5 papers
READ-Bench: Benchmarking Historical Instance Retrieval for Time-Series Diagnosis
Gerardo Pastrana, Haojun Li, Dhruv Mehta +4
Time-series diagnostic systems rarely rely on retrieving relevant historical cases, and when they do, retrieval is evaluated only indirectly through downstream prediction. We intro…
Data Intelligence Agents: Interpreting, Modeling, and Querying Enterprise Data via Autonomous Coding Agents
Anoushka Vyas, Aarushi Dhanuka, Sina Khoshfetrat Pakazad +1
Production data integration is bottlenecked by repeated, lossy handoffs between data owners, engineers, and analysts who must collaboratively discover, structure, and query enterpr…
Giving Sensors a Voice: Multimodal JEPA for Semantic Time-Series Embeddings
Utsav Dutta, Gerardo Pastrana, Sina Khoshfetrat Pakazad +1
Transformer-based architectures have advanced sequence modeling in language and vision, yet general-purpose representation learning for heterogeneous multivariate time series remai…
NEMO: Execution-Aware Optimization Modeling via Autonomous Coding Agents
Yang Song, Anoushka Vyas, Zirui Wei +3
We present NEMO, a system that translates Natural-language descriptions of decision problems into formal Executable Mathematical Optimization implementations using autonomous codin…
Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions
Utsav Dutta, Sina Khoshfetrat Pakazad, Henrik Ohlsson
Traditional time series models are task-specific and often depend on dataset-specific training and extensive feature engineering. While Transformer-based architectures have improve…