Does Time-Series Question Answering Need a Large Language Model to Reason?

Does time-series question answering need a large language model (LLM) as its reasoning engine? Many existing methods rely on an LLM both to interpret the signal and to reason over it. This is computationally expensive and makes it difficult to determine whether better answers come from better representations or better reasoning. We introduce GLEAN (**G**rounded **L**atent **E**vidence **A**ggregation **N**etwork), which separates pretrained input encoding from a dedicated recurrent reasoning engine. Frozen text and time-series encoders process each input once, supplemented by numerical features that retain details such as signal endpoints and slopes. GLEAN then reasons over these representations through recurrent latent updates. Its current state guides each new read of the inputs, so relevant details can be revisited and combined as the answer develops. A small shared network performs this computation, and structured output heads select answers or order signal fragments without generating text. The separation makes each part measurable: frozen encoders alone already lead every time-series LLM on perception questions, while the recurrent reasoner raises relational accuracy from 58.03% to 69.16% on the same representations. With 0.46B total parameters, of which only 8.2M are trained, GLEAN achieves $77.33\%$ accuracy on TSAQA, outperforming all five evaluated fine-tuned time-series LLMs we evaluated while using $87$–$693\times$ fewer estimated inference FLOPs per question. These results suggest that closed-ended temporal question answering does not require a large language model as its reasoning engine: pretrained representations coupled with a small recurrent network offer a strong and far cheaper alternative.