87 citations · 225 across the 56 of their papers we have counts for
62 papers
Rethinking Pairwise Token Interaction in Spiking Transformers
Sicheng Shen, Dongcheng Zhao, Zhiyuan Li +5
Spiking Transformers inherit token interaction mechanisms from conventional Transformers, yet their sparse binary representations fundamentally alter how token-to-token communicati…
Spiking Local Interaction and Adaptive Complementary Fusion for Spiking Transformer
Dongcheng Zhao, Sicheng Shen, Zhenyu Yang +6
Spiking Transformers model token interactions primarily through spiking self-attention (SSA). However, binary query and key representations map continuous similarities to sparse an…
From Generic Correlation to Input-Specific Credit in On-Policy Self Distillation
Guobin Shen, Lei Huang, Xiang Cheng +4
On-policy self-distillation has emerged as a promising paradigm for post-training language models, in which the model conditions on environment feedback to serve as its own teacher…
Anti-Self-Distillation for Reasoning RL via Pointwise Mutual Information
Guobin Shen, Xiang Cheng, Chenxiao Zhao +4
On-policy self-distillation, where a student is pulled toward a copy of itself conditioned on privileged context (e.g., a verified solution or feedback), offers a promising directi…
Light Alignment Improves LLM Safety via Model Self-Reflection with a Single Neuron
Sicheng Shen, Mingyang Lv, Han Shen +7
The safety of large language models (LLMs) has increasingly emerged as a fundamental aspect of their development. Existing safety alignment for LLMs is predominantly achieved throu…
FireFly-P: FPGA-Accelerated Spiking Neural Network Plasticity for Robust Adaptive Control
Tenglong Li, Jindong Li, Guobin Shen +3
Spiking Neural Networks (SNNs) offer a biologically plausible learning mechanism through synaptic plasticity, enabling unsupervised adaptation without the computational overhead of…
TEFormer: Structured Bidirectional Temporal Enhancement Modeling in Spiking Transformers
Sicheng Shen, Mingyang Lv, Bing Han +4
In recent years, Spiking Neural Networks (SNNs) have achieved remarkable progress, with Spiking Transformers emerging as a promising architecture for energy-efficient sequence mode…
Towards Reliable Evaluation of Adversarial Robustness for Spiking Neural Networks
Jihang Wang, Dongcheng Zhao, Ruolin Chen +2
Spiking Neural Networks (SNNs) utilize spike-based activations to mimic the brain's energy-efficient information processing. However, the binary and discontinuous nature of spike a…
Efficient LLM Safety Evaluation through Multi-Agent Debate
Dachuan Lin, Guobin Shen, Zihao Yang +3
Safety evaluation of large language models (LLMs) increasingly relies on LLM-as-a-judge pipelines, but strong judges can still be expensive to use at scale. We study whether struct…
Safety Instincts: LLMs Learn to Trust Their Internal Compass for Self-Defense
Guobin Shen, Dongcheng Zhao, Haibo Tong +3
Ensuring Large Language Model (LLM) safety remains challenging due to the absence of universal standards and reliable content validators, making it difficult to obtain effective tr…
Bidirectional Intention Inference Enhances LLMs' Defense Against Multi-Turn Jailbreak Attacks
Haibo Tong, Dongcheng Zhao, Guobin Shen +4
The remarkable capabilities of Large Language Models (LLMs) have raised significant safety concerns, particularly regarding "jailbreak" attacks that exploit adversarial prompts to…
DiverValue-Bench: A Benchmark and Fine-Tuning Framework for Aligning Large Language Models with Diverse Human Values
Yao Liang, Dongcheng Zhao, Feifei Zhao +4
Aligning large language models (LLMs) with diverse human values is essential for safe and effective deployment, yet existing benchmarks often overlook cultural and demographic vari…
Boosting the Robustness-Accuracy Trade-off of SNNs by Robust Temporal Self-Ensemble
Jihang Wang, Dongcheng Zhao, Ruolin Chen +2
Spiking Neural Networks (SNNs) offer a promising direction for energy-efficient and brain-inspired computing, yet their vulnerability to adversarial perturbations remains poorly un…
Multi-Level Safety Continual Projection for Fine-Tuned Large Language Models without Retraining
Bing Han, Feifei Zhao, Dongcheng Zhao +4
While fine-tuning services drive the rapid expansion of task capabilities in large language models (LLMs), they are often accompanied by the degradation and reorganization of safet…
Hummingbird: A Smaller and Faster Large Language Model Accelerator on Embedded FPGA
Jindong Li, Tenglong Li, Ruiqi Chen +4
Deploying large language models (LLMs) on embedded devices remains a significant research challenge due to the high computational and memory demands of LLMs and the limited hardwar…
Alignment between Brains and AI: Evidence for Convergent Evolution across Modalities, Scales and Training Trajectories
Guobin Shen, Dongcheng Zhao, Yiting Dong +2
Artificial and biological systems may evolve similar computational solutions despite fundamental differences in architecture and learning mechanisms -- a form of convergent evoluti…
C-VARC: A Large-Scale Chinese Value Rule Corpus for Value Alignment of Large Language Models
Ping Wu, Guobin Shen, Dongcheng Zhao +6
Aligning LLMs with Chinese values requires culturally grounded resources that connect abstract values to concrete behavioral guidance. We introduce C-VARC, a Chinese Value Rule Cor…
PandaGuard: Systematic Evaluation of LLM Safety against Jailbreaking Attacks
Guobin Shen, Dongcheng Zhao, Linghao Feng +8
Large language models (LLMs) have achieved remarkable capabilities but remain vulnerable to adversarial prompts known as jailbreaks, which can bypass safety alignment and elicit ha…
FireFly-T: High-Throughput Sparsity Exploitation for Spiking Transformer Acceleration with Dual-Engine Overlay Architecture
Tenglong Li, Jindong Li, Guobin Shen +3
Spiking transformers are emerging as a promising architecture that combines the energy efficiency of Spiking Neural Networks (SNNs) with the powerful attention mechanisms of transf…
Incorporating brain-inspired mechanisms for multimodal learning in artificial intelligence
Xiang He, Dongcheng Zhao, Yang Li +3
Multimodal learning enhances the perceptual capabilities of cognitive systems by integrating information from different sensory modalities. However, existing multimodal fusion rese…
STEP: A Unified Spiking Transformer Evaluation Platform for Fair and Reproducible Benchmarking
Sicheng Shen, Dongcheng Zhao, Linghao Feng +5
Spiking Transformers have recently emerged as promising architectures for combining the efficiency of spiking neural networks with the representational power of self-attention. How…
Super Co-alignment of Human and AI for Sustainable Symbiotic Society
Yi Zeng, Feifei Zhao, Yuwei Wang +13
As Artificial Intelligence (AI) advances toward Artificial General Intelligence (AGI) and eventually Artificial Superintelligence (ASI), it may potentially surpass human control, d…
Biologically Inspired Spiking Diffusion Model with Adaptive Lateral Selection Mechanism
Linghao Feng, Dongcheng Zhao, Sicheng Shen +1
Lateral connection is a fundamental feature of biological neural circuits, facilitating local information processing and adaptive learning. In this work, we integrate lateral conne…
Enhancing Audio-Visual Spiking Neural Networks through Semantic-Alignment and Cross-Modal Residual Learning
Xiang He, Dongcheng Zhao, Yiting Dong +3
Humans interpret and perceive the world by integrating sensory information from multiple modalities, such as vision and hearing. Spiking Neural Networks (SNNs), as brain-inspired c…