RecoAtlas: A Benchmark and RL Environment for LLM Recommendation Agents Optimizing Set-Level Utility

LLM recommender agents increasingly produce structured reports: sets of items accompanied by natural-language justifications. Yet existing benchmarks often focus on reranking small candidate sets or judging reports primarily by semantic plausibility, providing limited support for evaluating and training agents based on set-level utility. We introduce Recommender Atlas (Agentic Tasks for Learning and Assessment in Shopping), or RecoAtlas, a benchmark and reinforcement learning environment grounded in user behavior. RecoAtlas provides a shopping toolkit for product retrieval, complementarity, and diversity. Given a shopping query, an agent uses these tools to generate a structured recommendation report and receives behavior-grounded feedback, forming an end-to-end interaction that supports both evaluation and reinforcement learning. Its controlled tool environment exposes agents to semantic, behavior-aligned, or faulty tools, enabling systematic analysis of how LLM reasoning, tool signals, and tool-use policies contribute to performance. Across controlled experiments, RecoAtlas exhibits key properties of a meaningful benchmark for agentic systems: performance scales with model capacity and test-time compute, and improves with stronger tools. These experiments also reveal that semantic plausibility does not necessarily translate into behavior-grounded utility. RecoAtlas provides a foundation for training and evaluating shopping assistants to move beyond semantic plausibility toward coherent, behaviorally grounded recommendation sets.