6 papers
MoWorld: A Flash World Model
Team Moxin, Deyi Ji, Tianrun Chen +37
The future of World Models depends not only on scaling model capability, but also on scaling practicality and inference efficiency. High-frame-rate inference enables responsive per…
OPERA: Aligning Open-Ended Reasoning via Objective Perplexity-based Reinforcement Learning
Wenxuan Jiang, Zining Fan, Zijian Zhang +6
Reinforcement Learning (RL) has enabled LLMs to excel in objective reasoning tasks such as mathematics and code generation. However, applying RL to open-ended tasks, such as creati…
A Hierarchical Framework for Measuring Scientific Paper Innovation via Large Language Models
Hongming Tan, Shaoxiong Zhan, Fengwei Jia +2
Measuring scientific paper innovation is both important and challenging. Existing content-based methods often overlook the full-paper context, fail to capture the full scope of inn…
LexSemBridge: Fine-Grained Dense Representation Enhancement through Token-Aware Embedding Augmentation
Shaoxiong Zhan, Hai Lin, Hongming Tan +6
As queries in retrieval-augmented generation (RAG) pipelines powered by large language models (LLMs) become increasingly complex and diverse, dense retrieval models have demonstrat…
Data Augmentation in Time Series Forecasting through Inverted Framework
Hongming Tan, Ting Chen, Ruochong Jin +1
Currently, iTransformer is one of the most popular and effective models for multivariate time series (MTS) forecasting. Thanks to its inverted framework, iTransformer effectively c…
QAEA-DR: A Unified Text Augmentation Framework for Dense Retrieval
Hongming Tan, Shaoxiong Zhan, Hai Lin +2
In dense retrieval, embedding long texts into dense vectors can result in information loss, leading to inaccurate query-text matching. Additionally, low-quality texts with excessiv…