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New framework T-SMART boosts LLM time-series QA with deterministic computation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed T-SMART, a neurosymbolic framework designed to improve time-series question answering (TS-QA) for large language models. This framework separates language interpretation, computation, and perception to better understand component contributions. Experiments demonstrated that deterministic computation significantly boosts accuracy by 31.7 percentage points compared to direct LLM reasoning on serialized time series, highlighting the primary benefit of reliable numerical execution in tool-augmented TS-QA systems. AI

IMPACT Enhances LLM capabilities in numerical reasoning for time-series data, potentially improving applications requiring precise data analysis.

RANK_REASON The cluster contains a research paper detailing a new framework and experimental results for time-series question answering. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework T-SMART boosts LLM time-series QA with deterministic computation

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17 / 100
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The cluster contains a research paper detailing a new framework and experimental results for time-series question answering. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ivan Delgado, Himansi Gupta, Bishal Khatri, Niharika Sapre, Lameta Shamoon, Onat Gungor, Tajana Rosing ·

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

    arXiv:2609.14142v1 Announce Type: new Abstract: 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 exi…

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