Multimodal Collaborative Debate for Zero-Shot Time Series Reasoning

[2601.19151] Multimodal Collaborative Debate for Zero-Shot Time Series Reasoning Skip to main content Search Submit Donate Log in Computer Science > Artificial Intelligence arXiv:2601.19151 (cs) [Submitted on 27 Jan 2026 ( v1 ), last revised 28 Aug 2026 (this version, v2)] Title: Multimodal Collaborative Debate for Zero-Shot Time Series Reasoning Authors: Patara Trirat , Jin Myung Kwak , Jay Heo , Heejun Lee , Sung Ju Hwang View PDF HTML (experimental) Abstract: Large language models (LLMs) are increasingly used as natural-language interfaces to structured data, yet they remain brittle when reasoning over time series. Visual patterns can be misleading, numerical claims can be hallucinated, and textual context can override evidence from the signal. We study zero-shot time-series reasoning as a multimodal evidence arbitration problem for LLM agents. We propose TS-Debate, an inference-time multi-agent protocol that requires no task-specific fine-tuning. TS-Debate first elicits relevant domain knowledge, then assigns modality-specialized agents to textual context, visual patterns, and numerical signals, and coordinates their interaction through a verification-conflict-calibration procedure. Reviewer agents check decision-critical claims with lightweight code execution and numerical lookup, resolve cross-modal disagreement, and calibrate the final answer. Unlike generic multi-agent debate or unconstrained tool use, TS-Debate specifies how evidence is exposed, which claims are checkable, and how verification outcomes shape synthesis. Across 20 tasks from three public benchmarks, TS-Debate improves classification and question answering performance over strong baselines, while revealing that debate is most useful for global-structure and cross-view reasoning rather than local value reconstruction. Comments: EMNLP 2026 (Main), Project Page: this https URL Subjects: Artificial Intelligence (cs.AI) ; Multiagent Systems (cs.MA) Cite as: arXiv:2601.19151 [cs.AI] (or arXiv:2601.19151v2 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2601.19151 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Patara Trirat [ view email ] [v1] Tue, 27 Jan 2026 03:29:22 UTC (1,361 KB) [v2] Fri, 28 Aug 2026 10:17:09 UTC (1,236 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.AI < prev | next > new | recent | 2026-01 Change to browse by: cs cs.MA References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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