8 papers
Reasoning Before Diagnosis: Physician-Inspired Structured Thinking for ECG Classification
Yang Wu, Xiaoyan Yuan, Hau-San Wong +1
Electrocardiogram (ECG) diagnosis in clinical practice relies on structured reasoning over multiple hierarchical aspects, including cardiac rhythm, conduction properties, waveform…
DPRM: A Plug-in Doob h transform-induced Token-Ordering Module for Discrete Diffusion Models
Dake Bu, Wei Huang, Andi Han +5
Discrete diffusion models admit many token orders, yet most systems rely on confidence-based decoding. Confidence is a strong and efficient heuristic, but it can be myopic because…
Provable Benefit of Curriculum in Transformer Tree-Reasoning Post-Training
Dake Bu, Wei Huang, Andi Han +4
Recent curriculum techniques in the post-training stage of LLMs have been empirically observed to outperform non-curriculum approaches in improving reasoning performance, yet a pri…
Distributional Biases in Post-Training: A Markovian Analysis of Reasoning Trajectories
Dake Bu, Wei Huang, Andi Han +5
Foundation models exhibit broad knowledge but limited task-specific reasoning, motivating post-training strategies such as RL with verifiable rewards (RLVR) and test-time scaling (…
Provable In-Context Vector Arithmetic via Retrieving Task Concepts
Dake Bu, Wei Huang, Andi Han +4
In-context learning (ICL) has garnered significant attention for its ability to grasp functions/tasks from demonstrations. Recent studies suggest the presence of a latent task/func…
QM-ToT: A Medical Tree of Thoughts Reasoning Framework for Quantized Model
Zongxian Yang, Jiayu Qian, Kay Chen Tan +4
Large language models (LLMs) face significant challenges in specialized biomedical tasks due to the inherent complexity of medical reasoning and the sensitive nature of clinical da…
Provably Transformers Harness Multi-Concept Word Semantics for Efficient In-Context Learning
Dake Bu, Wei Huang, Andi Han +4
Transformer-based large language models (LLMs) have displayed remarkable creative prowess and emergence capabilities. Existing empirical studies have revealed a strong connection b…
Provably Neural Active Learning Succeeds via Prioritizing Perplexing Samples
Dake Bu, Wei Huang, Taiji Suzuki +4
Neural Network-based active learning (NAL) is a cost-effective data selection technique that utilizes neural networks to select and train on a small subset of samples. While existi…