10 papers
Event Signature Transfer: Model-Agnostic Forecast Scenario Construction from Historical Events
Karthik Sridhar, Aaditya Jain, Murari Mandal +1
Forecasters often know an event is imminent but not the shape, size, or timing of its effect. We introduce Event Signature Transfer (EST), a training-free, model-agnostic operator…
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, Dhruv Kumar +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…
Confidence is Not Competence
Debdeep Sanyal, Manya Pandey, Dhruv Kumar +2
Large language models (LLMs) often exhibit a puzzling disconnect between their asserted confidence and actual problem-solving competence. We offer a mechanistic account of this dec…
Policy Optimization Prefers The Path of Least Resistance
Debdeep Sanyal, Aakash Sen Sharma, Dhruv Kumar +2
Policy optimization (PO) algorithms are used to refine Large Language Models for complex, multi-step reasoning. Current state-of-the-art pipelines enforce a strict think-then-answe…
time2time: Causal Intervention in Hidden States to Simulate Rare Events in Time Series Foundation Models
Debdeep Sanyal, Aaryan Nagpal, Dhruv Kumar +2
While transformer-based foundation models excel at forecasting routine patterns, two questions remain: do they internalize semantic concepts such as market regimes, or merely fit c…