9 papers
Semantics or Structure? Auditing Text Sensitivity in Multimodal Time-Series Forecasting
Karthik Sridhar, Atharva Gupta, Nishant Pradhan +3
Multimodal time-series forecasting has emerged as a promising paradigm in which natural-language context is expected to improve predictive performance. Recent multimodal foundation…
Where Computation Lives Inside TabPFN: Causal Localisation of Attention Head Function
Atharva Gupta, Dhruv Kumar, Murari Mandal +1
We present the first causal mechanistic analysis of a tabular foundation model, investigating how TabPFN 2.5's feature wise attention heads distribute computation across layers. Us…
Mix, Don't Pick: Why Synthetic Corpus Composition Matters for Time Series Foundation Model Pretraining
Aaryan Nagpal, Debdeep Sanyal, Murari Mandal +2
Choosing the wrong synthetic generator for time-series foundation model pretraining is costly: under identical training budgets, the best and worst generators produce up to a $2\ti…
GITCO: Gated Inference-Time Context Optimization in TSFMs
Manya Pandey, Dhruv Kumar, Murari Mandal +1
Patch-based Time Series Foundation Models (TSFMs) suffer from context poisoning: structurally anomalous patches capture disproportionate attention and silently degrade zero-shot fo…
REGEN: Reference-Guided Synthetic Multivariate Time Series Generation for Forecasting
Moulik Gupta, Dhruv Kumar, Murari Mandal +1
Training robust multivariate time series forecasting models requires large, diverse corpora, yet many real-world domains provide only a handful of observed sequences. Existing gene…
TSQueryBench: LLM-as-a-Judge for Time Series Explanations
Preetham Sivalingam, Murari Mandal, Saurabh Deshpande +1
Natural language explanations of time series data are increasingly produced by foundation models in high stakes domains, making factual correctness critical. Evaluating such explan…