RITS: Reasoning with Identifiable Traces for Time-Series Analysis Agents

In domains such as finance, healthcare, and industrial monitoring, many decisions rely on time-series analysis. Tool-using agents promise to automate such analyses posed as natural-language questions that require a multi-step workflow of analysis tools. However, training and evaluating agents that execute such analyses faithfully is challenging because natural analysis questions often underspecify the intended procedure. Specifically, the same question can be addressed with different tools and parameter bindings, and these choices can yield different answers. There is then no single correct answer to train on or to grade against, and no unique reference workflow exists to check a produced analysis against. We present RITS, a framework that constructs trace-identifiable multi-hop time-series questions and trains and evaluates agents against their reference workflows. We build every question to admit a unique executable workflow within a modeled tool space, and refer to that workflow as a trace. A produced analysis can thus be scored against a single reference. To this end, RITS instantiates templates structured as directed acyclic graphs (DAGs) over 308 parameterized analysis tools grounded in standard libraries. It executes every candidate trace and retains a question only when its constraints determine a single trace. We then post-train a tool-using agent with a trace-aware reward that scores both the procedure and the answer. Experiments show that trace-aware reinforcement learning reaches near-perfect trace fidelity with half the tool calls of answer-only training, while improving answer accuracy. The trained agent generalizes to held-out workflow structures and tools and transfers zero-shot to external benchmarks. Compared with zero-shot frontier agents, it attains comparable answer accuracy with higher trace fidelity and fewer tool calls.