TS-Debate: Multimodal Collaborative Debate for Zero-Shot Time Series Reasoning

2026-01-27

AI Summary

Large language models (LLMs) in zero-shot time series reasoning are often hindered by numerical distortion, modality interference, and insufficient cross-modal integration. This work proposes TS-Debate, a novel framework that introduces, for the first time, a modality-specific multi-agent debate mechanism: dedicated expert agents are assigned to textual, visual, and numerical signals, respectively, and engage in structured collaborative reasoning under explicit domain knowledge guidance. The framework incorporates a verification–conflict–calibration protocol to enhance output reliability without requiring task-specific fine-tuning. By preserving modality fidelity and mitigating numerical hallucinations, TS-Debate achieves significant performance gains over strong existing baselines—including standard multimodal shared-input approaches—across 20 tasks spanning three public benchmarks.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Multiagent Systems: Agreement, Argumentation & Negotiation

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

Recent progress at the intersection of large language models (LLMs) and time series (TS) analysis has revealed both promise and fragility. While LLMs can reason over temporal structure given carefully engineered context, they often struggle with numeric fidelity, modality interference, and principled cross-modal integration. We present TS-Debate, a modality-specialized, collaborative multi-agent debate framework for zero-shot time series reasoning. TS-Debate assigns dedicated expert agents to textual context, visual patterns, and numerical signals, preceded by explicit domain knowledge elicitation, and coordinates their interaction via a structured debate protocol. Reviewer agents evaluate agent claims using a verification-conflict-calibration mechanism, supported by lightweight code execution and numerical lookup for programmatic verification. This architecture preserves modality fidelity, exposes conflicting evidence, and mitigates numeric hallucinations without task-specific fine-tuning. Across 20 tasks spanning three public benchmarks, TS-Debate achieves consistent and significant performance improvements over strong baselines, including standard multimodal debate in which all agents observe all inputs.

Problem

• time series reasoning
• multimodal integration
• numeric fidelity
• modality interference
• zero-shot learning

Innovation

• multimodal debate
• zero-shot time series reasoning
• modality-specialized agents
• numeric hallucination mitigation
• structured verification protocol