20 citations · 131 across the 43 of their papers we have counts for
12 papers · 1 filter
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…
-OPD: Stabilizing Long-Horizon On-Policy Distillation with Freshness-Aware Control
Xianwei Chen, Shimin Zhang, Jibin Wu
Scaling on-policy distillation (OPD) for large language models (LLMs) confronts a fundamental tension: asynchronous execution is necessary for system efficiency, but structurally d…
ReLaX: Reasoning with Latent Exploration for Large Reasoning Models
Shimin Zhang, Xianwei Chen, Yufan Shen +2
Reinforcement Learning with Verifiable Rewards (RLVR) has recently demonstrated remarkable potential in enhancing the reasoning capability of Large Reasoning Models (LRMs). However…
SpikingBrain: Spiking Brain-inspired Large Models
Yuqi Pan, Yupeng Feng, Jinghao Zhuang +16
Mainstream Transformer-based large language models face major efficiency bottlenecks: training computation scales quadratically with sequence length, and inference memory grows lin…
ZeCO: Zero Communication Overhead Sequence Parallelism for Linear Attention
Yuhong Chou, Zehao Liu, Ruijie Zhu +6
Linear attention mechanisms deliver significant advantages for Large Language Models (LLMs) by providing linear computational complexity, enabling efficient processing of ultra-lon…
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…