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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

Thinking Inertia: LLMs Keep Thinking When Told Not To

208 upvotes · 8 OCT 2026 · Dianqiao Lei, Kevin Qinghong Lin, Pan Lu et al.

This paper investigates how Large Language Models (LLMs) behave when "told not to think", i.e., when instructed to produce answers without using reasoning. Practitioners might care about understanding how to control this behavior, as it can impact the accuracy of LLMs in certain tasks.

LLMsthinking modescontinual learningevaluation metricsquestion answeringreinforcement learning