Auto-Chroma: Automating the Design of Test-Time Time-Series Model Portfolios with Memory-Augmented LLM Agents.

Time series foundation models (TSFMs) deliver accurate zero-shot forecasts across domains, but their accuracy is tied to model sizes that make inference costly at scale. Chroma offers an alternative paradigm: a portfolio of small specialists, each post-trained from a small TSFM on a subset of the post-training data, matches models orders of magnitude larger with only a few specialists active during inference. However, the portfolio is designed heuristically: the data is partitioned once by sampling frequency or domain, although any subset of it could define a specialist. We present **Auto-Chroma**, an agentic framework that casts portfolio design as a search over specialist configurations and runs the search end to end with memory-augmented large language model (LLM) agents. Rather than guessing from a fixed prompt, the agents reason over a memory of every trial, diagnosing each evaluated portfolio and proposing the next as a hypothesis grounded in the full history of the search. On CB-II and fev-bench-mini, with three univariate and multivariate generalists from 4M to 28M parameters, the portfolios found outperform the frequency heuristic portfolio on test in five of the six settings and achieve competitive performance with respect to large TSFM baselines, and under the same budget Auto-Chroma leads greedy and random search throughout, surpassing the final performance of random search within a fraction of the budget while post-training far fewer specialists. Beyond performance, we analyze both the portfolios the search produces and the search process itself: how fragile the heuristic partitions are, whether the portfolios transfer across benchmarks, whether the validation objective faithfully reflects test performance, how similar the portfolios are across repeated runs and orchestrator LLMs, and how the agents use the memory of past rounds. To our knowledge, this is the first work to investigate portfolio construction as an autonomous process, and the first to treat the search and the portfolios it finds as a subject of study in their own right.