3 citations · 6 across the 8 of their papers we have counts for
8 papers
Praxist: From Experimental Artifacts to Solution Lineages
Jin Li, Ahmed Murtadha, Zhiyu Wang +14
Autonomous R&D agents now write, run, and improve executable artifacts under automated evaluation-but largely as laboratory instruments: shown on curated benchmarks, with gains tha…
Rethinking EEG-Based Disease Diagnosis: Decoupling Instance Representation Learning from Subject-Level Supervision
Zhiyuan Ma, Zeyuan Li, Zhiyi Lu +7
EEG-based disease diagnosis requires one prediction per subject, yet common pipelines segment recordings into short instances, inherit the subject label for every instance, and tra…
HRM-Text: Efficient Pretraining Beyond Scaling
Guan Wang, Changling Liu, Chenyu Wang +6
The current pretraining paradigm for large language models relies on massive compute and internet-scale raw text, creating a significant barrier to foundational research. In contra…
An Evolutionary Algorithm with Probabilistic Annealing for Large-scale Sparse Multi-objective Optimization
Shuai Shao, Yuhao Sun, Xing Chen +3
Large-scale sparse multi-objective optimization problems (LSMOPs) are prevalent in real-world applications, where optimal solutions typically contain only a few nonzero variables,…
Signal-Adaptive Trust Regions for Gradient-Free Optimization of Recurrent Spiking Neural Networks
Jinhao Li, Yuhao Sun, Zhiyuan Ma +5
Recurrent spiking neural networks (RSNNs) are a promising substrate for energy-efficient control policies, but training them for high-dimensional, long-horizon reinforcement learni…
Hierarchical Reasoning Model
Guan Wang, Jin Li, Yuhao Sun +6
Reasoning, the process of devising and executing complex goal-oriented action sequences, remains a critical challenge in AI. Current large language models (LLMs) primarily employ C…
Capturing Aperiodic Temporal Dynamics of EEG Signals through Stochastic Fluctuation Modeling
Yuhao Sun, Zhiyuan Ma, Xinke Shen +3
Electrophysiological brain signals, such as electroencephalography (EEG), exhibit both periodic and aperiodic components, with the latter often modeled as 1/f noise and considered…
Evolving Connectivity for Recurrent Spiking Neural Networks
Guan Wang, Yuhao Sun, Sijie Cheng +1
Recurrent spiking neural networks (RSNNs) hold great potential for advancing artificial general intelligence, as they draw inspiration from the biological nervous system and show p…