54 citations · 105 across the 15 of their papers we have counts for
8 papers · 1 filter
Register Tokens for Bounded-State Reasoning in Diffusion Language Models
Albert Ge, Chandan Singh, Yufan Zhuang +3
Masked diffusion language models (dLLMs) generate text by iteratively denoising masked tokens with bidirectional attention. Extending reasoning across generation chunks normally re…
Test-time Recursive Thinking: Self-Improvement without External Feedback
Yufan Zhuang, Chandan Singh, Liyuan Liu +5
Modern Large Language Models (LLMs) have shown rapid improvements in reasoning capabilities, driven largely by reinforcement learning (RL) with verifiable rewards. Here, we ask whe…
Text Generation Beyond Discrete Token Sampling
Yufan Zhuang, Liyuan Liu, Chandan Singh +2
In standard autoregressive generation, an LLM predicts the next-token distribution, samples a discrete token, and then discards the distribution, passing only the sampled token as…
Self-Taught Agentic Long Context Understanding
Yufan Zhuang, Xiaodong Yu, Jialian Wu +7
Answering complex, long-context questions remains a major challenge for large language models (LLMs) as it requires effective question clarifications and context retrieval. We prop…
Vector-ICL: In-context Learning with Continuous Vector Representations
Yufan Zhuang, Chandan Singh, Liyuan Liu +2
Large language models (LLMs) have shown remarkable in-context learning (ICL) capabilities on textual data. We explore whether these capabilities can be extended to continuous vecto…
Data Contamination Can Cross Language Barriers
Feng Yao, Yufan Zhuang, Zihao Sun +3
The opacity in developing large language models (LLMs) is raising growing concerns about the potential contamination of public benchmarks in the pre-training data. Existing contami…
WavSpA: Wavelet Space Attention for Boosting Transformers' Long Sequence Learning Ability
Yufan Zhuang, Zihan Wang, Fangbo Tao +1
Transformer and its variants are fundamental neural architectures in deep learning. Recent works show that learning attention in the Fourier space can improve the long sequence lea…
Exploring Software Naturalness through Neural Language Models
Luca Buratti, Saurabh Pujar, Mihaela Bornea +8
The Software Naturalness hypothesis argues that programming languages can be understood through the same techniques used in natural language processing. We explore this hypothesis…