Automated Negotiation and Multimodal Time-Series Forecasting for Efficient Procurement

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Procurement is a key function in supply chain management that involves acquiring goods and services to meet organizational needs. Efficient procurement is crucial for minimizing costs, ensuring timely delivery, and maintaining quality standards. This paper explores the integration of automated negotiation and multimodal time-series forecasting to enhance procurement processes. Automated negotiation can streamline interactions with suppliers, while multimodal time-series forecasting can improve demand prediction accuracy by leveraging diverse data sources leading to better negotiation outputs. By combining these approaches, organizations can optimize procurement strategies, reduce costs, and improve overall supply chain efficiency. We present two case studies using simulations based on real-world data for procurement that show the effectiveness of the proposed framework.

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Open-SAT: LLM-Guided Query Embedding Refinement for Open-Vocabulary Object Retrieval in Satellite Imagery

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In satellite applications, user queries often take the form of open-ended natural language, extending beyond a fixed set of predefined categories. This open-vocabulary nature poses significant challenges for retrieving relevant image tiles, as the retrieval system must generalize to a wide range of unseen objects and concepts. While vision-language models (VLMs) such as CLIP are widely used for text-image retrieval, even fine-tuned variants often struggle to accurately align such queries with satellite imagery. To address this, we propose Open-SAT, a training-free query embedding refinement algorithm that operates at inference time to improve alignment between user queries and satellite image content. Open-SAT uses VLMs to compute embeddings for image tiles, which are stored in a vector database for efficient retrieval. At query time, it leverages Large Language Models (LLMs) to refine the text embeddings by incorporating contextual information about objects of interest and their surroundings. A threshold-free retrieval mechanism further enhances accuracy and efficiency. Experimental results in three public benchmarks demonstrate that Open-SAT improves the F1 score by up to 16.04%, while retrieving a comparable number of image tiles. These results demonstrate the effectiveness of Open-SAT in open-vocabulary satellite image retrieval, leveraging LLM guidance without the need for additional training or supervision.

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Closed-Form Statistical Modeling of PDL-Induced SNR Margins for Reliable Optical Networks

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We develop closed-form formulas for PDL-induced SNR margins using solutions based on central limit theorem. Experimental validations confirm accurate and conservative performancepredictions, enabling precise quality of transmission assessment and margin-aware design in optical networks.

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RunAgent: Interpreting Natural-Language Plans with Constraint-Guided Execution (IEEE)

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Humans solve problems by executing targeted plans, yet large language models (LLMs) remain unreliable for structured workflow execution. We propose RunAgent, a multiagent plan execution platform that interprets natural-language plans while enforcing stepwise execution through constraints and rubrics. RunAgent bridges the expressiveness of natural language and the determinism of programming languages via an agentic language with explicit control constructs (e.g., IF, GOTO, FORALL). It autonomously derives and verifies constraints at each step; dynamically selects among LLM reasoning, tool use, and Python execution; and integrates error correction to ensure correctness. Evaluations on Natural-plan and SciBench Datasets demonstrate that RunAgent outperforms baseline LLMs and state-of-the-art PlanGEN methods.

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Event Classification by Physics-Informed Inpainting for Distributed Multichannel Acoustic Sensor with Partially Degraded Channels

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Distributed multichannel acoustic sensing (DMAS) enables large-scale sound event classification (SEC), but performance drops when many channels are degraded and when sensor layouts at test time differ from training layouts. We propose a learning-free, physics-informed inpainting frontend based on reverse time migration (RTM). In this approach, observed multichannel spectrograms are first back-propagated on a 3D grid using an analytic Green’s function to form a scene-consistent image, and then forward-projected to reconstruct inpainted signals before log–mel feature extraction and transformer-based classification. We evaluate the method on ESC-50 with 50 sensors and three layouts (circular, linear, right-angle), where per-channel SNRs are sampled from ?30 to 0 dB. Compared with an AST baseline, scaling-sparsemax channel selection, and channel-swap augmentation, the proposed RTM frontend achieves the best or competitive accuracy across all layouts, improving accuracy by 13.1 points on the right-angle layout (from 9.7% to 22.8%). Correlation analyses show that spatial weights align more strongly with SNR than with channel–source distance, and that higher SNR–weight correlation corresponds to higher SEC accuracy. These results demonstrate that a reconstruct-then-project, physics-based preprocessing effectively complements learning-only methods for DMAS under layout-open configurations and severe channel degradation.

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Learning to Tune OpticalWANs: A Field Deployment of Noise Models in Optical Networks

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Accurately modeling optical signal transmission is critical foroptimizing network performance, particularly in large-scalefiber optic networks operated by Internet Service Providers.In this work, we develop a Gaussian Noise model for a NewYork state ISP’s optical backbone. Our model accounts for allmajor network components, including amplifiers, fiber spans,reconfigurable optical add-drop multiplexers, and transceivers.By accurately predicting end-to-end signal-to-noise ratio, ourmodel provides a foundation for network performance analysisand optimization. Then, we leverage hyperparameter searchtechniques—commonly used in machine learning—to identifyamplifier gain settings that improve signal quality. By treatingthe model as an opaque box, we systematically search foramplifier configurations that maximize the predicted end-to-end SNR while maintaining practical network constraints. Wevalidate our approach through a field deployment by applyingoptimized amplifier gain settings in a live ISP network. Ourresults show a significant improvement in optical signal quality,achieving a 2 dB increase in SNR on a single wavelength 1.

