Chronicle is the first model jointly pretrained from scratch on text and time series in a unified transformer that matches a comparable language model on NLU tasks and sets new bars for time series classification and multimodal forecasting.
Language models still struggle to zero-shot reason about time series
6 Pith papers cite this work. Polarity classification is still indexing.
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TimeSeriesExamAgent combines templates and LLM agents to generate scalable time series reasoning benchmarks, demonstrating that current LLMs have limited performance on both abstract and domain-specific tasks.
TSVer is a new benchmark dataset for fact verification against time-series evidence, with 304 annotated real-world claims, 400 time series, verdicts, and justifications, plus baseline results showing current models struggle.
TS-Agent is an agentic framework that uses LLMs only for evidence-based reasoning while delegating extraction to raw time series tools, matching or exceeding baselines on four benchmarks with largest gains on reasoning tasks.
A semi-synthetic dataset of 7 million context-augmented time series windows with verifier-filtered contexts enables transfer to real-world context-aided forecasting and suggests data quality—not architecture—was the bottleneck.
FoNE encodes numbers as single tokens via Fourier features and outperforms subword and digit-wise embeddings on addition, subtraction, and multiplication with far less data.
citing papers explorer
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Chronicle: A Multimodal Foundation Model for Joint Language and Time Series Understanding
Chronicle is the first model jointly pretrained from scratch on text and time series in a unified transformer that matches a comparable language model on NLU tasks and sets new bars for time series classification and multimodal forecasting.
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TimeSeriesExamAgent: Creating Time Series Reasoning Benchmarks at Scale
TimeSeriesExamAgent combines templates and LLM agents to generate scalable time series reasoning benchmarks, demonstrating that current LLMs have limited performance on both abstract and domain-specific tasks.
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TSVer: A Benchmark for Fact Verification Against Time-Series Evidence
TSVer is a new benchmark dataset for fact verification against time-series evidence, with 304 annotated real-world claims, 400 time series, verdicts, and justifications, plus baseline results showing current models struggle.
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TS-Agent: Understanding and Reasoning Over Raw Time Series via Iterative Insight Gathering
TS-Agent is an agentic framework that uses LLMs only for evidence-based reasoning while delegating extraction to raw time series tools, matching or exceeding baselines on four benchmarks with largest gains on reasoning tasks.
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Overcoming the Modality Gap in Context-Aided Forecasting
A semi-synthetic dataset of 7 million context-augmented time series windows with verifier-filtered contexts enables transfer to real-world context-aided forecasting and suggests data quality—not architecture—was the bottleneck.
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FoNE: Precise Single-Token Number Embeddings via Fourier Features
FoNE encodes numbers as single tokens via Fourier features and outperforms subword and digit-wise embeddings on addition, subtraction, and multiplication with far less data.