CAN-QA creates 33,128 QA pairs from CAN traffic logs in 10 categories to test LLMs, which capture patterns but struggle with temporal reasoning and multi-condition inference.
Chatts: Aligning time series with llms via synthetic data for enhanced understanding and reasoning
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
STReasoner uses S-GRPO reinforcement learning to let LLMs integrate time series, graphs, and text for spatio-temporal reasoning, delivering 17-135% accuracy gains over baselines on a new four-task benchmark at 0.004X the cost of proprietary models.
LENS creates over 100,000 sensor-text QA pairs from 258 participants' EMA data and trains a patch-level encoder that projects raw multimodal sensor streams into an LLM's space, enabling generation of clinically grounded depression and anxiety narratives that outperform baselines on NLP and symptom-1
TS-Agent is an agentic framework that uses LLMs only for evidence-based reasoning while delegating extraction to raw time series tools, matching or exceeding baselines on four benchmarks with largest gains on reasoning tasks.
Presents TS-Skill benchmark and SKEvol construction framework to diagnose three composable analytical skills in time-series QA across LLMs and TSLMs.
A survey proposing a taxonomy of Injective, Bridging, and Internal Alignment paradigms to evolve TSA into user-driven Time Series Question Answering with LLMs.
citing papers explorer
-
CAN-QA: A Question-Answering Benchmark for Reasoning over In-Vehicle CAN Traffic
CAN-QA creates 33,128 QA pairs from CAN traffic logs in 10 categories to test LLMs, which capture patterns but struggle with temporal reasoning and multi-condition inference.
-
STReasoner: Empowering LLMs for Spatio-Temporal Reasoning in Time Series via Spatial-Aware Reinforcement Learning
STReasoner uses S-GRPO reinforcement learning to let LLMs integrate time series, graphs, and text for spatio-temporal reasoning, delivering 17-135% accuracy gains over baselines on a new four-task benchmark at 0.004X the cost of proprietary models.
-
LENS: LLM-Enabled Narrative Synthesis for Mental Health by Aligning Multimodal Sensing with Language Models
LENS creates over 100,000 sensor-text QA pairs from 258 participants' EMA data and trains a patch-level encoder that projects raw multimodal sensor streams into an LLM's space, enabling generation of clinically grounded depression and anxiety narratives that outperform baselines on NLP and symptom-1
-
TS-Agent: Understanding and Reasoning Over Raw Time Series via Iterative Insight Gathering
TS-Agent is an agentic framework that uses LLMs only for evidence-based reasoning while delegating extraction to raw time series tools, matching or exceeding baselines on four benchmarks with largest gains on reasoning tasks.
-
TS-Skill: A Benchmark for Evaluating Analytical Skills in Time-Series Question Answering
Presents TS-Skill benchmark and SKEvol construction framework to diagnose three composable analytical skills in time-series QA across LLMs and TSLMs.
-
From Time Series Analysis to Question Answering: A Survey in the LLM Era
A survey proposing a taxonomy of Injective, Bridging, and Internal Alignment paradigms to evolve TSA into user-driven Time Series Question Answering with LLMs.