Customer-obsessed science

E-commerce stores rely on product catalog data, which can be enriched by automated mechanisms like visual attribute extraction, for features like search and filtering. Extracting visual attributes from product images in e-commerce is challenging due to the wide diversity in products and the high cost of manual labeling, making traditional methods that rely on human-annotated data often impractical. In this

We study stochastic bandits in which observing a reward is optional but incurs an action-dependent cost. This setting captures applications where feedback acquisition (e.g., human evaluation or randomized testing) is expensive, and the learner must trade off exploration, exploitation, and observation cost. We formulate regret to include both reward loss and the cumulative cost of requested observations.

Large language models (LLMs) are good at generating code, but remain brittle for formal verification in systems like LEAN4. A core scalability challenge is that verified synthesis requires consistent outputs across multiple artifacts: executable code, precise specifications, theorem statements, and ultimately proofs. Existing approaches rarely treat these as a unified pipeline. We present BRIDGE, a structured

As advanced RAG variants like GraphRAG and Agentic RAG emerge, one leading question is when and how to use them. Here, we introduce a framework for different RAG scenarios evaluation and comparison on semi-structured knowledge bases, including regular RAG, GraphRAG, Modular RAG and Agentic RAG. We provide implementation for 9 standardized RAG scenarios, and conduct experiments for a comprehensive comparison

Simulation models are widely used for decision support in manufacturing systems, yet their accuracy degrades over time as real-world operations evolve. Existing approaches to model maintenance rely heavily on manual diagnosis by domain experts, creating a bottleneck in sustaining model fidelity. This paper proposes a methodology for root cause attribution of simulation model drift using system logs and