Yan Li

The paper introduces BYOT-CPS, a hybrid cyber-physical testbed that bridges the gap between purely simulated and purely physical IoT testing environments, enabling realistic and scalable security assessment.

CyBOKClaw is an interpretable human-in-the-loop retrieval framework designed to map broad cybersecurity keywords to the Cyber Security Body of Knowledge (CyBOK), achieving high expert-guided mapping accuracy on both development and validation datasets.

The paper introduces Metacognitive Memory Policy Optimization (MMPO), a novel memory training approach that optimizes LLM memory not based on final task success, but on minimizing epistemic uncertainty in intermediate summaries, significantly improving long-horizon agent performance.

The paper proposes Meta-Team, an experience-driven framework that enables multi-agent systems (MAS) to collaboratively self-evolve by transforming complex execution experiences into reusable improvements for agent behaviors and coordination.

The paper introduces FAM-Bench, a novel multimodal benchmark designed to test advanced, condition-aware reasoning for food-as-medicine applications.

GSAM introduces a generalizable and safe robotic framework for articulated object manipulation, significantly improving success rates and reducing variability across diverse tasks by integrating commonsense reasoning and explicit collision constraints.

CAREAgent is a novel agent designed for fine-grained clinical order generation, achieving significant performance improvements on unseen benchmarks by integrating structured reasoning and tool usage.

MViewRouter proposes a multi-view framework that internalizes geometric equivariance using a Multi-view Alternating Attention mechanism to improve generalization and stabilize training for combinatorial routing problems like TSP and CVRP.

The paper proposes DART, a test-time adaptation method that enhances zero-resource dense retrieval reranking by adaptively tuning a bilinear scoring matrix using pseudo-positive and pseudo-negative examples, achieving significant performance gains with minimal latency.

This survey reviews how Large and Multi-modal Language Models (LLMs/MM-LLMs) are being applied to integrate diverse data sources for enhanced decision support in transportation systems management and operations.

The paper introduces Moment-Video, a new benchmark that diagnoses the ability of video MLLMs to understand brief, critical visual events, revealing that current models struggle significantly with temporal fidelity.

This paper investigates whether adults' struggles with conjunctive causal rules persist when they have agency through active exploration.

This paper proposes ARTSN, a scheduling paradigm for autonomous real-time systems using time-sensitive networking, addressing volatility and absence challenges of self-triggered traffic.

This paper proposes ShopX, a model-centric framework for intent-driven shopping experiences using a single foundation model for intent understanding, execution planning, and item-space operations.

This paper presents the first quantitative analysis of China's Brain-Computer Interface (BCI) translational ecosystem, examining clinical trials, investigator-initiated trials, and regulatory-approved products.

The paper introduces IdeaGene-Bench, a benchmark for scientific lineage reasoning and idea generation, which includes 1,961 golden lineage traces, 1,085 curated Idea Genome objects, and 920 pairwise GenomeDiff records.

This paper proposes PA-HDP, a framework for privacy-preserving retrieval-augmented generation using prompt-aware dynamic hierarchical differential privacy.

This paper introduces Code-Poisoning Property Inference Attack (CPPIA), the first code-level Property Inference Attack (PIA) for leaking privacy from ML models trained on private data, overcoming limitations of existing works.

This paper introduces StellarTTS, a mobile-optimized non-autoregressive text-to-speech framework with sparse temporal embeddings and a semantic-aware codec, achieving lower latency and stronger robustness.

This paper introduces GS-Agent, an end-to-end multi-agent framework that generates realistic, dynamic, and controllable 4D physical worlds from natural language descriptions by emulating human creation process using physics engines.