collaborators

6 papers

cs.LG2026

PocketTSLM: Deploying an On-Device Soft-Prompt Time-Series Language Model for Privacy-Preserving Smartphone Reasoning

Max Rosenblattl, Nikhil Krishnaswamy, Leon Nissen +9

Consumer phones and wearables increasingly collect physiological time-series data, yet most mobile systems treat these streams as summaries or text rather than as temporal evidence…

cs.LG2026

TimeCap: A Scalable Open-Source Pipeline for Captioning Wearable Health Time-Series Data

Max Rosenblattl, Milana Gurbanova, Karl Aurel Deck +16

Time-series data generated continuously across industrial, environmental, and especially health domains produce multimodal signals whose scale and heterogeneity make their processi…

cs.LG2026

TimeRLM: Recursive Language Models Enable Precise Anomaly Localization in Long-Context Time-Series

Nicolas Zumarraga, Lorenzo Steno, Ning Wang +9

Precise anomaly localization over long-context time series is a crucial task in monitoring applications across clinical care, industrial operations, financial services, and logisti…

cs.CL2026

Safety-Oriented Evaluation of Language Understanding Systems for Air Traffic Control

Yujing Chang, Yash Guleria, Duc-Thinh Pham +4

Air Traffic Control (ATC) is a safety-critical domain in which incorrect interpretation of instructions may lead to severe operational consequences. While large language models (LL…

cs.LG2026

TS-Haystack: A Multi-Task Retrieval Benchmark for Long-Context Time-Series Reasoning

Nicolas Zumarraga, Thomas Kaar, Ning Wang +12

Time Series Language Models (TSLMs) promise reasoning over real-world temporal data, but their ability to retrieve and reason over long time-series remains largely untested. We int…

cs.LG2025

OpenTSLM: Time-Series Language Models for Reasoning over Multivariate Medical Text- and Time-Series Data

Patrick Langer, Thomas Kaar, Max Rosenblattl +19

LLMs have emerged as powerful tools for interpreting multimodal data. In medicine, they hold particular promise for synthesizing large volumes of clinical information into actionab…