# MMShopBench: A Real-Log Benchmark for Multimodal, Multi-Turn Shopping Agents

arXiv: https://arxiv.org/abs/2607.29002 · HTML: https://mlsanity.com/paper/2607.29002/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 21 references that are in the map.
- First posted on arXiv 31 Jul 2026.

## Builds on (most cited first, 12 of 12)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents](https://mlsanity.com/paper/2207.01206/) (2022)
- [The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence](https://mlsanity.com/paper/2605.26494/) (2026)
- [ShoppingBench: A Real-World Intent-Grounded Shopping Benchmark for LLM-based Agents](https://mlsanity.com/paper/2508.04266/) (2025)
- [MC-Search: Evaluating and Enhancing Multimodal Agentic Search with Structured Long Reasoning Chains](https://mlsanity.com/paper/2603.00873/) (2026)
- [Visual-Seeker: Towards Visual-Native Multimodal Agentic Search via Active Visual Reasoning](https://mlsanity.com/paper/2606.15231/) (2026)
- [Generalized Contrastive Learning for Multi-Modal Retrieval and Ranking](https://mlsanity.com/paper/2404.08535/) (2024)
- [Shopping Companion: Benchmarking and Training LLM Agents for Long-Horizon Preference-Grounded E-Commerce Tasks](https://mlsanity.com/paper/2603.14864/) (2026)
- [InterLV-Search: Benchmarking Interleaved Multimodal Agentic Search](https://mlsanity.com/paper/2605.07510/) (2026)
- [EComAgentBench: Benchmarking Shopping Agents on Long-Horizon Tasks with Distributed Hidden Intent](https://mlsanity.com/paper/2606.17698/) (2026)
- [MERRIN: A Benchmark for Multimodal Evidence Retrieval and Reasoning in Noisy Web Environments](https://mlsanity.com/paper/2604.13418/) (2026)

## Abstract (arXiv)

Online shoppers increasingly turn to AI shopping assistants, using images and multi-turn dialogue to express and refine product needs that are difficult to articulate in text alone. However, existing benchmarks largely rely on text-only or synthetic requests, underrepresenting complex real-world shopping requirements jointly expressed through images and language. We introduce MMShopBench, the first real-log benchmark for multimodal, multi-turn shopping agents. Built from carefully cleaned and manually annotated shopping logs, MMShopBench provides ground-truth annotations of each request's purchase intent and mandatory product requirements. Agents must infer these requirements jointly from user images and multi-turn dialogue, retrieve candidate products through image and text search, and verify that each candidate satisfies all requirements using its product images and structured attributes. We evaluate representative open-source and proprietary models using an evidence-grounded multimodal protocol and construct a companion training set for fine-tuning an open-source model. To ensure reproducible experimentation, we build an offline shopping sandbox, where fine-tuning substantially narrows the performance gap between our open-source model and leading proprietary models, demonstrating the effectiveness of our training data.