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Mix-Clap: Adaptive Fusion of Knowledge-Distilled Audio Embeddings for Noise-Aware Audio-Language Models

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Real-world deployment requires sound event and acoustic scene classification systems to remain reliable in noisy, diverse environments on resource-constrained devices. Although contrastive language-audio pretraining (CLAP) models with Transformer-based audio encoders achieve strong zero-shot performance, their computational cost hinders deployment. In this paper, we propose Mix-CLAP, a computationally efficient, noise-aware CLAP model with knowledge-distilled audio encoders. Our method includes: (1) a two-stage knowledge distillation from teacher embeddings to two lightweight student encoders?one on clean audio, the other on noisy audio, and (2) adaptive inference that combines their embeddings together with a fusion parameter and minimizes the parameterized entropy at test time. Experiments show that Mix-CLAP with MobileNetV3-based audio encoders greatly improves computational efficiency, while achieving a comparable average accuracy of 52.58% to the Transformer-based CLAP model at 52.83% on the recorded ESC50 datasets with different devices including microphones and fiber-optic distributed acoustic sensors under diverse conditions, making it suitable for real-world, resource-constrained applications.

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PhyCo: Learning Controllable Physical Priors for Generative Motion

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Modern video diffusion models excel at appearance synthesis but still struggle with physical consistency: objects drift, collisions lack realistic rebound, and material responses seldom match their underlying properties. We present PhyCo, a framework that introduces continuous, interpretable, and physically grounded control into video generation. Our approach integrates three key components: (i) a large-scale dataset of over 100K photorealistic simulation videos where friction, restitution, deformation, and force are systematically varied across diverse scenarios; (ii) physics-supervised fine-tuning of a pretrained diffusion model using a ControlNet conditioned on pixel-aligned physical property maps; and (iii) VLM-guided reward optimization, where a fine-tuned vision-language model evaluates generated videos with targeted physics queries and provides differentiable feedback. This combination enables a generative model to produce physically consistent and controllable outputs through variations in physical attributes-without any simulator or geometry reconstruction at inference. On the Physics-IQ benchmark, PhyCo significantly improves physical realism over strong baselines, and human studies confirm clearer and more faithful control over physical attributes. Our results demonstrate a scalable path toward physically consistent, controllable generative video models that generalize beyond synthetic training environments.

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GNPy as a Benchmark for Open and Disaggregated Optical Networks

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The evolution toward open and partially disaggregated optical networks has introduced new, to our knowledge,requirements on how transmission performance is evaluated and compared across technologies, vendors, and deployment scenarios. In this context, sound benchmarking practices are essential to ensure that quality-of-transmission (QoT) assessments are reproducible, transparent, and meaningful beyond isolated experimental demonstrations. QoT estimation plays a central role in these practices, as it directly impacts network planning,commissioning, automation, and long-term technology selection in heterogeneous optical infrastructures. This paper discusses benchmarking practices for optical transmission in open networks using the open-source GNPy library as a reference digital model. The contribution of this work lies in formalizing how a transparent, vendor-agnostic QoT estimator can be used as a common benchmarking baseline across research and industry. Representative experimental validations spanning short-reach, multiband, and multi-vendor flex-grid transmission scenarios are reviewed and reframed as benchmarking baselines, establishing evidence-based expectations on achievable accuracy and applicability limits under realistic operating conditions. Finally, the paper illustrates how reference QoT models are employed in industry-facing benchmarking workflows,including closed-loop interactions with standardization bodies, multi-vendor planning and automation,procurement processes and strategic network evolution toward emerging architectures.

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Solving Inverse Problems via a Score-Based Prior: An Approximation-Free Posterior Sampling Approach

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Diffusion models (DMs) have proven to be effective in modeling high-dimensional distributions, leading to their widespread adoption for representing complex priors in Bayesian inverse problems (BIPs). However, current DM-based posterior sampling methods proposed for solving common BIPs rely on heuristic approximations to the generative process. To exploit the generative capability of DMs and avoid the usage of such approximations, we propose an ensemble-based algorithm that performs posterior sampling without the use of heuristic approximations. Our algorithm is motivated by existing work that combines DM-based methods with the sequential Monte Carlo (SMC) method. By examining how the prior evolves through the diffusion process encoded by the pre-trained score function, we derive a modified partial differential equation (PDE) governing the evolution of the corresponding posterior distribution. This PDE includes a modified diffusion term and a reweighting term, which can be simulated via stochastic weighted particle methods. Theoretically, we prove that the error between the true posterior and the empirical distribution of the generated samples can be bounded in terms of the training error of the pre-trained score function and the number of particles in the ensemble. Empirically, we validate our algorithm on several inverse problems in imaging to show that our method gives more accurate reconstructions compared to existing DM-based methods.

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