Adaptively Robust LLM Inference Optimization under Prediction Uncertainty

We study the problem of optimizing Large Language Model (LLM) inference scheduling to minimize total latency. LLM inference is an online and multi-task service process and also heavily energy consuming by which a pre-trained LLM processes input requests and generates output tokens sequentially. Therefore, it is vital to improve its scheduling efficiency and reduce the power consumption while a great amount of prompt requests are arriving. A key challenge in LLM inference scheduling is that while the prompt length is known upon arrival, the output length, which critically impacts memory usage and processing time, is unknown. To address this uncertainty, we propose algorithms that leverage machine learning to predict output lengths, assuming the prediction provides an interval classification (min-max range) for each request. We first design a conservative algorithm, $\mathcal{A}_{\max}$, which schedules requests based on the upper bound of predicted output lengths to prevent memory overflow. However, this approach is overly conservative: as prediction accuracy decreases, performance degrades significantly due to potential overestimation. To overcome this limitation, we propose $\mathcal{A}_{\min}$, an adaptive algorithm that initially treats the predicted lower bound as the output length and dynamically refines this estimate during inferencing. We prove that $\mathcal{A}_{\min}$ achieves a log-scale competitive ratio. Through numerical simulations, we demonstrate that $\mathcal{A}_{\min}$ often performs nearly as well as the hindsight scheduler, highlighting both its efficiency and robustness in practical scenarios. Moreover, $\mathcal{A}_{\min}$ relies solely on the lower bound of the prediction interval--an advantageous design choice since upper bounds on output length are typically more challenging to predict accurately.

LLM Serving Optimization with Variable Prefill and Decode Lengths

We study the problem of serving LLM (Large Language Model) requests where each request has heterogeneous prefill and decode lengths. In LLM serving, the prefill length corresponds to the input prompt length, which determines the initial memory usage in the KV cache. The decode length refers to the number of output tokens generated sequentially, with each additional token increasing the KV cache memory usage by one unit. Given a set of n requests, our goal is to schedule and process them to minimize the total completion time. We show that this problem is NP-hard due to the interplay of batching, placement constraints, precedence relationships, and linearly increasing memory usage. We then analyze commonly used scheduling strategies in practice, such as First-Come-First-Serve (FCFS) and Shortest-First (SF), and prove that their competitive ratios scale up sublinearly with the memory limit-a significant drawback in real-world settings where memory demand is large. To address this, we propose a novel algorithm based on a new selection metric that efficiently forms batches over time. We prove that this algorithm achieves a constant competitive ratio. Finally, we develop and evaluate a few algorithm variants inspired by this approach, including dynamic programming variants, local search methods, and an LP-based scheduler, demonstrating through comprehensive simulations that they outperform standard baselines while maintaining computational efficiency.

LRFusionPR: A Polar BEV-Based LiDAR-Radar Fusion Network for Place Recognition

In autonomous driving, place recognition is critical for global localization in GPS-denied environments. LiDAR and radar-based place recognition methods have garnered increasing attention, as LiDAR provides precise ranging, whereas radar excels in adverse weather resilience. However, effectively leveraging LiDAR-radar fusion for place recognition remains challenging. The noisy and sparse nature of radar data limits its potential to further improve recognition accuracy. In addition, heterogeneous radar configurations complicate the development of unified cross-modality fusion frameworks. In this paper, we propose LRFusionPR, which improves recognition accuracy and robustness by fusing LiDAR with either single-chip or scanning radar. Technically, a dual-branch network is proposed to fuse different modalities within the unified polar coordinate bird's eye view (BEV) representation. In the fusion branch, cross-attention is utilized to perform cross-modality feature interactions. The knowledge from the fusion branch is simultaneously transferred to the distillation branch, which takes radar as its only input to further improve the robustness. Ultimately, the descriptors from both branches are concatenated, producing the multimodal global descriptor for place retrieval. Extensive evaluations on multiple datasets demonstrate that our LRFusionPR achieves accurate place recognition, while maintaining robustness under varying weather conditions. Our open-source code will be released at https://github.com/QiZS-BIT/LRFusionPR.

