DeepSieve: Information Sieving via LLM-as-a-Knowledge-Router

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Large Language Models (LLMs) excel at many reasoning tasks but struggle with knowledge-intensive queries due to their inability to dynamically access up-to-date or domain-specific information. Retrieval-Augmented Generation (RAG) has emerged as a promising solution, enabling LLMs to ground their responses in external sources. However, existing RAG methods lack fine-grained control over both the query and source sides, resulting in noisy retrieval, shallow reasoning, and limited adaptability to heterogeneous knowledge sources. In this work, we introduce DeepSieve, a novel RAG method that incorporates information sieving via LLM-as-a-knowledge-router. DeepSieve breaks down complex queries into structured sub-queries and recursively routes each to the most appropriate knowledge source, filtering out irrelevant information through a multi-stage information sieving process. This modular and transparent approach ensures that DeepSieve remains adaptable across diverse information needs. Experiments on three multi-hop QA benchmarks involving heterogeneous sources show that DeepSieve achieves greater reasoning depth, retrieval precision, and interpretability compared to conventional RAG approaches. Our codes are available at https://github.com/MinghoKwok/DeepSieve.

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Decoding Time Series with LLMs: A Multi-Agent Framework for Cross-Domain Annotation

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Time series data is ubiquitous across various domains, including manufacturing, finance, and healthcare. High-quality annotations are essential for effectively understanding time series and facilitating downstream tasks. However, obtaining such annotations is challenging, particularly in mission-critical domains. In this paper, we propose TESSA, a multi-agent system designed to automatically generate both general and domain-specific annotations for time series data. TESSA introduces two agents: a general annotation agent and a domain-specific annotation agent. The general agent captures common patterns and knowledge across multiple source domains, leveraging both time-series-wise and text-wise features to generate general annotations. Meanwhile, the domain-specific agent utilizes limited annotations from the target domain to learn domain-specific terminology and generate targeted annotations. Extensive experiments on multiple synthetic and real-world datasets demonstrate that TESSA effectively generates high-quality annotations, outperforming existing methods.

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Interpretability and Implicit Model Semantics in Biomedicine and Deep Learning

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We introduce a framework to analyse interpretability in deep learning, by drawing on a formal notion of model semantics from the philosophy of science. We argue that interpretability is only one aspect of a model’s semantics and illustrate our framework with examples from biomedicine.

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Mobile Orbital Domain-based Hierarchical Routing in Satellite Networks

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We propose a mobile orbital domain-based hierarchical routing scheme which addresses the challenges posed by constant satellite movement and the resulting dynamicnetwork topology, thus significantly improving the routing scalability and efficiency in satellite networks.

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Frequency-Division Multiplexed Time-Interleaved Phase-OTDR with Nested Phase References

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We propose a method to compensate the phase offset between samples from different tributaries in time-interleaved phase OTDR using nested phase reference channels. We demonstrate our method for a four-span bidirectional link with high-loss loopback.

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Field study on phase and polarization dynamics of deployed anti-resonant hollow core fiber cable for vibration sensing

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We report the first field study of the phase and polarization dynamics of deployed antiresonant hollow core fiber cable in a data center interconnect for real-world vibration sensing,revealing enhanced phase sensitivity and significantly faster polarization angular rate compared with standard single mode fibers.

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Agnostic QoT Probing via Receiver-Side ASE Loading in a Production Metro for Transparent Datacenter Exchange

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We demonstrate agnostic QoT probing for datacenter exchange in a metro network via receiver-side ASE loading. Knowing BER telemetry and the progressive ASEload, the device estimates GSNR, enabling IPoWDM operations and digital-twin calibration.

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Yangmin Ding Presents at the 5th Workshop on Foundation Models of the Electric Grid on March 18th

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As AI data centers grow, the fiber-optic networks that connect massive computing clusters become critical infrastructure. This talk explores how Distributed Fiber Optic Sensing (DFOS) can turn communication cables into real-time sensors that detect physical threats and improve cyber-physical resilience.

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Influential NEC Researchers in the United States Who Helped Shape Modern Computing

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Many pioneers of modern artificial intelligence and machine learning spent part of their careers at NEC research labs in the United States. Researchers such as Yann LeCun, Vladimir Vapnik, Léon Bottou, Corinna Cortes, and others contributed foundational ideas in deep learning, statistical learning theory, speech recognition, and computer vision.

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Image-Specific Adaptation of Transformer Encoders for Compute-Efficient Segmentation

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Vision transformer-based models bring significant improvements for image segmentation tasks. Although these architectures offer powerful capabilities irrespective of specific segmentation tasks, their use of computational resources can be taxing on deployed devices. One way to overcome this challenge is by adapting the computation level to the specific needs of the input image rather than the current one-size-fits-all approach. To this end, we introduce ECO-M2F or EffiCient TransfOrmer Encoders for Mask2Former-style models. Noting that the encoder module of M2F-style models incurs high resource-intensive computations, ECO-M2F provides a strategy to self-select the number of hidden layers in the encoder, conditioned on the input image. To enable this self-selection ability for providing a balance between performance and computational efficiency, we present a three-step recipe. The first step is to train the parent architecture to enable early exiting from the encoder. The second step is to create a derived dataset of the ideal number of encoder layers required for each training example. The third step is to use the aforementioned derived dataset to train a gating network that predicts the number of encoder layers to be used, conditioned on the input image. Additionally, to change the computational-accuracy tradeoff, only steps two and three need to be repeated, which significantly reduces retraining time. Experiments on the public datasets show that the proposed approach reduces expected encoder computational cost while maintaining performance, adapts to various user compute resources, is flexible in architecture configurations, and can be extended beyond the segmentation task to object detection.

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