11 citations · 25 across the 21 of their papers we have counts for
25 papers
Relevance Is Not Sufficiency: What Actually Closes the Evidence Gap in Long-Term Memory QA
Yufeng Li, Shuxin Li, Zhenhua Xu +7
LLM agents that interact with a user across many sessions accumulate histories that exceed their context window, so they store past interactions in an external memory and answer ea…
Benchmarking EEG Foundation Models at Scale: Lessons from 20,000 Evaluations
Zhige Chen, Shu Peng, Chengxuan Qin +4
Electroencephalography (EEG) foundation models (FMs) promise transferable neural representations, yet their advantages over strong supervised baselines and their prospects for furt…
EEG Benchmarking Needs a Task Specification Layer: NeuroDoc for Rulebook-Guided, Executable Benchmark Construction
Chengxuan Qin, Zhige Chen, Shu Peng +9
Electroencephalography (EEG) foundation models increasingly rely on multi-dataset training and evaluation, yet public EEG datasets still lack a shared task specification layer that…
HEAR: An EEG Foundation Model with Heterogeneous Electrode Adaptive Representation
Zhige Chen, Chengxuan Qin, Wenlong You +5
Electroencephalography (EEG) is an essential technique for neuroscience research and brain-computer interface (BCI) applications. Recently, large-scale EEG foundation models have b…
Efficient Training of Spiking Neural Networks by Spike-aware Data Pruning
Chenxiang Ma, Xinyi Chen, Yujie Wu +2
Spiking neural networks (SNNs), recognized as an energy-efficient alternative to traditional artificial neural networks (ANNs), have advanced rapidly through the scaling of models…
Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping
Hangming Zhang, Zheng Li, Chenxiang Ma +4
Spiking neural networks (SNNs) offer advantages in computational efficiency via event-driven computing, compared to traditional artificial neural networks (ANNs). While direct trai…
Spatio-Temporal Decoupled Learning for Spiking Neural Networks
Chenxiang Ma, Xinyi Chen, Kay Chen Tan +1
Spiking neural networks (SNNs) have gained significant attention for their potential to enable energy-efficient artificial intelligence. However, effective and efficient training o…
LLM Cannot Discover Causality, and Should Be Restricted to Non-Decisional Support in Causal Discovery
Xingyu Wu, Kui Yu, Jibin Wu +1
This paper critically re-evaluates LLMs' role in causal discovery and argues against their direct involvement in determining causal relationships. We demonstrate that LLMs' autoreg…
Neuromorphic Sequential Arena: A Benchmark for Neuromorphic Temporal Processing
Xinyi Chen, Chenxiang Ma, Yujie Wu +2
Temporal processing is vital for extracting meaningful information from time-varying signals. Recent advancements in Spiking Neural Networks (SNNs) have shown immense promise in ef…
Diversity-Aware Policy Optimization for Large Language Model Reasoning
Jian Yao, Ran Cheng, Xingyu Wu +2
The reasoning capabilities of large language models (LLMs) have advanced rapidly, particularly following the release of DeepSeek R1, which has inspired a surge of research into dat…
Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects
Chenxiang Ma, Xinyi Chen, Yanchen Li +7
Temporal processing is fundamental for both biological and artificial intelligence systems, as it enables the comprehension of dynamic environments and facilitates timely responses…
Towards Ultra-Low-Power Neuromorphic Speech Enhancement with Spiking-FullSubNet
Xiang Hao, Chenxiang Ma, Qu Yang +2
Speech enhancement is critical for improving speech intelligibility and quality in various audio devices. In recent years, deep learning-based methods have significantly improved s…
HM3: Hierarchical Multi-Objective Model Merging for Pretrained Models
Yu Zhou, Xingyu Wu, Jibin Wu +2
Model merging is a technique that combines multiple large pretrained models into a single model with enhanced performance and broader task adaptability. It has gained popularity in…
Towards Automated Knowledge Transfer in Evolutionary Multitasking via Large Language Models
Xuebin Lyu, Yuxiao Huang, XueFeng Chen +3
Evolutionary multi-task optimization (EMTO) is an advanced optimization paradigm that improves search efficiency by enabling knowledge transfer across multiple tasks solved in para…
PMSN: A Parallel Multi-compartment Spiking Neuron for Multi-scale Temporal Processing
Xinyi Chen, Jibin Wu, Chenxiang Ma +4
Spiking Neural Networks (SNNs) hold great potential to realize brain-inspired, energy-efficient computational systems. However, current SNNs still fall short in terms of multiscale…
Autonomous Multi-Objective Optimization Using Large Language Model
Yuxiao Huang, Shenghao Wu, Wenjie Zhang +3
Multi-objective optimization problems (MOPs) are ubiquitous in real-world applications, presenting a complex challenge of balancing multiple conflicting objectives. Traditional evo…
Unlock the Power of Algorithm Features: A Generalization Analysis for Algorithm Selection
Xingyu Wu, Yan Zhong, Jibin Wu +3
In the algorithm selection research, the discussion surrounding algorithm features has been significantly overshadowed by the emphasis on problem features. Although a few empirical…
CausalBN-Bench: A Comprehensive Benchmark for Causal Learning Capability of LLMs
Yu Zhou, Xingyu Wu, Jibin Wu +2
The ability to understand causality significantly impacts the competence of large language models (LLMs) in output explanation and counterfactual reasoning, as causality reveals th…
Exploring the True Potential: Evaluating the Black-box Optimization Capability of Large Language Models
Beichen Huang, Xingyu Wu, Yu Zhou +4
Large language models (LLMs) have demonstrated exceptional performance not only in natural language processing tasks but also in a great variety of non-linguistic domains. In diver…
Scaling Supervised Local Learning with Augmented Auxiliary Networks
Chenxiang Ma, Jibin Wu, Chenyang Si +1
Deep neural networks are typically trained using global error signals that backpropagate (BP) end-to-end, which is not only biologically implausible but also suffers from the updat…
Efficient Online Learning for Networks of Two-Compartment Spiking Neurons
Yujia Yin, Xinyi Chen, Chenxiang Ma +2
The brain-inspired Spiking Neural Networks (SNNs) have garnered considerable research interest due to their superior performance and energy efficiency in processing temporal signal…
Evolutionary Computation in the Era of Large Language Model: Survey and Roadmap
Xingyu Wu, Sheng-hao Wu, Jibin Wu +2
Large language models (LLMs) have not only revolutionized natural language processing but also extended their prowess to various domains, marking a significant stride towards artif…
Large Language Model-Enhanced Algorithm Selection: Towards Comprehensive Algorithm Representation
Xingyu Wu, Yan Zhong, Jibin Wu +2
Algorithm selection, a critical process of automated machine learning, aims to identify the most suitable algorithm for solving a specific problem prior to execution. Mainstream al…
LC-TTFS: Towards Lossless Network Conversion for Spiking Neural Networks with TTFS Coding
Qu Yang, Malu Zhang, Jibin Wu +2
The biological neurons use precise spike times, in addition to the spike firing rate, to communicate with each other. The time-to-first-spike (TTFS) coding is inspired by such biol…