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This paper creates a benchmark (HarnessDev) to test whether large language models (LLMs) can design and improve their own execution infrastructure, called the agent harness, which affects their performance. Practitioners might care because it explores how models can adapt to changing environments and potentially improve efficiency.
This paper introduces ASPIRE, a benchmark for self-evolving models that can learn from vague goals, and explores how agents can interpret and operationalize these goals to improve their performance. Practitioners may care about how models can learn from unclear objectives in real-world applications.
This paper proposes a new retrieval framework called CoGR, which uses two generative models to co-evolve and optimize retrieval representations on both the query and item sides, leading to improved search and advertising performance. Practitioners might care because CoGR can potentially lead to better retrieval results and more efficient search systems.
This paper develops a framework to evaluate the social reasoning of large language models (LLMs) in a more realistic setting, by simulating interactions between the LLM and users who provide feedback on the LLM's predictions. Practitioners might care about this research because it helps improve the social reasoning of LLMs, which are increasingly used for advice and decision-making.