MerchantBench: Benchmarking LLM Agents for Long-Term Coherence in E-Commerce Operations
Quick Answer
MerchantBench introduces a 365-day simulation for evaluating LLM agents in e-commerce, revealing that even the best LLMs achieve only 27.3% of the net assets of human participants.
Quick Take
This benchmark highlights the critical need for long-term coherence in AI decision-making within complex environments.
Key Points
- MerchantBench simulates 365 days of e-commerce operations using 98,843 product records.
- Eight were evaluated across two frameworks, revealing significant performance gaps.
- The best LLM configuration achieved only 27.3% of human participants' final net assets.
- The benchmark emphasizes the importance of long-term coherence in AI decision-making.
- Agents must adapt to delayed feedback and interdependent decisions throughout the simulation.
DeepSignal Analysis
What happened
MerchantBench is a new benchmarking tool designed to evaluate large language model (LLM) agents in e-commerce over a simulated 365-day period. The study found that the best LLMs achieved only 27.3% of the net assets accumulated by human participants, indicating a significant performance gap.
Key evidence
- MerchantBench simulates a 365-day e-commerce environment using 98,843 real product records and 26 tools for agent interaction.
- The evaluation involved eight LLMs tested under two frameworks across 48 runs, each lasting 365 simulated days.
- The best-performing LLM configuration reached only 27.3% of the mean final net assets of human participants.
Why it matters
This research underscores the limitations of current LLMs in maintaining long-term coherence in decision-making, particularly in complex environments like e-commerce. The findings suggest that while LLMs can perform specific tasks, they struggle with sustained, coherent strategies over time, which is crucial for real-world applications.
What to watch
Paper Resources
Source Excerpt
agents are increasingly evaluated as autonomous tool users, yet most benchmarks focus on bounded tasks with immediate success criteria. Real-world deployments often require Long-Term Coherence, the capacity to preserve purposeful behavior across extended horizons while adapting decisions to accumulated evidence. Evaluating this capacity requires a persistent environment in which actions constrain future choices, feedback arrives at heterogeneous delays, and incoherent behavi
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