Industrial Internet Robot Collaboration System and Edge Computing Optimization

In a complex environment, for a mobile robot to safely and collision - free avoid all obstacles, it poses high requirements for its intelligence level. Given that the information such as the position and geometric characteristics of obstacles is random, the control parameters of the robot, such as velocity and angular velocity, are also prone to random deviations. To address this issue in the framework of the Industrial Internet Robot Collaboration System, this paper proposes a global path control scheme for mobile robots based on deep learning. First of all, the dynamic equation of the mobile robot is established. According to the linear velocity and angular velocity of the mobile robot, its motion behaviors are divided into obstacle - avoidance behavior, target - turning behavior, and target approaching behavior. Subsequently, the neural network method in deep learning is used to build a global path planning model for the robot. On this basis, a fuzzy controller is designed with the help of a fuzzy control algorithm to correct the deviations that occur during path planning, thereby achieving optimized control of the robot's global path. In addition, considering edge computing optimization, the proposed model can process local data at the edge device, reducing the communication burden between the robot and the central server, and improving the real time performance of path planning. The experimental results show that for the mobile robot controlled by the research method in this paper, the deviation distance of the path angle is within 5 cm, the deviation convergence can be completed within 10 ms, and the planned path is shorter. This indicates that the proposed scheme can effectively improve the global path planning ability of mobile robots in the industrial Internet environment and promote the collaborative operation of robots through edge computing optimization.

SegLocNet: Multimodal Localization Network for Autonomous Driving via Bird's-Eye-View Segmentation

Robust and accurate localization is critical for autonomous driving. Traditional GNSS-based localization methods suffer from signal occlusion and multipath effects in urban environments. Meanwhile, methods relying on high-definition (HD) maps are constrained by the high costs associated with the construction and maintenance of HD maps. Standard-definition (SD) maps-based methods, on the other hand, often exhibit unsatisfactory performance or poor generalization ability due to overfitting. To address these challenges, we propose SegLocNet, a multimodal GNSS-free localization network that achieves precise localization using bird's-eye-view (BEV) semantic segmentation. SegLocNet employs a BEV segmentation network to generate semantic maps from multiple sensor inputs, followed by an exhaustive matching process to estimate the vehicle's ego pose. This approach avoids the limitations of regression-based pose estimation and maintains high interpretability and generalization. By introducing a unified map representation, our method can be applied to both HD and SD maps without any modifications to the network architecture, thereby balancing localization accuracy and area coverage. Extensive experiments on the nuScenes and Argoverse datasets demonstrate that our method outperforms the current state-of-the-art methods, and that our method can accurately estimate the ego pose in urban environments without relying on GNSS, while maintaining strong generalization ability. Our code and pre-trained model will be released publicly.

Online Scheduling for LLM Inference with KV Cache Constraints

Large Language Model (LLM) inference, where a trained model generates text one word at a time in response to user prompts, is a computationally intensive process requiring efficient scheduling to optimize latency and resource utilization. A key challenge in LLM inference is the management of the Key-Value (KV) cache, which reduces redundant computations but introduces memory constraints. In this work, we model LLM inference with KV cache constraints theoretically and propose novel batching and scheduling algorithms that minimize inference latency while effectively managing the KV cache's memory. We analyze both semi-online and fully online scheduling models, and our results are threefold. First, we provide a polynomial-time algorithm that achieves exact optimality in terms of average latency in the semi-online prompt arrival model. Second, in the fully online case with a stochastic prompt arrival, we introduce an efficient online scheduling algorithm with constant regret. Third, we prove that no algorithm (deterministic or randomized) can achieve a constant competitive ratio in fully online adversarial settings. Our empirical evaluations on a public LLM inference dataset, using the Llama-70B model on A100 GPUs, show that our approach significantly outperforms benchmark algorithms used currently in practice, achieving lower latency while reducing energy consumption. Overall, our results offer a path toward more sustainable and cost-effective LLM deployment.

