AI Insights

• ORKG (Open Research Knowledge Graph): A large-scale knowledge graph that integrates various sources of research information. [3]
• The paper discusses the development of an AI-supported research platform called Tib Aissistant, which aims to facilitate research across various life cycles. [2]
• Tib Aissistant's architecture is based on a modular design, with components for prompt engineering, tool integration, and knowledge graph-based search. [1]

Abstract
The rapid advancements in Generative AI and Large Language Models promise to transform the way research is conducted, potentially offering unprecedented opportunities to augment scholarly workflows. However, effectively integrating AI into research remains a challenge due to varying domain requirements, limited AI literacy, the complexity of coordinating tools and agents, and the unclear accuracy of Generative AI in research. We present the vision of the TIB AIssistant, a domain-agnostic human-machine collaborative platform designed to support researchers across disciplines in scientific discovery, with AI assistants supporting tasks across the research life cycle. The platform offers modular components - including prompt and tool libraries, a shared data store, and a flexible orchestration framework - that collectively facilitate ideation, literature analysis, methodology development, data analysis, and scholarly writing. We describe the conceptual framework, system architecture, and implementation of an early prototype that demonstrates the feasibility and potential impact of our approach.

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

This paper directly addresses the potential of AI to transform research workflows, aligning with your interest in AI for research processes. It explores how AI can augment scholarly workflows, a key area of interest given your focus on AI applications.

AI Insights

• The paper presents a benchmark for evaluating automatic data product creation called DP-Bench. [3]
• DP-Bench contains manually vetted data product requests (DPRs) and corresponding data products with non-derived columns and derived columns. [3]
• Each data product is accompanied by a set of sample questions that can be answered by the data product and are related to the corresponding DPR. [3]
• The paper proposes various evaluation metrics for automatic data product generation, including precision, recall, F1-score, and mean average precision. [3]
• Data Product (DP): A collection of columns from one or more databases that are relevant to a specific business problem or use case. [3]
• Data Product Request (DPR): A short description of the business use case or problem that the data product is intended to solve. [3]
• Derived Column: A column in the data product that is not present in the original database but can be inferred from other columns. [3]
• The authors provide a rigorous experimental analysis of several approaches to automatic data product generation and propose various evaluation metrics for this task. [3]
• The authors also provide a rigorous experimental analysis for several approaches to automatic data product generation. [2]

Abstract
A data product is created with the intention of solving a specific problem, addressing a specific business usecase or meeting a particular need, going beyond just serving data as a raw asset. Data products enable end users to gain greater insights about their data. Since it was first introduced over a decade ago, there has been considerable work, especially in industry, to create data products manually or semi-automatically. However, there exists hardly any benchmark to evaluate automatic data product creation. In this work, we present a benchmark, first of its kind, for this task. We call it DP-Bench. We describe how this benchmark was created by taking advantage of existing work in ELT (Extract-Load-Transform) and Text-to-SQL benchmarks. We also propose a number of LLM based approaches that can be considered as baselines for generating data products automatically. We make the DP-Bench and supplementary materials available in https://huggingface.co/datasets/ibm-research/dp-bench .

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

Given your interest in product strategy and product management, this paper offers a valuable framework for evaluating data product creation, a core component of successful product development. Understanding how to build effective data products is essential for strategic decision-making.

AI Insights

• Nano Banana Pro is a generative model that has been evaluated on various low-level vision tasks such as image denoising, deraining, and shadow removal. [3]
• The model's performance is generally inferior to state-of-the-art models in terms of pixel-level fidelity metrics like PSNR and SSIM. [3]
• However, Nano Banana Pro demonstrates strong semantic and structural priors, which can be beneficial in scenarios with limited supervision or severe information loss. [3]
• The model's instruction-following behavior can lead to unintended alterations in background elements due to an inherent bias in fine-grained semantic understanding. [3]
• Generative models: These are deep learning models that can generate new data samples based on a given input. [3]
• They have been widely used in various applications such as image synthesis, text-to-image translation, and data augmentation. [3]
• Semantic understanding: This refers to the ability of a model to understand the meaning and context of an input. [3]
• Future work will focus on employing prompt tuning to enhance pixel-level restoration accuracy for low-level vision tasks while preserving the model's strong semantic generation capabilities. [2]

Abstract
The rapid evolution of text-to-image generation models has revolutionized visual content creation. While commercial products like Nano Banana Pro have garnered significant attention, their potential as generalist solvers for traditional low-level vision challenges remains largely underexplored. In this study, we investigate the critical question: Is Nano Banana Pro a Low-Level Vision All-Rounder? We conducted a comprehensive zero-shot evaluation across 14 distinct low-level tasks spanning 40 diverse datasets. By utilizing simple textual prompts without fine-tuning, we benchmarked Nano Banana Pro against state-of-the-art specialist models. Our extensive analysis reveals a distinct performance dichotomy: while \textbf{Nano Banana Pro demonstrates superior subjective visual quality}, often hallucinating plausible high-frequency details that surpass specialist models, it lags behind in traditional reference-based quantitative metrics. We attribute this discrepancy to the inherent stochasticity of generative models, which struggle to maintain the strict pixel-level consistency required by conventional metrics. This report identifies Nano Banana Pro as a capable zero-shot contender for low-level vision tasks, while highlighting that achieving the high fidelity of domain specialists remains a significant hurdle.

