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  • Bayesian dynamic scheduling of multipurpose batch processes under incomplete look-ahead information

    Bayesian dynamic scheduling of multipurpose batch processes under incomplete look-ahead information

    [Submitted on 30 Nov 2025 (v1), last revised 9 Oct 2026 (this version, v2)]

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    Abstract:Multipurpose batch processes become increasingly popular in manufacturing industries since they adapt to low-volume, high-value products and shifting demands. These processes often operate in a dynamic environment, which faces disturbances such as processing delays and demand changes. To minimise long-term cost and system nervousness (i.e., disruptive changes to schedules), schedulers must design rescheduling strategies to address such disturbances effectively. Existing methods often assume complete look-ahead information over the scheduling horizon. This assumption contrasts with realistic situations where schedulers can only access incomplete look-ahead information. Sticking with existing methods may lead to suboptimal long-term costs and high-level system nervousness. In this work we propose a Bayesian dynamic scheduling method. Our method relies on learning a Bayesian Network from the probability distribution of disturbances. Specifically, the Bayesian Network represents how likely each operation will be impacted by disturbances. During the online execution, when new disturbances become observed, this method updates the posterior distribution and therefore guides the rescheduling strategy. We compare our method with the existing periodic rescheduling strategy (which generates new schedules from scratch at fixed intervals) on four benchmark problems. Computational results show that our method achieves statistically better long-term costs and system nervousness. In the theoretical aspect, we prove that if disturbances are mutually independent, the impact-quantifying variables inherently satisfy the independence assumptions required by Bayesian Networks. As an implication, practitioners can extend the method to other scheduling problems (such as job shop scheduling and continuous processes), given that they define the problem-specific dependencies between operations.

    Submission history

    From: Taicheng Zheng [view email]
    [v1] Sun, 30 Nov 2025 21:27:52 UTC (1,350 KB)
    [v2] Fri, 9 Oct 2026 09:42:06 UTC (2,695 KB)

  • Kronecker Product Attention: Memory-Efficient KV Compression via Structured Factorization

    Kronecker Product Attention: Memory-Efficient KV Compression via Structured Factorization

    [Submitted on 8 Oct 2026]

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    Abstract:Efficient autoregressive generation is increasingly limited by the memory footprint of the key-value (KV) cache, which grows linearly with sequence length, batch size, and model depth. Existing approaches such as Multi-Query Attention (MQA), Grouped-Query Attention (GQA), Multi-Head Latent Attention (MLA), and Tensor Product Attention (TPA) reduce this cost through different trade-offs, but often sacrifice expressivity, require additional positional-encoding machinery, or impose restrictive low-rank structure. We propose Kronecker Product Attention (KPA), which represents key and value activations as sums of Kronecker products of compact, token-dependent matrix factors. KPA strictly generalizes TPA: rank-one vector-outer-product factorization arises as a degenerate Kronecker split, while non-degenerate matrix factors can represent richer block-structured patterns. We further show that a structured rotary embedding operator matched to the Kronecker representation preserves the relative-position property of RoPE while rotating only the compact factors, removing the need for a decoupled RoPE branch as in MLA. Empirically, KPA matches or exceeds TPA, MHA, MQA, GQA, and MLA across four model scales and nine downstream benchmarks under identical training conditions, while achieving up to 10x KV cache compression. The Kronecker head split is a tuneable hyperparameter that allows practitioners to navigate the compression-accuracy trade-off at design time without architectural changes.

    Submission history

    From: Bahareh Nikpour [view email]
    [v1] Thu, 8 Oct 2026 19:23:56 UTC (227 KB)

  • Constrained Triple-Head Price Optimization (C3PO) Network for Discrete-Choice Models

    Constrained Triple-Head Price Optimization (C3PO) Network for Discrete-Choice Models

    [Submitted on 7 May 2026 (v1), last revised 8 Oct 2026 (this version, v2)]

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    Abstract:We propose a Constrained Triple-Head Price Optimization (C3PO) network for real-time bi-level decision making in discrete-choice environments. In these settings, a seller selects product or service offerings to achieve business objectives, while heterogeneous users make personalized acceptance or rejection decisions based on their preferences, ultimately determining seller outcomes. Existing approaches that fine-tune pre-trained tabular foundation models on synthetic data are limited by their imitation-learning paradigm and inability to enforce common pricing rules, including cross-product and global constraints. C3PO addresses these limitations through a novel transformer-based architecture that jointly integrates price imitation learning, multi-task revenue prediction, and in-context elasticity learning to generate robust pricing recommendations across domains without retraining. A lightweight differentiable constraint layer enables generated prices to satisfy business constraints while optimizing the revenue objective. We demonstrate strong out-of-distribution performance on simulated, synthetic, and real-world datasets. Across simulated benchmarks where the calibrated MNL model is misspecified relative to the ground-truth choice model, C3PO improves expected revenue by 1.4 to 7.9 percentage points over a calibrated MNL baseline. The constraint layer reduces average absolute constraint violations by more than 50%. We further deploy the model in real-world applications, including healthcare tender pricing and airline ancillary pricing, where C3PO improves directional pricing effectiveness, measured by price-increase and price-decrease recall, by 18–34 percentage points depending on the baselines considered, including calibrated MNL, classical optimization, and off-the-shelf tabular foundation-models.

