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. This representation is poorly matched to personal health systems that need to retrieve, route, or summarize sensor evidence while preservi...
Max Rosenblattl, Nikhil Krishnaswamy, Leon Nissen et al.· 0 citations
Time-series data generated continuously across industrial, environmental, and especially health domains produce multimodal signals whose scale and heterogeneity make their processing and interpretation particularly challenging. Time-Series Language Models (TSLMs) promise scalable interpretation of these signals, yet re...
Max Rosenblattl, Milana Gurbanova, Karl Aurel Deck et al.· 0 citations
Retrieval-augmented generation (RAG) is a widely used approach for grounding large language models (LLMs) in external knowledge. However, configuring a pipeline is an expensive hyperparameter optimization problem over many interacting choices, from chunking and embedding model to reranking and generation. Existing opti...
Lasse B. Strand, Robert Jakob, Kevin O'Sullivan et al.· 0 citations
TimeNet provides the data and systems foundation needed to move beyond task- and dataset-specific TFMs toward models that can learn jointly across heterogeneous domains, modalities, temporal scales, and forms of supervision from a common data model.
Martin Maritsch, Timo Stoffregen, Thomas Kaar et al.· 0 citations
OpenTSLM TeeMoE is introduced, a generalist time-series language model that can forecast directly from observed time series, reason over textual context and temporal patterns, and synthesize and refine predictions from external numerical forecasting specialists.
Tony Chen, Timo Stoffregen, Maxwell A. Xu et al.· 0 citations
ConRAG is introduced, which builds a lightweight entity-document graph from entity co-occurrence and LLM-based entity filtering that consistently improves bridge entity and reasoning chain recovery over strong RAG baselines, while reducing graph-indexing token cost by up to roughly 1.5 orders of magnitude.
Kilian Bänziger, Sonia Laguna, Markus Kreft et al.· 0 citations
TimeRLM, an RLM formulation for time-series that sequentially manipulates the signal using code and vision capabilities, and AnomalyXL, a synthetic long-context anomaly localization benchmark with programmatically injected anomalies that require precise retrieval.
Nicolas Zumarraga, Lorenzo Steno, Ning Wang et al.· 0 citations
A benchmark that evaluates diagnostic and resolving non-convergent power flow cases across multiple LLMs and three architectures, providing a reproducible foundation for developing agentic AI systems for power system planning and operation.
Riccardo Mansutti, Andrea Pomarico, Robert Jakob et al.· 0 citations
As LLMs increasingly shape the political information citizens rely on, no standard exists to assess whether they do so responsibly. We introduce Polistemics, a theory-grounded diagnostic benchmark for evaluating LLMs as mediators of political information in elections. Prior work has treated this task as reproduction ra...
B. Peters, Gabor Hollbeck, Robert Jakob et al.· arXiv.org· 0 citations
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