{"total":103,"rows":[{"id":"2503.01875","title":"Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement","created":"2025-02-26","cited_by_count":82,"area":null},{"id":"2511.11698","title":"Moirai 2.0: When Less Is More for Time Series Forecasting","created":"2025-11-12","cited_by_count":69,"area":null},{"id":"2502.08942","title":"Language in the Flow of Time: Time-Series-Paired Texts Weaved into a Unified Temporal Narrative","created":"2025-02-13","cited_by_count":40,"area":null},{"id":"2503.01013","title":"TimeXL: Explainable Multi-modal Time Series Prediction with LLM-in-the-Loop","created":"2025-03-02","cited_by_count":39,"area":null},{"id":"2509.22295","title":"Aurora: Towards Universal Generative Multimodal Time Series Forecasting","created":"2025-09-26","cited_by_count":29,"area":null},{"id":"2601.13653","title":"TimeART: Towards Agentic Time Series Reasoning via Tool-Augmentation","created":"2026-01-20","cited_by_count":26,"area":"lr"},{"id":"2509.11575","title":"A Survey of Reasoning and Agentic Systems in Time Series with Large Language Models","created":"2025-09-15","cited_by_count":21,"area":"lr"},{"id":"2503.11835","title":"How Can Time Series Analysis Benefit From Multiple Modalities? A Survey and Outlook","created":"2025-03-14","cited_by_count":20,"area":null},{"id":"2506.10412","title":"Time-IMM: A Dataset and Benchmark for Irregular Multimodal Multivariate Time Series","created":"2025-06-12","cited_by_count":19,"area":null},{"id":"2506.13705","title":"TimeMaster: Training Time-Series Multimodal LLMs to Reason via Reinforcement Learning","created":"2025-06-16","cited_by_count":18,"area":"lr"},{"id":"2508.10552","title":"When Language Overrules: Revealing Text Dominance in Multimodal Large Language Models","created":"2025-08-14","cited_by_count":18,"area":"mllm"},{"id":"2601.18744","title":"TSRBench: A Comprehensive Multi-task Multi-modal Time Series Reasoning Benchmark for Generalist Models","created":"2026-01-26","cited_by_count":18,"area":"lr"},{"id":"2509.23988","title":"LLM/Agent-as-Data-Analyst: A Survey","created":"2025-09-28","cited_by_count":16,"area":"agents"},{"id":"2503.11411","title":"Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models","created":"2025-03-14","cited_by_count":15,"area":"synth"},{"id":"2510.03255","title":"SciTS: Scientific Time Series Understanding and Generation with LLMs","created":"2025-09-26","cited_by_count":15,"area":"lr"},{"id":"2509.05215","title":"BEDTime: A Unified Benchmark for Automatically Describing Time Series","created":"2025-09-05","cited_by_count":12,"area":null},{"id":"2510.03519","title":"TS-Reasoner: Aligning Time Series Foundation Models with LLM Reasoning","created":"2025-10-03","cited_by_count":12,"area":"lr"},{"id":"2601.23204","title":"TSAQA: Time Series Analysis Question And Answering Benchmark","created":"2026-01-30","cited_by_count":11,"area":null},{"id":"2503.14190","title":"Inferring Events from Time Series using Language Models","created":"2025-03-18","cited_by_count":10,"area":null},{"id":"2509.21190","title":"Towards Foundation Models for Zero-Shot Time Series Anomaly Detection: Leveraging Synthetic Data and Relative Context Discrepancy","created":"2025-09-25","cited_by_count":10,"area":"synth"},{"id":"2510.07432","title":"TS-Agent: Understanding and Reasoning Over Raw Time Series via Iterative Insight Gathering","created":"2025-10-08","cited_by_count":9,"area":"lr"},{"id":"2510.25817","title":"A Survey on Efficient Large Language Model Training: From Data-centric Perspectives","created":"2025-10-29","cited_by_count":9,"area":null},{"id":"2506.00807","title":"Enhancing LLM Reasoning for Time Series Classification by Tailored Thinking and Fused Decision","created":"2025-06-01","cited_by_count":8,"area":"lr"},{"id":"2501.07335","title":"TempoGPT: Enhancing Time Series Reasoning via Quantizing Embedding","created":"2025-01-13","cited_by_count":7,"area":"lr"},{"id":"2503.10883","title":"Chat-TS: Enhancing Multi-Modal Reasoning Over Time-Series and Natural Language Data","created":"2025-03-13","cited_by_count":7,"area":"lr"},{"id":"2506.09114","title":"TRACE: Grounding Time Series in Context for Multimodal Embedding