Tokenphormer: Structure-aware Multi-token Graph Transformer for Node Classification

Graph Neural Networks (GNNs) are widely used in graph data mining tasks. Traditional GNNs follow a message passing scheme that can effectively utilize local and structural information. However, the phenomena of over-smoothing and over-squashing limit the receptive field in message passing processes. Graph Transformers were introduced to address these issues, achieving a global receptive field but suffering from the noise of irrelevant nodes and loss of structural information. Therefore, drawing inspiration from fine-grained token-based representation learning in Natural Language Processing (NLP), we propose the Structure-aware Multi-token Graph Transformer (Tokenphormer), which generates multiple tokens to effectively capture local and structural information and explore global information at different levels of granularity. Specifically, we first introduce the walk-token generated by mixed walks consisting of four walk types to explore the graph and capture structure and contextual information flexibly. To ensure local and global information coverage, we also introduce the SGPM-token (obtained through the Self-supervised Graph Pre-train Model, SGPM) and the hop-token, extending the length and density limit of the walk-token, respectively. Finally, these expressive tokens are fed into the Transformer model to learn node representations collaboratively. Experimental results demonstrate that the capability of the proposed Tokenphormer can achieve state-of-the-art performance on node classification tasks.

XHand: Real-time Expressive Hand Avatar

Hand avatars play a pivotal role in a wide array of digital interfaces, enhancing user immersion and facilitating natural interaction within virtual environments. While previous studies have focused on photo-realistic hand rendering, little attention has been paid to reconstruct the hand geometry with fine details, which is essential to rendering quality. In the realms of extended reality and gaming, on-the-fly rendering becomes imperative. To this end, we introduce an expressive hand avatar, named XHand, that is designed to comprehensively generate hand shape, appearance, and deformations in real-time. To obtain fine-grained hand meshes, we make use of three feature embedding modules to predict hand deformation displacements, albedo, and linear blending skinning weights, respectively. To achieve photo-realistic hand rendering on fine-grained meshes, our method employs a mesh-based neural renderer by leveraging mesh topological consistency and latent codes from embedding modules. During training, a part-aware Laplace smoothing strategy is proposed by incorporating the distinct levels of regularization to effectively maintain the necessary details and eliminate the undesired artifacts. The experimental evaluations on InterHand2.6M and DeepHandMesh datasets demonstrate the efficacy of XHand, which is able to recover high-fidelity geometry and texture for hand animations across diverse poses in real-time. To reproduce our results, we will make the full implementation publicly available at https://github.com/agnJason/XHand.

Explicit Interaction for Fusion-Based Place Recognition

Fusion-based place recognition is an emerging technique jointly utilizing multi-modal perception data, to recognize previously visited places in GPS-denied scenarios for robots and autonomous vehicles. Recent fusion-based place recognition methods combine multi-modal features in implicit manners. While achieving remarkable results, they do not explicitly consider what the individual modality affords in the fusion system. Therefore, the benefit of multi-modal feature fusion may not be fully explored. In this paper, we propose a novel fusion-based network, dubbed EINet, to achieve explicit interaction of the two modalities. EINet uses LiDAR ranges to supervise more robust vision features for long time spans, and simultaneously uses camera RGB data to improve the discrimination of LiDAR point clouds. In addition, we develop a new benchmark for the place recognition task based on the nuScenes dataset. To establish this benchmark for future research with comprehensive comparisons, we introduce both supervised and self-supervised training schemes alongside evaluation protocols. We conduct extensive experiments on the proposed benchmark, and the experimental results show that our EINet exhibits better recognition performance as well as solid generalization ability compared to the state-of-the-art fusion-based place recognition approaches. Our open-source code and benchmark are released at: https://github.com/BIT-XJY/EINet.

LCPR: A Multi-Scale Attention-Based LiDAR-Camera Fusion Network for Place Recognition

Place recognition is one of the most crucial modules for autonomous vehicles to identify places that were previously visited in GPS-invalid environments. Sensor fusion is considered an effective method to overcome the weaknesses of individual sensors. In recent years, multimodal place recognition fusing information from multiple sensors has gathered increasing attention. However, most existing multimodal place recognition methods only use limited field-of-view camera images, which leads to an imbalance between features from different modalities and limits the effectiveness of sensor fusion. In this paper, we present a novel neural network named LCPR for robust multimodal place recognition, which fuses LiDAR point clouds with multi-view RGB images to generate discriminative and yaw-rotation invariant representations of the environment. A multi-scale attention-based fusion module is proposed to fully exploit the panoramic views from different modalities of the environment and their correlations. We evaluate our method on the nuScenes dataset, and the experimental results show that our method can effectively utilize multi-view camera and LiDAR data to improve the place recognition performance while maintaining strong robustness to viewpoint changes. Our open-source code and pre-trained models are available at https://github.com/ZhouZijie77/LCPR .