Time-series language models provide a shared natural-language interface across temporal tasks, but plausible text does not guarantee reliable task outputs.
Responses may appear reasonable while hallucinating the required object: numerical sequences can violate shape, scale, channel order, or temporal alignment, and textual decisions can fall outside the legal label space.
We formulate reliable time-series language modeling, separating task-object reliability from predictive quality.
We introduce ExecTS-QA, a contract-grounded benchmark spanning forecasting, imputation, classification, anomaly detection, and waveform analysis.
We further propose WaveTLM, a unified compiler-executor model whose task compiler transforms user requests, visible arguments, and wave-grounded evidence into typed task states, while task-native executors construct numerical tensors, legal decisions, or structured records.
On ExecTS-QA, a single WaveTLM checkpoint achieves 99.40% contract-valid coverage, compared with 37.83% for the strongest evaluated string-first baseline, while retaining balanced predictive performance across all five task families.
Evaluations on SciTS, TSQA, IRTS-ToolBench, and ARFBench provide additional evidence of transfer.
The code, construction scripts, and ExecTS-QA dataset will be publicly released upon publication.
These results show that task compilation can convert plausible language generation into reliable time-series outputs.