and Retrieval","created":"2025-06-10","cited_by_count":7,"area":null},{"id":"2601.03248","title":"STReasoner: Empowering LLMs for Spatio-Temporal Reasoning in Time Series via Spatial-Aware Reinforcement Learning","created":"2026-01-06","cited_by_count":7,"area":"lr"},{"id":"2602.17149","title":"TimeOmni-VL: Unified Models for Time Series Understanding and Generation","created":"2026-02-19","cited_by_count":7,"area":null},{"id":"2508.03963","title":"Can Large Language Models Adequately Perform Symbolic Reasoning Over Time Series?","created":"2025-08-05","cited_by_count":6,"area":"lr"},{"id":"2604.21199","title":"ARFBench: Benchmarking Time Series Question Answering Ability for Software Incident Response","created":"2026-04-23","cited_by_count":6,"area":null},{"id":"2510.01116","title":"Eliciting Chain-of-Thought Reasoning for Time Series Analysis using Reinforcement Learning","created":"2025-10-01","cited_by_count":5,"area":"lr"},{"id":"2511.05124","title":"QuAnTS: Question Answering on Time Series","created":"2025-11-07","cited_by_count":5,"area":null},{"id":"2509.24378","title":"AXIS: Explainable Time Series Anomaly Detection with Large Language Models","created":"2025-09-29","cited_by_count":4,"area":null},{"id":"2510.01111","title":"TsLLM: Augmenting LLMs for General Time Series Understanding and Prediction","created":"2025-10-01","cited_by_count":4,"area":null},{"id":"2602.18645","title":"Adaptive Time Series Reasoning via Segment Selection","created":"2026-02-20","cited_by_count":4,"area":"lr"},{"id":"2603.01042","title":"Thoth: Mid-Training Bridges LLMs to Time Series Understanding","created":"2026-03-01","cited_by_count":4,"area":null},{"id":"2603.11950","title":"Learning Transferable Sensor Models via Language-Informed Pretraining","created":"2026-03-12","cited_by_count":4,"area":null},{"id":"2607.09880","title":"CLIR-Bench: Benchmarking Multimodal Question Answering over Irregular Clinical Time Series","created":"2026-07-10","cited_by_count":4,"area":null},{"id":"2506.11040","title":"Large Language models for Time Series Analysis: Techniques, Applications, and Challenges","created":"2025-05-21","cited_by_count":3,"area":null},{"id":"2506.20073","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","created":"2025-06-25","cited_by_count":3,"area":"lr"},{"id":"2510.07858","title":"Augur: Modeling Covariate Causal Associations in Time Series via Large Language Models","created":"2025-10-09","cited_by_count":3,"area":null},{"id":"2511.06344","title":"TimeSense:Making Large Language Models Proficient in Time-Series Analysis","created":"2025-11-09","cited_by_count":3,"area":"lr"},{"id":"2602.07830","title":"Time Series Reasoning via Process-Verifiable Thinking Data Synthesis and Scheduling for Tailored LLM Reasoning","created":"2026-02-08","cited_by_count":3,"area":"lr"},{"id":"2603.00312","title":"How Well Do Multimodal Models Reason on ECG Signals?","created":"2026-02-27","cited_by_count":3,"area":"lr"},{"id":"2605.00015","title":"TimeRFT: Stimulating Generalizable Time Series Forecasting for TSFMs via Reinforcement Finetuning","created":"2026-04-18","cited_by_count":3,"area":null},{"id":"2503.21833","title":"Refining Time Series Anomaly Detectors using Large Language Models","created":"2025-03-26","cited_by_count":2,"area":null},{"id":"2510.16980","title":"Towards Interpretable and Trustworthy Time Series Reasoning: A BlueSky Vision","created":"2025-10-19","cited_by_count":2,"area":null},{"id":"2602.14200","title":"TS-Haystack: A Multi-Task Retrieval Benchmark for Long-Context Time-Series Reasoning","created":"2026-02-15","cited_by_count":2,"area":"lr"},{"id":"2602.19455","title":"SenTSR-Bench: Thinking with Injected Knowledge for Time-Series Reasoning","created":"2026-02-23","cited_by_count":2,"area":"lr"},{"id":"2605.07675","title":"FactoryBench: Evaluating Industrial Machine Understanding","created":"2026-05-08","cited_by_count":2,"area":null},{"id":"2605.29401","title":"Rethinking Post-Training Recipes for Multimodal Time-Series Forecasting","created":"2026-05-28","cited_by_count":2,"area":"lr"},{"id":"2607.08940","title":"TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning","created":"2026-07-09","cited_by_count":2,"area":"lr"},{"id":"2609.24559","title":"$t_0$: A Time-Series Foundation Model for Forecasting with Context","created":"2026-09-21","cited_by_count":2,"area":null},{"id":"2512.22627","title":"Chain-of-thought Reviewing and Correction for Time Series Question Answering","created":"2025-12-27","cited_by_count":1,"area":"lr"},{"id":"2601.04709","title":"Bridging Temporal and Textual Modalities: A Multimodal Framework for Automated Cloud Failure Root Cause Analysis","created":"2026-01-08","cited_by_count":1,"area":null},{"id":"2602.19919","title":"Janus-Q: End-to-End Event-Driven Trading via Hierarchical-Gated Reward Modeling","created":"2026-02-23","cited_by_count":1,"area":"alignment"},{"id":"2604.13924","title":"ASTER: Latent Pseudo-Anomaly Generation for Unsupervised Time-Series Anomaly Detection","created":"2026-04-15","cited_by_count":1,"area":null},{"id":"2604.24935","title":"CAN-QA: A Question-Answering Benchmark for Reasoning over In-Vehicle CAN Traffic","created":"2026-04-27","cited_by_count":1,"area":"lr"},{"id":"2605.24703","title":"TS-Skill: A Benchmark for Evaluating Analytical Skills in Time-Series Question Answering","created":"2026-05-23","cited_by_count":1,"area":null},{"id":"2608.05238","title":"Decoupling Perception from Description: Computation-Grounded Representation Alignment between Multivariate Time Series and Language","created":"2026-08-05","cited_by_count":1,"area":null},{"id":"2608.23058","title":"LLM-based Agents for Forecasting and Prediction: Methods, Training, Evaluation, and Applications","created":"2026-08-24","cited_by_count":1,"area":"agents"},{"id":"2608.27182","title":"TraceBench: Controlled Evaluation of LLM Agents for Time-Series Root-Cause Attribution","created":"2026-08-27","cited_by_count":1,"area":"agents"},{"id":"2506.11512","title":"From Time Series Analysis to Question Answering: A Survey in the LLM Era","created":"2025-06-13","cited_by_count":0,"area":null},{"id":"2508.20578","title":"Human-AI Collaborative Bot Detection in MMORPGs","created":"2025-08-28","cited_by_count":0,"area":null},{"id":"2509.20007","title":"DiffNator: Generating Structured Explanations of Time-Series Differences","created":"2025-09-24","cited_by_count":0,"area":null},{"id":"2509.25696","title":"Can VLM Pseudo-Labels Train a Time-Series QA Model That Outperforms the VLM?","created":"2025-09-30","cited_by_count":0,"area":null},{"id":"2510.04710","title":"ViTs: Teaching Machines to See Time Series Anomalies Like Human Experts","created":"2025-10-06","cited_by_count":0,"area":null},{"id":"2511.07126","title":"Saliency Map-Guided Knowledge Discovery for Subclass Identification with LLM-Based Symbolic Approximations","created":"2025-11-10","cited_by_count":0,"area":null},{"id":"2512.16453","title":"TimeSeries2Report prompting enables adaptive large language model management of lithium-ion batteries","created":"2025-12-18","cited_by_count":0,"area":null},{"id":"2601.02968","title":"Rationale-Grounded In-Context Learning for Time Series Reasoning with Multimodal Large Language Models","created":"2026-01-06","cited_by_count":0,"area":"lr"},{"id":"2601.10148","title":"DecisionLLM: Large Language Models for Long Sequence Decision Exploration","created":"2026-01-15","cited_by_count":0,"area":"offlinerl"},{"id":"2601.13546","title":"ChatAD: Reasoning-Enhanced Time-Series Anomaly Detection with Multi-Turn Instruction Evolution","created":"2026-01-20","cited_by_count":0,"area":"lr"},{"id":"2602.03564","title":"CoGenCast: A Coupled Autoregressive-Flow Generative Framework for Time Series Forecasting","created":"2026-02-03","cited_by_count":0,"area":"diffusion"},{"id":"2602.08868","title":"AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection","created":"2026-02-09","cited_by_count":0,"area":"lr"},{"id":"2602.17001","title":"Sonar-TS: Search-Then-Verify Natural Language Querying for Time Series Databases","created":"2026-02-19","cited_by_count":0,"area":null},{"id":"2602.20494","title":"KairosVL: Orchestrating Time Series and Semantics for Unified Reasoning","created":"2026-02-24","cited_by_count":0,"area":null},{"id":"2603.11479","title":"Grammar of the