MerchantBench: Benchmarking LLM Agents for Long-Term Coherence in E-Commerce Operations
AI Summary
MerchantBench introduces a persistent benchmark for evaluating long-term coherence in LLM agents performing seller-side e-commerce operations. Its 365-day, order-level simulation is grounded in 98,843 real e-commerce product records and exposes 26 interaction tools. Agents must make interdependent decisions across product sourcing, listing and pricing control, cash-flow management, and adaptation to feedback arriving at different delays. The study evaluates eight LLMs under two agent frameworks across 48 runs. Unlike bounded task benchmarks, the environment links present actions to future choices and makes incoherence observable through cumulative operational effects.
Why it's worth reading
It shifts agent evaluation from isolated tasks to 365-day, interdependent decisions with delayed feedback, making it useful for testing whether agents preserve objectives over time rather than merely completing local actions.
Deep Read
1. What happened
Original facts: The paper introduces MerchantBench, a benchmark for evaluating long-term coherence in LLM agents operating on the seller side of e-commerce. It uses a 365-day, order-level simulation covering product sourcing, listing and pricing control, cash-flow management, and adaptation to mixed-latency feedback.
2. Core technology
Original facts: The environment is grounded in 98,843 real e-commerce product records and exposes 26 tools. It combines promptly observable upstream supplier events with delayed downstream order outcomes. Agents must follow individual order lifecycles and revisit earlier decisions. Analysis: This design places memory, state tracking, credit assignment, and multi-period planning inside one evaluation loop.
3. Key evidence and numbers
Original facts: The study evaluates eight LLMs under two agent frameworks across 48 runs. The supplied abstract does not report model-level scores, costs, failure modes, or statistical significance, so it does not support a detailed ranking of models.
4. Why it matters
Analysis: Many agent benchmarks emphasize immediate success or bounded tool-use tasks, while operational outcomes such as profit, inventory, and cash flow unfold on different timescales. MerchantBench attempts to measure the accumulated consequences of local decisions. Unverified inference: If its metrics prove stable, the benchmark could support shared research on long-horizon planning, persistent memory, and strategy degradation in operational agents.
5. Practical impact
Analysis: Researchers can use it to compare models, prompts, and agent frameworks on state maintenance and delayed-feedback adaptation. Engineering teams may also use the setting to probe whether automated sourcing, pricing, or cash-management agents improve short-term objectives at the expense of later decisions.
6. Limitations and uncertainty
Original facts: The supplied abstract does not specify the simulator rules, evaluation metrics, time range of the product records, data-cleaning process, tool permission boundaries, or full experimental results. Real product records also do not fully represent real merchant operations. Analysis: Forty-eight runs may be limited for comparisons spanning multiple models and frameworks; replication settings and random-seed controls will materially affect confidence in the findings.
7. Original sources
- arXiv abstract page
- Source: hf-papers
- Published: 2026-07-30T20:00:00.000Z