342 citations · 358 across the 15 of their papers we have counts for
15 papers
Peer-Predictive Self-Training for Language Model Reasoning
Shi Feng, Hanlin Zhang, Fan Nie +2
Mechanisms for continued self-improvement of language models without external supervision remain an open challenge. We propose Peer-Predictive Self-Training (PST), a label-free fin…
Towards Robust Process Reward Modeling via Noise-aware Learning
Bin Xie, Bingbing Xu, Xueyun Tian +2
Process Reward Models (PRMs) have achieved strong results in complex reasoning, but are bottlenecked by costly process-level supervision. A widely used alternative, Monte Carlo Est…
Efficiently Transforming Neural Networks into Decision Trees: A Path to Ground Truth Explanations with RENTT
Helena Monke, Benjamin Fresz, Marco Bernreuther +2
Although neural networks are a powerful tool, their widespread use is hindered by the opacity of their decisions and their black-box nature, which result in a lack of trustworthine…
LANTERN: Scalable Distillation of Large Language Models for Job-Person Fit and Explanation
Zhoutong Fu, Yihan Cao, Yi-Lin Chen +16
Large language models (LLMs) have achieved strong performance across a wide range of natural language processing tasks. However, deploying LLMs at scale for domain specific applica…
Human-Agent Collaborative Paper-to-Page Crafting
Qianli Ma, Siyu Wang, Yilin Chen +7
In the quest for scientific progress, communicating research is as vital as the discovery itself. Yet, researchers are often sidetracked by the manual, repetitive chore of building…
The Bias is in the Details: An Assessment of Cognitive Bias in LLMs
R. Alexander Knipper, Charles S. Knipper, Kaiqi Zhang +3
As Large Language Models (LLMs) are increasingly embedded in real-world decision-making processes, it becomes crucial to examine the extent to which they exhibit cognitive biases.…
UI-TARS-2 Technical Report: Advancing GUI Agent with Multi-Turn Reinforcement Learning
Haoming Wang, Haoyang Zou, Huatong Song +109
The development of autonomous agents for graphical user interfaces (GUIs) presents major challenges in artificial intelligence. While recent advances in native agent models have sh…
Harnessing Rule-Based Reinforcement Learning for Enhanced Grammatical Error Correction
Yilin Li, Xunjian Yin, Yilin Chen +1
Grammatical error correction is a significant task in NLP. Traditional methods based on encoder-decoder models have achieved certain success, but the application of LLMs in this fi…
TEMPURA: Temporal Event Masked Prediction and Understanding for Reasoning in Action
Jen-Hao Cheng, Vivian Wang, Huayu Wang +11
Understanding causal event relationships and achieving fine-grained temporal grounding in videos remain challenging for vision-language models (VLMs). We propose TEMPURA (Temporal…
Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs
Microsoft, :, Abdelrahman Abouelenin +73
We introduce Phi-4-Mini and Phi-4-Multimodal, compact yet highly capable language and multimodal models. Phi-4-Mini is a 3.8-billion-parameter language model trained on high-qualit…
Control LLM: Controlled Evolution for Intelligence Retention in LLM
Haichao Wei, Yunxiang Ren, Zhoutong Fu +4
Large Language Models (LLMs) demand significant computational resources, making it essential to enhance their capabilities without retraining from scratch. A key challenge in this…
Learning from Rich Semantics and Coarse Locations for Long-tailed Object Detection
Lingchen Meng, Xiyang Dai, Jianwei Yang +7
Long-tailed object detection (LTOD) aims to handle the extreme data imbalance in real-world datasets, where many tail classes have scarce instances. One popular strategy is to expl…
i-Code V2: An Autoregressive Generation Framework over Vision, Language, and Speech Data
Ziyi Yang, Mahmoud Khademi, Yichong Xu +16
The convergence of text, visual, and audio data is a key step towards human-like artificial intelligence, however the current Vision-Language-Speech landscape is dominated by encod…
i-Code: An Integrative and Composable Multimodal Learning Framework
Ziyi Yang, Yuwei Fang, Chenguang Zhu +17
Human intelligence is multimodal; we integrate visual, linguistic, and acoustic signals to maintain a holistic worldview. Most current pretraining methods, however, are limited to…
Florence: A New Foundation Model for Computer Vision
Lu Yuan, Dongdong Chen, Yi-Ling Chen +20
Automated visual understanding of our diverse and open world demands computer vision models to generalize well with minimal customization for specific tasks, similar to human visio…