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Researcher profile

Xiaoyang Li

Xiaoyang Li contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
eess.SPInformation Theorymath.ITComputer VisionArtificial IntelligenceMachine Learning

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Published work

6 published item(s)

preprint2026arXiv

AdaGamma: State-Dependent Discounting for Temporal Adaptation in Reinforcement Learning

The discount factor in reinforcement learning controls both the effective planning horizon and the strength of bootstrapping, yet most deep RL methods use a single fixed value across all states. While state-dependent discounting is conceptually appealing, naive deep actor--critic implementations can become unstable and degenerate toward TD-error collapse. We propose AdaGamma, a practical deep actor--critic method for state-dependent discounting that learns a state-dependent discount function together with a return-consistency objective to regularize the induced backup structure. On the theory side, we analyze the Bellman operator induced by state-dependent discounting and establish its basic well-posedness properties under suitable conditions. Empirically, AdaGamma integrates into both SAC and PPO, yielding consistent improvements on continuous-control benchmarks, and achieves statistically significant gains in an online A/B test on the JD Logistics platform. These results suggest that state-dependent discounting can be made effective in deep RL when coupled with a return-consistency objective that prevents degenerate target manipulation.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Codebook Design for Limited Feedback in Near-Field XL-MIMO Systems

In this paper, we study efficient codebook design for limited feedback in extremely large-scale multiple-input-multiple-output (XL-MIMO) frequency division duplexing (FDD) systems. It is worth noting that existing codebook designs for XL-MIMO, such as polar-domain codebook, have not well taken into account user (location) distribution in practice, thereby incurring excessive feedback overhead. To address this issue, we propose in this paper a novel and efficient feedback codebook tailored to user distribution. To this end, we first consider a typical scenario where users are uniformly distributed within a specific polar-region, based on which a sum-rate maximization problem is formulated to jointly optimize angle-range samples and bit allocation among angle/range feedback. This problem is challenging to solve due to the lack of a closed-form expression for the received power in terms of angle and range samples. By leveraging a Voronoi partitioning approach, we show that uniform angle sampling is optimal for received power maximization. For more challenging range sampling design, we obtain a tight lower-bound on the received power and show that geometric sampling, where the ratio between adjacent samples is constant, can maximize the lower bound and thus serves as a high-quality suboptimal solution. We then extend the proposed framework to accommodate more general non-uniform user distribution via an alternating sampling method. Furthermore, theoretical analysis reveals that as the array size increases, the optimal allocation of feedback bits increasingly favors range samples at the expense of angle samples. Finally, numerical results validate the superior rate performance and robustness of the proposed codebook design under various system setups, achieving significant gains over benchmark schemes, including the widely used polar-domain codebook.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Bringing The Consistency Gap: Explicit Structured Memory for Interleaved Image-Text Generation

Existing Vision Language Models (VLMs) often struggle to preserve logic, entity identity, and artistic style during extended, interleaved image-text interactions. We identify this limitation as "Multimodal Context Drift", which stems from the inherent tendency of implicit neural representations to decay or become entangled over long sequences. To bridge this gap, we propose IUT-Plug, a model-agnostic Neuro-Symbolic Structured State Tracking mechanism. Unlike purely neural approaches that rely on transient attention maps, IUT-Plug introduces the Image Understanding Tree (IUT) as an explicit, persistent memory module. The framework operates by (1) parsing visual scenes into hierarchical symbolic structures (entities, attributes, and relationships); (2) performing incremental state updates to logically lock invariant properties while modifying changing elements; and (3) guiding generation through topological constraints. We evaluate our approach on a novel benchmark comprising 3,000 human-annotated samples. Experimental results demonstrate that IUT-Plug effectively mitigates context drift, achieving significantly higher consistency scores compared to unstructured text-prompting baselines. This confirms that explicit symbolic grounding is essential for maintaining robust long-horizon consistency in multimodal generation.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Integrated Sensing, Communication, and Computation Over-the-Air: MIMO Beamforming Design

To support the unprecedented growth of the Internet of Things (IoT) applications, tremendous data need to be collected by the IoT devices and delivered to the server for further computation. By utilizing the same signals for both radar sensing and data communication, the integrated sensing and communication (ISAC) technique has broken the barriers between data collection and delivery in the physical layer. By exploiting the analog-wave addition in a multi-access channel, over-the-air computation (AirComp) enables function computation via transmissions in the physical layer. The promising performance of ISAC and AirComp motivates the current work on developing a framework called integrated sensing, communication, and computation over-the-air (ISCCO). The performance metrics of radar sensing and AirComp are evaluated by the mean squared errors of the estimated target response matrix and the received computation results, respectively. The design challenge of MIMO ISCCO lies in the joint optimization of beamformers for sensing, communication, and computation at both the IoT devices and the server, which results in a non-convex problem. To solve this problem, an algorithmic solution based on the technique of semidefinite relaxation is proposed. The use case of target location estimation based on ISCCO is demonstrated in simulation to show the performance superiority.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Joint Sensing and Communication-Rate Control for Energy Efficient Mobile Crowd Sensing

Driven by the rapid growth of Internet of Things applications, tremendous data need to be collected by sensors and uploaded to the servers for further process. As a promising solution, mobile crowd sensing enables controllable sensing and transmission processes for multiple types of data in a single device. In this paper, a typical user is considered that is required to sense and transmit data to a server, while it is assumed to remain busy and incapable of sensing data during an interval. An optimization problem is formulated to minimize the energy consumption of data sensing and transmission by controlling the sensing and transmission rates over time, subject to the constraints on the sensing data sizes, transmission data sizes, data casualty, and sensing busy time. This problem is highly challenging, due to the coupling between the rates as well as the existence of the busy time. To deal with this problem, we first show that it can be equivalently decomposed into two subproblems, corresponding to a search for the amount of data size that needs to be sensed before the busy time (referred to as the height), as well as the sensing and transmission rate control given the height. Next, we show that the latter problem can be efficiently solved by using the classical string-pulling method, while an efficient algorithm is proposed to progressively find the optimal height without the exhaustive search. Moreover, the solution approach is extended to a more complex scenario where there is a finite-size buffer at the server for receiving data. Last, simulations are conducted to evaluate the performance of the proposed design.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Few Is Enough: Task-Augmented Active Meta-Learning for Brain Cell Classification

Deep Neural Networks (or DNNs) must constantly cope with distribution changes in the input data when the task of interest or the data collection protocol changes. Retraining a network from scratch to combat this issue poses a significant cost. Meta-learning aims to deliver an adaptive model that is sensitive to these underlying distribution changes, but requires many tasks during the meta-training process. In this paper, we propose a tAsk-auGmented actIve meta-LEarning (AGILE) method to efficiently adapt DNNs to new tasks by using a small number of training examples. AGILE combines a meta-learning algorithm with a novel task augmentation technique which we use to generate an initial adaptive model. It then uses Bayesian dropout uncertainty estimates to actively select the most difficult samples when updating the model to a new task. This allows AGILE to learn with fewer tasks and a few informative samples, achieving high performance with a limited dataset. We perform our experiments using the brain cell classification task and compare the results to a plain meta-learning model trained from scratch. We show that the proposed task-augmented meta-learning framework can learn to classify new cell types after a single gradient step with a limited number of training samples. We show that active learning with Bayesian uncertainty can further improve the performance when the number of training samples is extremely small. Using only 1% of the training data and a single update step, we achieved 90% accuracy on the new cell type classification task, a 50% points improvement over a state-of-the-art meta-learning algorithm.

0 citations0 reviewsNo code linkedOpen access