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Matching papers

HarnessDev: Can LLMs Create and Evolve Their Own Agent Harness?

236 upvotes · 1 SEP 2026 · Yuhao Wu, Jingyuan Zhang, Jiajun Shi et al.

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.

continual learningagent developmentLLMsmodel-external executionbenchmarking

It Takes Two to Match: Co-Evolving Generative Retriever with Reinforcement Learning

62 upvotes · 1 SEP 2026 · Runpeng Dai, Kaili Huang, Changsung Kang et al.

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.

generative retrieverreinforcement learningcontinual learninginverted indexkeyword-based retrievalLLMs