Benchmarking In-context Experiential Learning Through Repeated Product Recommendations
This addresses the need for better benchmarks to measure adaptive learning in AI agents for applications like product recommendations, though it is incremental as it focuses on evaluation rather than new methods.
The paper tackled the problem of evaluating agents' ability to adaptively learn from ambiguous, shifting real-world environments by creating a benchmark for in-context experiential learning in product recommendations, finding that current frontier models struggle to improve across episodes.
To reliably navigate ever-shifting real-world environments, agents must grapple with incomplete knowledge and adapt their behavior through experience. However, current evaluations largely focus on tasks that leave no ambiguity, and do not measure agents' ability to adaptively learn and reason through the experiences they accrued. We exemplify the need for this in-context experiential learning in a product recommendation context, where agents must navigate shifting customer preferences and product landscapes through natural language dialogue. We curate a benchmark for experiential learning and active exploration (BELA) that combines (1) rich real-world products from Amazon, (2) a diverse collection of user personas to represent heterogeneous yet latent preferences, and (3) a LLM user simulator powered by the persona to create rich interactive trajectories. We observe that current frontier models struggle to meaningfully improve across episodes, underscoring the need for agentic systems with strong in-context learning capabilities.