Twice Upon a Time: Complementary Representations for Time-Series Reasoning

Paper recorded by Signals 4 on 2026-10-15 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-15 on arXiv · recorded by Signals 4 on 2026-10-16

Category: cs.AI · 人工智能 · first seen 2026-10-16

Abstract

Time Series Language Models (TSLMs) enable language-based understanding and reasoning over temporal data, yet a fundamental question remains: what information should their time-series representations preserve, and what variation should they factor out? Existing TSLMs typically expose the language model to a single representation, imposing a fixed inductive bias across downstream tasks. We instead

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