applications. Adapting or posttraining an LM using an unlabeled domain corpus can pr…