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

This research investigates the capabilities of a cutting-edge vision model, directly relating to your interest in vision setting for tech teams and exploring the potential of new technologies. Evaluating these models is crucial for informed decision-making within a visual context.

Abstract
Recent reinforcement learning (RL) approaches like outcome-supervised GRPO have advanced chain-of-thought reasoning in Vision Language Models (VLMs), yet key issues linger: (i) reliance on costly and noisy hand-curated annotations or external verifiers; (ii) flat and sparse reward schemes in GRPO; and (iii) logical inconsistency between a chain's reasoning and its final answer. We present Puzzle Curriculum GRPO (PC-GRPO), a supervision-free recipe for RL with Verifiable Rewards (RLVR) that strengthens visual reasoning in VLMs without annotations or external verifiers. PC-GRPO replaces labels with three self-supervised puzzle environments: PatchFit, Rotation (with binary rewards) and Jigsaw (with graded partial credit mitigating reward sparsity). To counter flat rewards and vanishing group-relative advantages, we introduce a difficulty-aware curriculum that dynamically weights samples and peaks at medium difficulty. We further monitor Reasoning-Answer Consistency (RAC) during post-training: mirroring reports for vanilla GRPO in LLMs, RAC typically rises early then degrades; our curriculum delays this decline, and consistency-enforcing reward schemes further boost RAC. RAC correlates with downstream accuracy. Across diverse benchmarks and on Qwen-7B and Qwen-3B backbones, PC-GRPO improves reasoning quality, training stability, and end-task accuracy, offering a practical path to scalable, verifiable, and interpretable RL post-training for VLMs.

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

This paper focuses on reinforcement learning and vision-centric reasoning, aligning with your interest in AI for product management and the development of intelligent systems. The exploration of reward schemes is particularly relevant to strategic optimization.

AI Insights

• EV-specific closeness: The average pairwise closeness between products in each chapter pair (i.e., sum over Cij normalized by Nj). [3]
• Closeness gain to EV specific products: Normalized closeness between the 30 HS chapters with the highest EU export value in 2022 to EV specific products, measured as the average pairwise closeness between products in each chapter pair (i.e., sum over Cij normalized by Nj). [3]
• RCA: Revealed Comparative Advantage. [3]
• A measure of a country's comparative advantage in producing a particular product or industry. [3]
• The study uses a component-based approach combining firm-level and trade data to examine the structural transformation of the automotive industry. [2]

Abstract
The automotive industry is undergoing transformation, driven by the electrification of powertrains, the rise of software-defined vehicles, and the adoption of circular economy concepts. These trends blur the boundaries between the automotive sector and other industries. Unlike internal combustion engine (ICE) production, where mechanical capabilities dominated, competitiveness in electric vehicle (EV) production increasingly depends on expertise in electronics, batteries, and software. This study investigates whether and how firms' ability to leverage cross-industry diversification contributes to competitive advantage. We develop a country-level product space covering all industries and an industry-specific product space covering over 900 automotive components. This allows us to identify clusters of parts that are exported together, revealing shared manufacturing capabilities. Closeness centrality in the country-level product space, rather than simple proximity, is a strong predictor of where new comparative advantages are likely to emerge. We examine this relationship across industrial sectors to establish patterns of path dependency, diversification and capability formation, and then focus on the EV transition. New strengths in vehicles and aluminium products in the EU are expected to generate 5 and 4.6 times more EV-specific strengths, respectively, than other EV-relevant sectors over the next decade, compared to only 1.6 and 4.5 new strengths in already diversified China. Countries such as South Korea, China, the US and Canada show strong potential for diversification into EV-related products, while established producers in the EU are likely to come under pressure. These findings suggest that the success of the automotive transformation depends on regions' ability to mobilize existing industrial capabilities, particularly in sectors such as machinery and electronic equipment.

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

This paper analyzes the evolving automotive industry, a significant area of strategic importance given your interest in product strategy and product roadmap development. Understanding industry trends is vital for anticipating future product needs.