    Submission history

    From: Yingdong Lu [view email]
    [v1] Thu, 7 May 2026 20:55:47 UTC (1,034 KB)
    [v2] Thu, 8 Oct 2026 18:22:06 UTC (1,047 KB)

  • Data-driven Optimal Sensor Placement for Aerosol Monitoring in the UK

    Data-driven Optimal Sensor Placement for Aerosol Monitoring in the UK

    [Submitted on 8 Oct 2026]

    Authors:Talha Ansar (1), Waqar Muhammad Ashraf (2), Alejandro Coca-Castro (2), Manvendra Janmaijaya (2), Ramit Debnath (3), J. Scott Hosking (2) ((1) University of Engineering and Technology, Lahore, Pakistan, (2) The Alan Turing Institute, London, UK, (3) University of Cambridge, Cambridge, UK)

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    Abstract:We develop a data-driven sensor-placement framework and apply it to aerosol monitoring in the United Kingdom (UK). A trained convolutional Gaussian neural process is embedded within the DeepSensor optimisation framework. Genetic optimisation with lazy evaluation and caching reduces mean computational time by 60% (10$pm$3 versus 25$pm$7 minutes) while producing sequential placement performance comparable to the greedy solver. Using the CAMS reference field to construct a retrospective oracle benchmark, Joint NLL improves by about 22% for Joint-MI-driven genetic placement after five locations for aerosol stations are identified. Seasonal and wind-regime analyses show that agreement between model-based criteria and oracle benchmarks varies with meteorological transport conditions. These results show a computationally efficient approach for data-informed expansion of environmental observing systems.

    Submission history

    From: Talha Ansar Ta [view email]
    [v1] Thu, 8 Oct 2026 19:25:38 UTC (1,478 KB)

  • Urban Power Grid Topology and Hierarchy Identification from Open Data

    Urban Power Grid Topology and Hierarchy Identification from Open Data

    [Submitted on 19 Jun 2026 (v1), last revised 9 Oct 2026 (this version, v2)]

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    Abstract:Understanding the complex topology and hierarchy of urban power grid is crucial for energy prognosis, power flow management, and system resilience analysis. However, detailed grid information remains largely proprietary. This information, nevertheless, is crucial especially for analyzing the last-mile distribution networks connecting individual buildings. This paper addresses this challenge by developing an open-data-driven framework to identify urban power grid topology, from high-voltage transmission down to individual building connections. We integrate public infrastructure data (power-lines, substations, transformers, poles) to map the high and medium-voltage grid skeleton. We then leverage geospatial machine learning on OpenStreetMap building data to cluster loads of building energy demand. Upon the clustering, we develop the physical topology of the final distribution lines linking the clustered buildings and low-voltage distribution grids. Our derived grid topology incorporates physical and geographical constraints like voltage level and road networks, thus leading to reasonable topology and hierarchy of grid networks. We apply the developed framework to the districts of Alna in Oslo, Norway, and Waiheke Island in New Zealand, and we reconstruct the complete grid topology that connects all the buildings, power lines, and electricity infrastructure assets. With the developed framework, we provide a critical tool that facilitates power system analysis, e.g., power flow optimization, cascading failure simulation, and grid resilience against the penetration of distributed renewable generation. This work also opens the opportunity to build energy digital twins that fuse power assets and grid topology, geographic constraints, and fine-grained power demand profiles.

    Submission history

    From: Shiliang Zhang [view email]
    [v1] Fri, 19 Jun 2026 11:51:38 UTC (12,892 KB)
    [v2] Fri, 9 Oct 2026 13:49:37 UTC (13,449 KB)

  • Recurrent Harness for Agentic Continual Learning

    Recurrent Harness for Agentic Continual Learning

    [Submitted on 8 Oct 2026]

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    Abstract:Language model agents increasingly operate over long interaction streams, requiring online adaptation to distribution shifts through continual learning. However, existing agent harnesses typically either evolve their states only through forward information flow or rely on additional replay or validation data for harness optimization. To address this, we propose ReHarness, a recurrent harness framework for agentic continual learning that complements forward read and write with backward revision under a strictly online streaming protocol. We ask what information could have been captured differently in the harness state to better inform the current instance in the stream. Specifically, after each instance, we leverage the resulting trajectory as feedback to revise the reflections retrieved for that instance, thereby propagating feedback backward through the textual harness state. Across three continual learning benchmarks and three language model backbones, ReHarness consistently outperforms strong raw-context baselines, memory- and reflection-based methods, and prompt optimization approaches. Further analysis shows that ReHarness produces source-grounded and meaningful revisions, corrects overgeneralized or outdated experience under distribution shifts, and improves both stability and plasticity during continual learning.

    Submission history

    From: Yuxin Xiao [view email]
    [v1] Thu, 8 Oct 2026 19:44:43 UTC (422 KB)

  • Kudos 3.0

    The AI that finds money you’re missing and gets it back

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  • Critical collaging as an AI resistance strategy

    Critical collaging as an AI resistance strategy

    Client Challenge
  • A light framework for AI ethics

    A light framework for AI ethics

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  • Governing artificial intelligence in criminal justice systems: ethical, legal, and policy challenges

    Governing artificial intelligence in criminal justice systems: ethical, legal, and policy challenges

    Client Challenge