Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks14 from public data
- A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment139
This paper introduces the concept of "full-stack" safety to systematically consider safety issues throughout the entire process of LLM training, deployment, and eventual commercialization, and represents the first safety survey to encompass the entire lifecycle of LLMs.
- A Survey on Trustworthy LLM Agents: Threats and Countermeasures137
The TrustAgent framework is proposed, a comprehensive study on the trustworthiness of agents, characterized by modular taxonomy, multi-dimensional connotations, and technical implementation, aiming to provide guidance for future endeavors.
- AgentSafe: Safeguarding Large Language Model-based Multi-agent Systems via Hierarchical Data Management49
Experiments show that AgentSafe significantly boosts system resilience, achieving defense success rates above 80% under adversarial conditions, and demonstrates scalability, maintaining robust performance as agent numbers and information complexity grow.
- CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networks42
A novel method for credit card fraud detection, the CaT-GNN, which leverages causal invariant learning to reveal inherent correlations within transaction data and applies a causal mixup strategy to enhance the model's robustness and interpretability.
- NetSafe: Exploring the Topological Safety of Multi-agent Networks36
A new topological perspective on the safety of LLM-based multi-agent networks is introduced and several unreported phenomena are discovered, paving the way for future research to explore the safety of such networks.
- 24
- Causal Deciphering and Inpainting in Spatio-Temporal Dynamics via Diffusion Model14
CaPaint overcomes the high complexity dilemma of optimal ST causal discovery models by reducing the data generation complexity from exponential to quasi-linear levels and underscores the potential of diffusion models in ST enhancement, offering a novel paradigm for this field.
- Mind Scramble: Unveiling Large Language Model Psychology Via Typoglycemia7
A research line and methodology called LLM Psychology is introduced, leveraging human psychology experiments to investigate the cognitive behaviors and mechanisms of large language models, and migrates the Typoglycemia phenomenon from psychology to explore the mind of LLMs.
- Granulon: Awakening Pixel-Level Visual Encoders with Adaptive Multi-Granularity Semantics for MLLM1
Granulon is proposed, a novel DINOv3-based MLLM with adaptive granularity augmentation that enables unified"pixel-to-fine-to-coarse"reasoning within a single forward pass.
- –
- –
- –
- –
- –
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it. You will be asked to sign in.