13 citations · 15 across the 12 of their papers we have counts for
15 papers
GACC-Bench: Benchmarking Goal-Adaptive Context Compression Ability across Large Language Models
Weiting Liu, Zhiyu Pan, Shuhong Wang +7
Context compression in long-running applications of large language models (LLMs) must preserve goal-relevant information, often before specific downstream questions are known. We i…
LLMs as Adaptive Meta-Solvers: Strategy-Diverse RL for Industrial-Scale Optimization
Shihao Zhang, Weiting Liu, Siyu Shao +4
Scaling LLM-based optimization from textbook-scale instances to real-world, industrial tasks remains a critical open challenge. Existing approaches are predominantly evaluated on s…
ToolAnchor: Anchoring Counterfactual Context to Boost Agentic Tool-use Capability
Weiting Liu, Jieyi Bi, Wanqi Zhou +4
Tool-augmented large language model agents excel at long-horizon tasks, yet they are typically post-trained on fixed toolsets. When tasks demand new tools, these agents struggle to…
On the Nonasymptotic Scaling Guarantee of Hyperparameter Estimation in Inhomogeneous, Weakly-Dependent Complex Network Dynamical Systems
Yi Yu, Yubo Hou, Yinchong Wang +3
Hierarchical Bayesian models are increasingly used in large, inhomogeneous complex network dynamical systems by modeling parameters as draws from a hyperparameter-governed distribu…
Automated Optimization Modeling via a Localizable Error-Driven Perspective
Weiting Liu, Han Wu, Yufei Kuang +4
Automated optimization modeling via Large Language Models (LLMs) has emerged as a promising approach to assist complex human decision-making. While post-training has become a pivot…
Dark Signals in the Brain: Augment Brain Network Dynamics to the Complex-valued Field
Jiangnan Zhang, Chengyuan Qian, Wenlian Lu +3
Recordings of brain activity, such as functional MRI (fMRI), provide low-dimensional, indirect observations of neural dynamics evolving in high-dimensional, unobservable spaces. Em…
EEG-fused Digital Twin Brain for Autonomous Driving in Virtual Scenarios
Yubo Hou, Zhengxin Zhang, Ziyi Wang +3
Current methodologies typically integrate biophysical brain models with functional magnetic resonance imaging(fMRI) data - while offering millimeter-scale spatial resolution (0.5-2…
Stochastic Forward-Forward Learning through Representational Dimensionality Compression
Zhichao Zhu, Yang Qi, Hengyuan Ma +2
The Forward-Forward (FF) learning algorithm provides a bottom-up alternative to backpropagation (BP) for training neural networks, relying on a layer-wise "goodness" function with…
Optimal signal transmission and timescale diversity in a model of human brain operating near criticality
Yang Qi, Jiexiang Wang, Weiyang Ding +4
Cortical neurons exhibit a hierarchy of timescales across brain regions in response to input stimuli, which is thought to be crucial for information processing of different tempora…
Uncertainty Quantification in Working Memory via Moment Neural Networks
Hengyuan Ma, Wenlian Lu, Jianfeng Feng
Humans possess a finely tuned sense of uncertainty that helps anticipate potential errors, vital for adaptive behavior and survival. However, the underlying neural mechanisms remai…
Towards free-response paradigm: a theory on decision-making in spiking neural networks
Zhichao Zhu, Yang Qi, Wenlian Lu +3
The energy-efficient and brain-like information processing abilities of Spiking Neural Networks (SNNs) have attracted considerable attention, establishing them as a crucial element…
Efficient Combinatorial Optimization via Heat Diffusion
Hengyuan Ma, Wenlian Lu, Jianfeng Feng
Combinatorial optimization problems are widespread but inherently challenging due to their discrete nature. The primary limitation of existing methods is that they can only access…
Learn to integrate parts for whole through correlated neural variability
Zhichao Zhu, Yang Qi, Wenlian Lu +1
Sensory perception originates from the responses of sensory neurons, which react to a collection of sensory signals linked to various physical attributes of a singular perceptual o…
A General Description of Criticality in Neural Network Models
Longbin Zeng, Fengjian Feng, Wenlian Lu
Recent experimental observations have supported the hypothesis that the cerebral cortex operates in a dynamical regime near criticality, where the neuronal network exhibits a mixtu…
Simulation and assimilation of the digital human brain
Wenlian Lu, Xin Du, Jiexiang Wang +7
Here, we present the Digital Brain (DB), a platform for simulating spiking neuronal networks at the large neuron scale of the human brain based on personalized magnetic-resonance-i…