TimeSage-MT: Challenging AI's Grip on Multi-Turn Time Series Analysis

Time series data is at the heart of decisions in sectors ranging from finance to healthcare. Yet, despite the prowess of large language models (LLMs) in handling single-step tasks like forecasting, their ability to navigate multi-turn conversations remains questionable. This is where TimeSage-MT enters the fray.

A New Benchmark Emerges

TimeSage-MT introduces a multi-turn benchmark designed specifically for real-world applications. Covering 240 tasks and 2,680 dialogue turns, it spans eight domains, pushing AI agents to adapt as user goals evolve. This isn't a partnership announcement. It's a convergence of time series data with the nuanced demands of human interaction.

Constructed through a reproducible pipeline, TimeSage-MT transforms real-world data into dialogues with verifiable answers. It offers a unified evaluation protocol and a public leaderboard that lets developers compare different time series agentic systems.

The Performance Gap

Evaluations reveal a stark truth: leading LLMs falter on decision-oriented tasks. These failures expose critical weaknesses in memory retention, handling uncertainty, and domain-based decision making. If agents have wallets, who holds the keys to their reasoning?

TimeSage-MT not only highlights these deficiencies but also offers a structured agent, TimeSage, equipped with a comprehensive skill library. However, even with this structured approach, the fundamental gaps in agentic reasoning remain visible.

Why It Matters

The AI-AI Venn diagram is getting thicker, yet without strong multi-turn reasoning capabilities, AI's potential remains untapped. As industries increasingly rely on AI for complex decision-making, the need for refined agentic reasoning becomes clear. Are we ready to entrust critical real-world decisions to systems that stumble over multi-step conversations?

This isn't just about improving AI performance. It's about building the financial plumbing for machines that can autonomously support human decision-making processes. The implications extend to every domain that requires nuanced interpretation of time series data, underscoring the urgency to bridge these gaps.