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T-SMART: Mechanism-Level Attribution for Tool-Augmented Time-Series Question Answering

arXiv · AI, language, vision and robotics · article · Sep 12, 2026 · UTC

Large language models (LLMs) can struggle with time-series question answering (TS-QA), especially when numerical signals are serialized as text and require explicit computation. Tool-augmented approaches improve performance, but existing systems often intertwine language reasoning, computation, and perception, making it difficult to determine which components drive the gains. We present T-SMART, a neurosymbolic framework that separates these roles: a frozen LLM interprets questions and selects operations, deterministic tools perform numerical computation, and structured perception is invoked o

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