xTime: Extreme Event Prediction with Hierarchical Knowledge Distillation and Expert Fusion

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Extreme events frequently occur in real-world time series and often carry significant practical implications. In domains such as climate and healthcare, these events, such as floods, heatwaves, or acute medical episodes, can lead to serious consequences. Accurate forecasting of such events is therefore of substantial importance. Most existing time series forecasting models are optimized for overall performance within the prediction window, but often struggle to accurately predict extreme events, such as high temperatures or heart rate spikes. The main challenges are data imbalance and the neglect of valuable information contained in intermediate events that precede extreme events. In this paper, we propose xTime, a novel framework for extreme event forecasting in time series. xTime leverages knowledge distillation to transfer information from models trained on lower-rarity events, thereby improving prediction performance on rarer ones. In addition, we introduce a MoE mechanism that dynamically selects and fuses outputs from expert models across different rarity levels, which further improves the forecasting performance for extreme events. Experiments on multiple datasets show that xTime achieves consistent improvements, with forecasting accuracy on extreme events improving from 3% to 78%.

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Correlation-aware Online Change Point Detection

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Change point detection aims to identify abrupt shifts occurring at multiple points within a data sequence. This task becomes particularly challenging in the online setting, where different types of change can occur, including shifts in both the marginal and joint distributions of the data. In this paper, we address these challenges by tracking the Riemannian geometry of correlation matrices, allowing Riemannian metrics to compute the geodesic distance as an accurate measure of correlation dynamics.We introduce Rio-CPD, a correlation-aware online change point detection framework that integrates the Riemannian geometry of the manifold of symmetric positive definite matrices with the cumulative sum (CUSUM) statistic for detecting change points. Rio-CPD employs a novel CUSUM design by computing the geodesic distance between current observations and the Fréchet mean of prior observations. With appropriate choices of Riemannian metrics, Rio-CPD offers a simple yet effective and computationally efficient algorithm. We also provide a theoretical analysis on standard metrics for change point detection within Rio-CPD. Experimental results on both synthetic and real-world datasets demonstrate that Rio-CPD outperforms existing methods on detection accuracy, average detection delay, and efficiency.

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Quantitative Bounds for Length Generalization in Transformers

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We study the problem of length generalization (LG) in transformers: the ability of a model trained on shorter sequences to maintain performance when evaluated on much longer, previously unseen inputs. Prior work by Huang et al. (2025) established that transformers eventually achieve length generalization once the training sequence length exceeds some finite threshold, but left open the question of how large it must be. In this work, we provide the first quantitative bounds on the required training length for length generalization to occur. Motivated by previous empirical and theoretical work, we analyze LG in several distinct problem settings: error control vs. average error control over an input distribution, infinite-precision softmax attention vs. finite-precision attention (which reduces to an argmax) in the transformer, and one- vs. two-layer transformers. In all scenarios, we prove that LG occurs when the internal behavior of the transformer on longer sequences can be “simulated” by its behavior on shorter sequences seen during training. Our bounds give qualitative estimates for the length of training data required for a transformer to generalize, and we verify these insights empirically. These results sharpen our theoretical understanding of the mechanisms underlying extrapolation in transformers, and formalize the intuition that richer training data is required for generalization on more complex tasks.

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Neuromorphic Photonics-Enabled Near-Field RF Sensing with Residual Signal Recovery and Classification

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We present near-field radio-frequency (RF) sensing using microwave photonic canceler (MPC) for residual signal recovery and neuromorphic photonic recurrent neural network (PRNN)chip and FPGA hardware to implement machine learning for high-bandwidth and low-latency classification.

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Scalable Photonic Neurons for High-speed Automatic Modulation Classification

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Automatic modulation classification (AMC) is becoming increasingly critical in the context of growing demands for ultra-wideband, low-latency signal intelligence in 5G/6G systems, with photonics addressing the bandwidth and real-time adaptability limitations faced by traditional radio-frequency (RF) electronics. This paper presents the first experimental photonicimplementation of AMC, achieved through a fully functional photonic neural network built from scalable microring resonators that co-integrate electro-optic modulation and weighting. Thiswork also represents a system-level deployment of such compact photonic neurons in a real photonic neural network, demonstrating the significant potential of photonic computing forlarge-scale, complex RF intellegence for next-generation wireless communication systems.

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Sound Event Classification meets Data Assimilation with Distributed Fiber-Optic Sensing

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Distributed Fiber-Optic Sensing (DFOS) is a promising technique for large-scale acoustic monitoring. However, its wide variation in installation environments and sensor characteristics causes spatial heterogeneity. This heterogeneity makes it difficult to collect representative training data. It also degrades the generalization ability of learning-based models, such as fine-tuning methods, under a limited amount of training data. To address this, we formulate Sound Event Classification (SEC) as data assimilation in an embedding space. Instead of training models, we infer sound event classes by combining pretrained audio embeddings with simulated DFOS signals. Simulated DFOS signals are generated by applying various frequency responses and noise patterns to microphone data, which allows for diverse prior modeling of DFOS conditions. Our method achieves out-of-domain (OOD) robust classification without requiring model training. The proposed method achieved accuracy improvements of 6.42, 14.11, and 3.47 percentage points compared with conventional zero-shot and two types of fine-tune methods, respectively. By employing the simulator in the framework of data assimilation, the proposed method also enables precise estimation of physical parameters from observed DFOS signals.

