Abstract
Data discovery and preparation remain persistent bottlenecks in the data management lifecycle, especially when user intent is vague, evolving, or difficult to operationalize. The Pneuma Project introduces Pneuma-Seeker, a system that helps users articulate and fulfill information needs through iterative interaction with a language model-powered platform. The system reifies the user's evolving information need as a relational data model and incrementally converges toward a usable document aligned with that intent. To achieve this, the system combines three architectural ideas: context specialization to reduce LLM burden across subtasks, a conductor-style planner to assemble dynamic execution plans, and a convergence mechanism based on shared state. The system integrates recent advances in retrieval-augmented generation (RAG), agentic frameworks, and structured data preparation to support semi-automatic, language-guided workflows. We evaluate the system through LLM-based user simulations and show that it helps surface latent intent, guide discovery, and produce fit-for-purpose documents. It also acts as an emergent documentation layer, capturing institutional knowledge and supporting organizational memory.

Why we are recommending this paper?
Due to your Interest in Product Roadmap

This paper directly addresses the need for aligning data with user intent, a core concern in product strategy and vision setting. The system's focus on automating discovery and preparation aligns perfectly with your interest in streamlining product roadmap development.

AI Insights

• The study relies heavily on historical data, which may not reflect current market trends or conditions. [3]
• The paper discusses the application of machine learning in supply chain management, specifically in predicting inventory levels and demand forecasting. [2]

Abstract
Demand forecasting in supply chain management (SCM) is critical for optimizing inventory, reducing waste, and improving customer satisfaction. Conventional approaches frequently neglect external influences like weather, festivities, and equipment breakdowns, resulting in inefficiencies. This research investigates the use of machine learning (ML) algorithms to improve demand prediction in retail and vending machine sectors. Four machine learning algorithms. Extreme Gradient Boosting (XGBoost), Autoregressive Integrated Moving Average (ARIMA), Facebook Prophet (Fb Prophet), and Support Vector Regression (SVR) were used to forecast inventory requirements. Ex-ternal factors like weekdays, holidays, and sales deviation indicators were methodically incorporated to enhance precision. XGBoost surpassed other models, reaching the lowest Mean Absolute Error (MAE) of 22.7 with the inclusion of external variables. ARIMAX and Fb Prophet demonstrated noteworthy enhancements, whereas SVR fell short in performance. Incorporating external factors greatly improves the precision of demand forecasting models, and XGBoost is identified as the most efficient algorithm. This study offers a strong framework for enhancing inventory management in retail and vending machine systems.

Why we are recommending this paper?
Due to your Interest in AI for Product Management

Given your interest in product management, this research on optimizing inventory through predictive models is highly relevant. Utilizing context-augmented machine learning offers a powerful approach to improving supply chain efficiency, a key aspect of product strategy.

Abstract
Collaborative perception (CP) is a critical technology in applications like autonomous driving and smart cities. It involves the sharing and fusion of information among sensors to overcome the limitations of individual perception, such as blind spots and range limitations. However, CP faces two primary challenges. First, due to the dynamic nature of the environment, the timeliness of the transmitted information is critical to perception performance. Second, with limited computational power at the sensors and constrained wireless bandwidth, the communication volume must be carefully designed to ensure feature representations are both effective and sufficient. This work studies the dynamic scheduling problem in a multi-region CP scenario, and presents a Timeliness-Aware Multi-region Prioritized (TAMP) scheduling algorithm to trade-off perception accuracy and communication resource usage. Timeliness reflects the utility of information that decays as time elapses, which is manifested by the perception performance in CP tasks. We propose an empirical penalty function that maps the joint impact of Age of Information (AoI) and communication volume to perception performance. Aiming to minimize this timeliness-oriented penalty in the long-term, and recognizing that scheduling decisions have a cumulative effect on subsequent system states, we propose the TAMP scheduling algorithm. TAMP is a Lyapunov-based optimization policy that decomposes the long-term average objective into a per-slot prioritization problem, balancing the scheduling worth against resource cost. We validate our algorithm in both intersection and corridor scenarios with the real-world Roadside Cooperative perception (RCooper) dataset. Extensive simulations demonstrate that TAMP outperforms the best-performing baseline, achieving an Average Precision (AP) improvement of up to 27% across various configurations.

Why we are recommending this paper?
Due to your Interest in Vision Setting for Tech Teams

This paper’s focus on collaborative perception aligns with the need to understand how data is gathered and utilized across different systems, a crucial element of product strategy. The work addresses the challenges of data fusion, directly relevant to your interests in product roadmap development.

Abstract
This paper examines the European Union's emerging regulatory landscape - focusing on the AI Act, corporate sustainability reporting and due diligence regimes (CSRD and CSDDD), and data center regulation - to assess whether it can effectively govern AI's environmental footprint. We argue that, despite incremental progress, current approaches remain ill-suited to correcting the market failures underpinning AI-related energy use, water consumption, and material demand. Key shortcomings include narrow disclosure requirements, excessive reliance on voluntary standards, weak enforcement mechanisms, and a structural disconnect between AI-specific impacts and broader sustainability laws. The analysis situates these regulatory gaps within a wider ecosystem of academic research, civil society advocacy, standard-setting, and industry initiatives, highlighting risks of regulatory capture and greenwashing. Building on this diagnosis, the paper advances strategic recommendations for the COP30 Action Agenda, calling for binding transparency obligations, harmonized international standards for lifecycle assessment, stricter governance of data center expansion, and meaningful public participation in AI infrastructure decisions.

Why we are recommending this paper?
Due to your Interest in AI for Product Management

Considering your interest in AI for product management, this paper explores the regulatory landscape surrounding AI's environmental impact. Understanding these considerations is increasingly important for responsible product development and strategic planning.

Abstract
Video see-through (VST) technology aims to seamlessly blend virtual and physical worlds by reconstructing reality through cameras. While manufacturers promise perceptual fidelity, it remains unclear how close these systems are to replicating natural human vision across varying environmental conditions. In this work, we quantify the perceptual gap between the human eye and different popular VST headsets (Apple Vision Pro, Meta Quest 3, Quest Pro) using psychophysical measures of visual acuity, contrast sensitivity, and color vision. We show that despite hardware advancements, all tested VST systems fail to match the dynamic range and adaptability of the naked eye. While high-end devices approach human performance in ideal lighting, they exhibit significant degradation in low-light conditions, particularly in contrast sensitivity and acuity. Our results map the physiological limitations of digital reality reconstruction, establishing a specific perceptual gap that defines the roadmap for achieving indistinguishable VST experiences.

Why we are recommending this paper?
Due to your Interest in Vision Setting for Tech Teams

While focused on a specific technology, this research investigates the fundamental challenges of human-computer interaction, which is a core consideration in product design and user experience. The exploration of perceptual fidelity is relevant to understanding how future technologies might impact product development.