[Submitted on 11 May 2026 (v1), last revised 3 Nov 2026 (this version, v2)]
Abstract: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. Behavioral reference sets support exact-recovery evaluation and the training of a learned bundle-level reward that supplies feedback for policy optimization with RL. Its controlled tool environment exposes agents to semantic and behavior-aligned tools, enabling systematic analysis of how LLM reasoning, tool signals, and tool-use policies contribute to performance. Across controlled experiments, performance scales with model capacity and test-time compute, and improves with stronger tools, while 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.
Submission history
From: Imad Aouali [view email]
[v1] Mon, 11 May 2026 18:55:32 UTC (303 KB)
[v2] Tue, 3 Nov 2026 12:49:46 UTC (267 KB)
