The pretrain+fine-tune paradigm is foundational for deploying large language models (LLMs) across various downstream applications. Within this framework, Low-Rank Adaptation (LoRA) stands out for its parameter-efficient fine-tuning (PEFT), producing numerous reusable task-specific LoRA adapters. However, this approach requires explicit task intention selection, posing challenges for autonomous task sensing and switching during inference with multiple existing LoRA adapters embedded in a single LLM. In this work, we introduce MeteoRA (Multiple-Tasks embedded LoRA), a scalable and efficient framework that reuses multiple task-specific LoRA adapters into the base LLM via a full-mode Mixture-of-Experts (MoE) architecture. This framework also includes novel MoE forward acceleration strategies to address the efficiency challenges of traditional MoE implementations. Our evaluation, using the LlaMA2-13B and LlaMA3-8B base models equipped with 28 existing LoRA adapters through MeteoRA, demonstrates equivalent performance with the traditional PEFT method. Moreover, the LLM equipped with MeteoRA achieves superior performance in handling composite tasks, effectively solving ten sequential problems in a single inference pass, thereby demonstrating the framework's enhanced capability for timely adapter switching.

Autonomous Driving Systems (ADS) are advancing rapidly due to progress in deep learning, yet critical challenges remain, particularly in the realm of safety verification.As safety-critical systems, ADS must undergo rigorous testing across diverse scenarios.Realworld data, while valuable, are inherently inflexible for interaction and scenario customization.In contrast, simulator-generated synthetic scenarios provide a platform that enables interaction, control, editability, and adaptability to specific needs.However, current simulation approaches are limited-either relying on costly, manually crafted, overly templated scenarios or generating unconditioned trivial behaviors based on learned distributions.In this work, we introduce LASER, an innovative framework that leverages large language models (LLMs) to conduct traffic simulations based on natural language inputs.The framework operates in two phases.First, it generates scripts from user-provided descriptions.Second, it executes these scripts by guiding autonomous agents within the CARLA simulator to perform tasks in real-time.This method effectively decomposes tasks, allocates controls, and integrates interactive elements to create dynamic and scalable simulations that align with user requirements.By using LASER, we overcome the rigid constraints of traditional simulation methods, enabling the creation of complex, diverse, flexible and on-demand driving scenarios.The approach significantly enhances the process of generating ADS training and testing data, addressing the scalability and diversity issues associated with previous simulation models.The code and all demos are available anonymously at https://njudeepengine.

Neuro-symbolic systems combine the abilities of neural perception and logical reasoning. However, end-to-end learning of neuro-symbolic systems is still an unsolved challenge. This paper proposes a natural framework that fuses neural network training, symbol grounding, and logical constraint synthesis into a coherent and efficient end-to-end learning process. The capability of this framework comes from the improved interactions between the neural and the symbolic parts of the system in both the training and inference stages. Technically, to bridge the gap between the continuous neural network and the discrete logical constraint, we introduce a difference-of-convex programming technique to relax the logical constraints while maintaining their precision. We also employ cardinality constraints as the language for logical constraint learning and incorporate a trust region method to avoid the degeneracy of logical constraint in learning. Both theoretical analyses and empirical evaluations substantiate the effectiveness of the proposed framework.