New Multi-Agent Framework Boosts Zero-Shot Time Series Reasoning

Global: New Multi-Agent Framework Boosts Zero-Shot Time Series Reasoning

Researchers have introduced TS-Debate, a modality‑specialized, collaborative multi‑agent debate system designed to improve zero‑shot reasoning on time‑series data. The framework was evaluated on 20 tasks across three public benchmarks, demonstrating notable gains over existing multimodal approaches without task‑specific fine‑tuning.

Framework Overview

TS-Debate structures the reasoning process by first eliciting explicit domain knowledge, then assigning dedicated expert agents to handle textual context, visual patterns, and numerical signals. This separation aims to preserve the fidelity of each modality while enabling coordinated analysis.

Specialized Expert Agents

Each expert focuses on a single data type: a language agent interprets narrative descriptions, a visual agent examines plotted patterns, and a numeric agent processes raw series values. By limiting exposure to a single modality, the agents reduce cross‑modal interference that often leads to hallucinated numbers.

Debate Protocol and Verification

The agents engage in a structured debate, presenting claims that are subsequently assessed by reviewer agents. Verification relies on lightweight code execution and numerical lookup, allowing claims to be programmatically confirmed or contested, which the reviewers calibrate through a conflict‑resolution mechanism.

Performance Evaluation

Across the three benchmarks, TS-Debate consistently outperformed strong baselines, including standard multimodal debate systems where all agents accessed all inputs. The improvements were most pronounced on tasks requiring precise numeric computation and pattern recognition.

Implications and Future Work

The results suggest that modality‑specific specialization combined with a rigorous debate and verification loop can mitigate numeric hallucinations in large language models. Future research may explore scaling the framework to additional domains and integrating more sophisticated verification tools.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.