Zhixian Wang

Jizhan Fang, Buqiang Xu, Zhixian Wang et al.

Existing memory-augmented LLM agents often treat memory as a static repository with pre-defined representations and fixed retrieval pipelines, which is brittle in dynamic agentic environments where feedback, task variation, and heterogeneous signals continuously reshape what should be remembered and how it should be connected. To address this, we propose FluxMem, a connectivity-evolving memory framework that models memory as a heterogeneous graph and progressively refines its topology through three stages: initial connection formation, feedback-driven refinement, and long-term consolidation. During execution, FluxMem repairs missing links, prunes interference, aligns abstraction granularity, and distills recurrent successful trajectories into reusable procedural circuits, guided by one metric for memory generalizability and evolutionary maturity. Across three fundamentally distinct benchmarks including LoCoMo, Mind2Web, and GAIA, FluxMem achieves consistent state-of-the-art performance, demonstrating strong adaptation and generalization in complex agentic environments. The code will be open-sourced in https://github.com/zjunlp/LightMem.

Zhixian Wang, Qingsong Wen, Chaoli Zhang et al.

Energy (load, wind, photovoltaic) forecasting is significant in the power industry as it can provide a reference for subsequent tasks such as power grid dispatch, thus bringing huge economic benefits. However, there are many differences between energy forecasting and traditional time series forecasting. On the one hand, traditional time series mainly focus on capturing characteristics like trends and cycles. In contrast, the energy series is largely influenced by many external factors, such as meteorological and calendar variables. On the other hand, energy forecasting aims to minimize the cost of subsequent tasks such as power grid dispatch, rather than simply pursuing prediction accuracy. In addition, the scale of energy data can also significantly impact the predicted results. In this paper, we collected large-scale load datasets and released a new renewable energy dataset that contains both station-level and region-level renewable generation data with meteorological data. For load data, we also included load domain-specific feature engineering and provided a method to customize the loss function and link the forecasting error to requirements related to subsequent tasks (such as power grid dispatching costs), integrating it into our forecasting framework. Based on such a situation, we conducted extensive experiments with 21 forecasting methods in these energy datasets at different levels under 11 evaluation metrics, providing a comprehensive reference for researchers to compare different energy forecasting models.

Zhixian Wang, Qingsong Wen, Chaoli Zhang et al.

Electrical load forecasting plays a crucial role in decision-making for power systems, including unit commitment and economic dispatch. The integration of renewable energy sources and the occurrence of external events, such as the COVID-19 pandemic, have rapidly increased uncertainties in load forecasting. The uncertainties in load forecasting can be divided into two types: epistemic uncertainty and aleatoric uncertainty. Separating these types of uncertainties can help decision-makers better understand where and to what extent the uncertainty is, thereby enhancing their confidence in the following decision-making. This paper proposes a diffusion-based Seq2Seq structure to estimate epistemic uncertainty and employs the robust additive Cauchy distribution to estimate aleatoric uncertainty. Our method not only ensures the accuracy of load forecasting but also demonstrates the ability to separate the two types of uncertainties and be applicable to different levels of loads. The relevant code can be found at \url{https://anonymous.4open.science/r/DiffLoad-4714/}.