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SlideCraft: Context-aware Slides Generation Agent

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Creating effective slide presentations requires adapting both content and structure to match the communication context e.g. whether the presentation is for summarizing to executives, or reporting progress to research supervisors. In research and enterprise environments, this need for context-sensitive presentations often leads to repeated, manual reformatting of the same material to suit different audiences. Existing generative systems support slide creation but typically rely on structured inputs, assume a fixed format, and offer limited ability to iteratively refine outputs through natural language feedback. Moreover, they rarely accommodate organizational constraints such as formatting guidelines, domain-specific terminology, or branding requirements. We present SlideCraft, a context-aware generative agent that autonomously creates and edits slide presentations based on natural language instructions. SlideCraft infers the intended presentation context, such as an executive-facing or a project review summary for technical oversight, and selects the appropriate slide template. It then synthesizes content from input documents, enriches it with external knowledge and internal assets, assembles it into a structured intermediate representation, and generates a validated slide deck. SlideCraft supports both first-time slide creation and iterative updates, operating through familiar natural language interfaces like email or messaging tools. Our experiments demonstrate that SlideCraft consistently produces high-quality, context-aware presentations tailored to diverse communication settings, with minimal human input and reliable adherence to enterprise constraints.

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TalentScout: Multimodal AI-Driven Expert Finding in Organizations

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Identifying subject-matter experts within organizations remains a challenging task due to the scale, heterogeneity, and unstructured nature of enterprise knowledge assets. We present TalentScout, an AI-driven expert identification system that constructs a unified, skill-centric knowledge graph by ingesting and analyzing diverse media, including research papers, reports, presentations, transcripts, and supervisor recommendations. TalentScout’s modular architecture integrates document parsing, audio/video transcription, metadata extraction, large language model-based skill extraction, multi-factor author disambiguation, and evidence-weighted skill attribution. At query time, TalentScout decomposes natural language queries into canonical skill requirements, traverses the constructed knowledge graph, and ranks experts based on aggregated skill weights, document quality, and endorsement signals, providing document-level justifications for each recommendation. We evaluate TalentScout on multiple public and internal enterprise datasets, including DBLP, TREC Enterprise, Tilburg, and ManConCorpus. Using standard information retrieval metrics such as Precision@ 5, Recall@5, nDCG@5, and Mean Reciprocal Rank (MRR), TalentScout consistently outperforms leading baselines, achieving up to 24% higher Precision@ 5 in early expert retrieval. The results highlight TalentScout’s scalability, transparency, and accuracy, establishing it as a practical solution for evidence-based expert discovery and organizational talent management.

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AutoScape: Geometry-Consistent Long-Horizon Scene Generation

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This paper proposes AutoScape, a long-horizon driving scene generation framework. At its core is a novel RGB-D diffusion model that iteratively generates sparse, geometrically consistent keyframes, serving as reliable anchors for the scenes appearance and geometry. To maintain long-range geometric consistency, the model 1) jointly handles image and depth in a shared latent space, 2) explicitly conditions on the existing scene geometry (i.e., rendered point clouds) from previously generated keyframes, and 3) steers the sampling process with a warp-consistent guidance. Given high-quality RGB-D keyframes, a video diffusion model then interpolates between them to produce dense nd coherent video frames. AutoScape generates realistic and geometrically consistent driving videos of over 20 seconds, improving the long-horizon FID and FVD scores over the prior state-of-the-art by 48.6% and 43.0%, respectively.

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DWIM: Towards Tool-aware Visual Reasoning via Discrepancy-aware Workflow Generation & Instruct-Masking Tuning

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Visual reasoning (VR), which is crucial in many fields for enabling human-like visual understanding, remains highly challenging. Recently, compositional visual reasoning approaches, which leverage the reasoning abilities of large language models (LLMs) with integrated tools to solve problems, have shown promise as more effective strategies than end-to-end VR methods. However, these approaches face limitations, as frozen LLMs lack tool awareness in VR, leading to performance bottlenecks. While leveraging LLMs for reasoning is widely used in other domains, they are not directly applicable to VR due to limited training data, imperfect tools that introduce errors and reduce data collection efficiency in VR, and challenges in fine-tuning on noisy workflows. To address these challenges, we propose DWIM: i) Discrepancy-aware training Workflow generation, which assesses tool usage and extracts more viable workflows for training; and ii) Instruct-Masking fine-tuning, which guides the model to only clone effective actions, enabling the generation of more practical solutions. Our experiments demonstrate that DWIM achieves state-of-the-art performance across various VR tasks, exhibiting strong generalization on multiple widely used datasets.

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