Wave: Towards Explainable Multivariate Time Series Event Detection via Neuro-Symbolic VLM Agents","created":"2026-03-12","cited_by_count":0,"area":"agents"},{"id":"2605.28866","title":"Continuity and Ordinality Matter: Constraining Time Series Tokens for Effective Time Series Analysis with Large Language Models","created":"2026-05-22","cited_by_count":0,"area":null},{"id":"2606.04019","title":"Gravity-Aware Hierarchical Routing for Lightweight SensorLLM on Human Activity Recognition","created":"2026-06-01","cited_by_count":0,"area":null},{"id":"2606.18986","title":"Beyond Tokenization: Direct Timestep Embedding and Contrastive Alignment for Time-Series Question Answering","created":"2026-06-17","cited_by_count":0,"area":null},{"id":"2608.03391","title":"TimeRLM: Recursive Language Models Enable Precise Anomaly Localization in Long-Context Time-Series","created":"2026-08-04","cited_by_count":0,"area":null},{"id":"2608.13741","title":"GALA: Generation-Aware Cross-Modal Alignment for Text-to-Time-Series Synthesis","created":"2026-08-13","cited_by_count":0,"area":null},{"id":"2608.14198","title":"MINT: A Universal Zero-Shot Predictor for Transaction Data","created":"2026-08-14","cited_by_count":0,"area":null},{"id":"2608.14270","title":"TimeSage-EV: A Live Benchmark for Agentic Time Series Analysis in Evolving Environments","created":"2026-08-14","cited_by_count":0,"area":"agents"},{"id":"2608.24910","title":"From Plots to Words: Model-Aware Multimodal Explanations as a Foundation for Accessible, Non-Visual Interaction","created":"2026-07-22","cited_by_count":0,"area":"mllm"},{"id":"2609.13457","title":"TimeThink: Eliciting Compositional Reasoning in Timeseries Large Language Models","created":"2026-09-11","cited_by_count":0,"area":"lr"},{"id":"2609.15393","title":"CodeTS: Verifiable Text-to-Time Series Generation via Executable Code","created":"2026-09-14","cited_by_count":0,"area":null},{"id":"2609.18812","title":"WaveTLM: Reliable Time-Series Language Modeling through Task Compilation","created":"2026-09-16","cited_by_count":0,"area":null},{"id":"2609.20156","title":"QUALS: Corpus Equilibrium for Universal Forecasting via Pattern Quantization and Learnability Synchronization","created":"2026-09-17","cited_by_count":0,"area":null},{"id":"2609.20836","title":"PhysioBench: A Unified Benchmark for Physiological Signal Question Answering","created":"2026-07-29","cited_by_count":0,"area":null},{"id":"2609.26389","title":"TimeInteract: Towards Real-Time Interactive Intelligence for Streaming Time Series","created":"2026-09-22","cited_by_count":0,"area":null},{"id":"2609.27277","title":"TimeEvo: Failure-Driven Self-Evolution of a Time Series Agent","created":"2026-09-23","cited_by_count":0,"area":null},{"id":"2609.29792","title":"TimeBraid: Unifying Time Series and Language for Understanding and Forecasting","created":"2026-09-24","cited_by_count":0,"area":null},{"id":"2609.32123","title":"READ-Bench: Benchmarking Historical Instance Retrieval for Time-Series Diagnosis","created":"2026-09-26","cited_by_count":0,"area":null},{"id":"2609.40265","title":"OpenTSLM TeeMoE: A Unified Time-Series Language Model for Forecasting, Contextual Prediction, and Reasoning","created":"2026-09-30","cited_by_count":0,"area":"lr"},{"id":"2610.01168","title":"Detect, Explain, Interpret: An End-to-End Benchmark for Time Series Anomaly Detection, Explainability and Interpretability","created":"2026-10-01","cited_by_count":0,"area":null},{"id":"2610.01800","title":"LineupRL: Verifiable Reinforcement Learning for Time Series Captioning via Caption-to-Series Identification","created":"2026-10-01","cited_by_count":0,"area":"lr"},{"id":"2610.04942","title":"Zero-Shot Time-Series Question Answering via Decoupled Perception and Reasoning","created":"2026-10-04","cited_by_count":0,"area":null},{"id":"2610.05686","title":"Do Time-Series QA Systems Read the Time Series? Evidence Use and Reasoning Reliability","created":"2026-10-05","cited_by_count":0,"area":null},{"id":"2610.17794","title":"GIFT-Eval-CTX: A Benchmark For Contextual Forecasting Model Evaluation","created":"2026-10-13","cited_by_count":0,"area":"agents"}],"next_cursor":"100"}